Multi-sensor processing for surgical device enhancement

Abstract

The computing device may include an input, a processor, and an output. The device may be configured to receive two points of surgical sensor data from different sensors. The sensors may include wearable patient sensors and / or a surgical room environment sensor system. The processor may determine a surgical device setting (e.g., a closing load for a powered surgical stapler, or, for example, a power level for a surgical energy device). The output may transmit a signal indicative of the determined setting. The surgical device may receive the signal and perform a surgical action based on the determined setting. Determining more optimal device settings using a combination of patient-specific and / or surgical environment-specific sensor inputs may result in better device performance and ultimately better patient outcomes.

Description

Technical Field

[0001] (Cross - reference to related applications) This application is related to the following, the contents of each of which are incorporated herein by reference. · U.S. Patent Application No. 17 / 156287, titled METHOD OF ADJUSTING A SURGICAL PARAMETER BASED ON BIOMARKER MEASUREMENTS, filed together with this specification and having Attorney Docket No. END9290USNP1.

Background Art

[0002] Surgical systems can include the integration of various electrical devices and electromechanical devices. In many cases, these devices include computing capabilities that can enhance their performance. For example, a surgical system can include sensing devices, display devices, imaging devices, smart surgical instruments, and the like. Such devices can include their respective computing capabilities to enhance their performance.

[0003] The computing aspects of surgical devices can be configurable. For example, mechanisms such as updatable firmware, editable setting profiles, swappable memory units, and configurable user interfaces can be used to change the manner in which the computing capabilities operate. The content and nature of these configuration changes, and thus the changes in the operation of each device, can affect patient outcomes.

Summary of the Invention

Means for Solving the Problems

[0004] The computing device comprises a processor. The processor is configured to receive first sensor data from a first sensor system. The processor is configured to receive second surgical sensor data from a second sensor system different from the first sensor system. The first sensor system is either a first patient sensor system or a first environmental sensor system. The second sensor system is either a second patient sensor system or a second environmental sensor system. The processor is configured to determine surgical device settings based on the first and second sensor data. The processor is configured to optionally transmit a signal indicating the determined surgical device settings to the surgical device. Advantageously, determining surgical device settings based on a combination of sensor data and transmitting a signal indicating this allows the surgical device configuration to be optimized according to the perceived circumstances and / or prompts healthcare providers to take appropriate action. This improves patient outcomes. Advantageously, considering data from multiple sources allows the content of the signal to be constructed with higher confidence than when a single source is considered. Optionally, the processor may be further configured to identify complications and / or physiological comorbidities based on first and second sensor data. The determination of surgical device settings is additionally based on the identified complications and / or physiological comorbidities. Advantageously, the resulting signals allow for optimization of the surgical device configuration based on how the identified complications and / or physiological comorbidities affect device use. Additionally or alternatively, medical professionals may be notified of complications and / or physiological comorbidities.

[0005] The signal may represent information that, when received by a surgical device, enables the surgical device to perform actions according to the determined surgical device settings. The surgical device may be any of the following: a motorized stapler, a motorized stapler generator, an energy device, an energy device generator, an intraoperative imaging system, a fume exhauster, a suction irrigation device, or a blowing system. The setting information may indicate any of the following: power level, forward speed, closing speed, closing load, or standby time.

[0006] The first sensor system may include a first patient sensor system. The second sensor system may include a second patient sensor system.

[0007] The first sensor system may include a first patient sensor system. The second sensor system may include a second environmental sensor system.

[0008] The first sensor system may include a first environmental sensor system. The second sensor system may include a second environmental sensor system.

[0009] The processor may be further configured to receive procedure information. The processor may be configured to determine surgical device settings based on first sensor data, second sensor data, and procedure information. Advantageously, the resulting signals allow the surgical device configuration to be optimized based on procedure information, e.g., the planned procedure, and / or prompt the healthcare provider to take appropriate action regarding the planned procedure. Optionally, the processor may be further configured to identify complications and / or physiological comorbidities based on the first and second sensor data. The determination of surgical device settings is additionally based on procedure information and the identified complications and / or physiological comorbidities. Advantageously, the resulting signals allow the surgical device configuration to be optimized based on how the identified complications and / or physiological comorbidities affect device use in relation to the planned surgical procedure. The signals may include information indicating alerts. An alert may represent an identified patient complication associated with the surgical device being used for a surgical task represented by the procedural information, without the surgical device switching to a determined surgical device setting. Benefitingly, the alert prompts the healthcare provider to take appropriate action on the planned procedure. Optionally, the processor is configured to determine the alert based on the sensed data and procedural information.

[0010] A computer implementation method includes receiving first sensor data from a first sensor system and receiving second sensor data from a second sensor system different from the first sensor system. The first sensor system is either a first patient sensor system or a first environmental sensor system. The second sensor system is either a second patient sensor system or a second environmental sensor system. The method includes determining a surgical device configuration based on the first and second sensor data. The method includes optionally transmitting a signal indicating the determined surgical device configuration to the surgical device. Advantageously, determining a surgical device configuration based on a combination of sensor data and transmitting a signal indicating it allows the configuration of the surgical device to be optimized according to the sensed circumstances and / or prompts healthcare providers to take appropriate action. This improves patient outcomes. Advantageously, considering data from multiple sources allows the content of the signal to be constructed with higher confidence than when a single source is considered. Optionally, the method may include identifying complications and / or physiological comorbidities based on first and second sensor data. The determination of surgical device settings is additionally based on the identified complications and / or physiological comorbidities. Advantageously, the resulting signals allow for optimization of the surgical device configuration based on how the identified complications and / or physiological comorbidities affect device use. Additionally or alternatively, medical professionals may be notified of complications and / or physiological comorbidities.

[0011] The method may further include receiving procedural information and determining surgical device settings based on first sensor data, second sensor data, and procedural information. Advantageously, the resulting signals allow the configuration of the surgical device to be optimized based on procedural information, e.g., a planned procedure, and / or prompt the healthcare provider to take appropriate action in accordance with the planned procedure.

[0012] The method may further include identifying complications and / or physiological comorbidities based on first and second sensor data. The determination of surgical device settings may additionally be based on procedural information and the identified complications and / or physiological comorbidities. Advantageously, the resulting signals allow for the optimization of the surgical device configuration based on how the identified complications and / or physiological comorbidities affect device use in relation to the planned surgical procedure.

[0013] The signal may contain information indicating an alert. The alert may represent an identified patient complication associated with the surgical device being used for a surgical task represented by the procedure information, without the surgical device switching to a determined surgical device setting. Benefitingly, the alert prompts the healthcare provider to take appropriate action on the planned procedure. Optionally, the processor is configured to determine an alert based on the sensed data and procedure information.

[0014] The signal may represent information that, when received by a surgical device, enables the surgical device to perform actions according to the determined surgical device settings. The surgical device may be any of the following: a motorized stapler, a motorized stapler generator, an energy device, an energy device generator, an intraoperative imaging system, a fume exhauster, a suction irrigation device, or a blowing system. The setting information may indicate any of the following: power level, forward speed, closing speed, closing load, or standby time.

[0015] The first sensor system may include a first patient sensor system. The second sensor system may include a second patient sensor system.

[0016] The first sensor system may include a first patient sensor system. The second sensor system may include a second environmental sensor system.

[0017] The first sensor system may include a first environmental sensor system. The second sensor system may include a second environmental sensor system.

[0018] The surgical device comprises a processor. The processor is configured to receive first sensor data from a first sensor system. The processor is configured to receive second surgical sensor data from a second surgical sensor system different from the first surgical sensor system. The first surgical sensor system is either a first patient sensor system or a first environmental sensor system. The second sensor system is either a second patient sensor system or a second environmental sensor system. The processor is configured to determine surgical device settings based on the first and second sensor data. The surgical device further comprises a driver that performs surgical actions based on the determined surgical device settings. Advantageously, determining surgical device settings based on a combination of sensor data and performing surgical actions based on these settings allows the configuration of the surgical device to be optimized according to the sensed situation and / or prompts healthcare providers to take appropriate action. This improves patient outcomes. Advantageously, considering data from multiple sources allows the content of the signals to be constructed with higher confidence than when a single source is considered. Optionally, the processor may be further configured to identify complications and / or physiological comorbidities based on first and second sensor data. The determination of surgical device settings is additionally based on the identified complications and / or physiological comorbidities. Advantageously, the resulting signals allow for optimization of the surgical device configuration based on how the identified complications and / or physiological comorbidities affect device use. Additionally or alternatively, medical professionals may be notified of complications and / or physiological comorbidities.

[0019] The surgical action may involve motorized stapling. The configuration information may indicate the closing load.

[0020] Surgical action may involve energy application. Configuration information may include power levels.

[0021] The processor may be further configured to receive procedure information. The processor may be configured to determine surgical device settings based on first sensor data, second sensor data, and procedure information. Advantageously, the resulting signals allow the surgical device configuration to be optimized based on procedure information, e.g., the planned procedure, and / or prompt the healthcare provider to take appropriate action regarding the planned procedure. Optionally, the processor may be further configured to identify complications and / or physiological comorbidities based on the first and second sensor data. The determination of surgical device settings is additionally based on the procedure information and the identified complications and / or physiological comorbidities. Advantageously, the resulting signals allow the surgical device configuration to be optimized based on how the identified complications and / or physiological comorbidities affect device use in relation to the planned surgical procedure.

[0022] The first and / or second sensor system may be wearable.

[0023] Using patient-specific and / or surgical environment-specific sensor input combinations to determine more optimal device settings can lead to better device performance and ultimately better patient outcomes.

[0024] A computing device may have a processor configured to receive two points of surgical sensor data from different sensors. The sensors may include wearable patient sensors and / or operating room environment sensor systems. The processor may be configured to determine surgical device settings (e.g., the closing load of an electric surgical stapler, or, for example, the power level of a surgical energy device). The processor may transmit a signal indicating the determined setting. The surgical device may receive the signal and perform a surgical action based on the determined setting.

[0025] The surgical device can include, for example, any one of an electric stapler, an electric stapler generator, an energy device, an energy device generator, an in - operating - room imaging system, a smoke evacuator, a suction irrigation device, or a blowing system. The setting information can include an instruction for any one of a power level, a forward speed, a closing speed, a closing load, or a standby time.

[0026] The processor can be configured to receive the procedure information. The processor can be configured to determine a surgical device setting based on the first surgical sensor data, the second surgical sensor data, and the procedure information. A signal transmitted from the processor can include information indicating an alert. The alert can represent, for example, an identified patient complication associated with the surgical device being used in its existing setting without first switching to the determined surgical device setting.

Brief Description of the Drawings

[0027] [Figure 1A] It is a block diagram of a computer - implemented patient and surgeon monitoring system. [Figure 1B] It is another block diagram of a computer - implemented patient and surgeon monitoring system. [Figure 2A] An example of a surgeon monitoring system in an operating room is shown. [Figure 2B] An example of a patient monitoring system (e.g., a controlled patient monitoring system) is shown. [Figure 2C] An example of a patient monitoring system (e.g., an uncontrolled patient monitoring system) is shown. [Figure 3] Illustrates an exemplary surgical hub paired with various systems. [Figure 4] Illustrates a surgical data network having a set of communication surgical hubs configured to connect to a set of sensing systems, an environmental sensing system, a set of devices, etc. [Figure 5]This paper illustrates an exemplary computer-implemented, interactive surgical system that could be part of a surgical monitoring system. [Figure 6A] An exemplary surgical hub comprising multiple modules coupled to a modular control tower is illustrated. [Figure 6B] An example of a controlled patient monitoring system is illustrated. [Figure 6C] This document illustrates an example of an uncontrolled patient monitoring system. [Figure 7A] This section illustrates a logical diagram of a control system for surgical instruments or tools. [Figure 7B] An exemplary sensing system having a sensor unit and a data processing and communication unit is shown. [Figure 7C] An exemplary sensing system having a sensor unit and a data processing and communication unit is shown. [Figure 7D] An exemplary sensing system having a sensor unit and a data processing and communication unit is shown. [Figure 8] This document illustrates an exemplary surgical procedure timeline demonstrating how to adjust the operating parameters of a surgical device based on the surgeon's biomarker level. [Figure 9] This is a block diagram of a computer-implemented bidirectional surgeon / patient monitoring system. [Figure 10] An exemplary surgical system is shown, comprising a handle having a controller and a motor, an adapter releasably coupled to the handle, and a loading unit releasably coupled to the adapter. [Figure 11A] An example of a sensing system that may be used to monitor surgical biomarkers or patient biomarkers is illustrated. [Figure 11B] An example of a sensing system that may be used to monitor surgical biomarkers or patient biomarkers is illustrated. [Figure 11C] An example of a sensing system that may be used to monitor surgical biomarkers or patient biomarkers is illustrated. [Figure 11D]An example of a sensing system that may be used to monitor surgical biomarkers or patient biomarkers is illustrated. [Figure 12] This is a block diagram of a patient monitoring system or a surgeon monitoring system. [Figure 13A] These are block diagrams depicting the operation of an exemplary system and an exemplary processor for determining surgical device settings, respectively. [Figure 13B] These are block diagrams depicting the operation of an exemplary system and an exemplary processor for determining surgical device settings, respectively. [Figure 14A] This is a plot illustrating an exemplary adjusted score rubric. [Figure 14B] This is a plot illustrating an exemplary adjusted score rubric. [Figure 15] This section illustrates an exemplary user interface for notifications and recommended device settings. [Figure 16] This document illustrates an exemplary user interface for managing computing devices to determine surgical device settings. [Figure 17] This diagram illustrates common-mode and mixed-mode sensor inputs to an exemplary computing device for determining surgical device settings. [Figure 18] This is a diagram illustrating an exemplary process for determining surgical device settings. [Modes for carrying out the invention]

[0028] Figure 1A is a block diagram of a computer-implemented patient and surgeon monitoring system 20000. The patient and surgeon monitoring system 20000 may include one or more surgeon monitoring systems 20002 and one or more patient monitoring systems (e.g., one or more controlled patient monitoring systems 20003 and one or more uncontrolled patient monitoring systems 20004). Each surgeon monitoring system 20002 may include a computer-implemented bidirectional surgical system. Each surgeon monitoring system 20002 may include at least one of a surgical hub 20006 in communication with a cloud computing system 20008, for example, as shown in Figure 2A. Each patient monitoring system may include at least one of a surgical hub 20006 or a computing device 20016 in communication with a cloud computing system 20008, for example, as further shown in Figures 2B and 2C. The cloud computing system 20008 may include at least one remote cloud server 20009 and at least one remote cloud storage unit 20010. Each of the surgeon monitoring system 20002, the controlled patient monitoring system 20003, or the uncontrolled patient monitoring system 20004 may include a wearable sensing system 20011, an environmental sensing system 20015, a robotic system 20013, one or more intelligent instruments 20014, a human interface system 20012, etc. The human interface system is also referred to herein as a human interface device. The wearable sensing system 20011 may include one or more surgeon sensing systems and / or one or more patient sensing systems. The environmental sensing system 20015 may include one or more devices used to measure one or more environmental attributes, for example, as further described in Figure 2A. The robotic system 20013 (same as 20034 in Figure 2A) may include multiple devices used to perform surgical procedures, for example, as further described in Figure 2A.

[0029] The surgical hub 20006 may have collaborative interaction with one of several means of displaying images from a laparoscope and information from one or more other smart devices and one or more sensing systems 20011. The surgical hub 20006 may interact with one or more sensing systems 20011, one or more smart devices, and multiple displays. The surgical hub 20006 may be configured to collect measurement data from one or more sensing systems 20011 and to send notification or control messages to one or more sensing systems 20011. The surgical hub 20006 may transmit and / or receive information, including notification information, to a human interface system 20012. The human interface system 20012 may include one or more human interface devices (HIDs). The surgical hub 20006 can transmit and / or receive notification information or control information to various devices that are in communication with the surgical hub in order to play, display, and / or control information.

[0030] Figure 1B is a block diagram of an exemplary relationship between a sensing system 20001, a biomarker 20005, and a physiological system 20007. This relationship may be employed in a computer-implemented patient and surgeon monitoring system 20000, as well as in the systems, devices, and methods disclosed herein. For example, the sensing system 20001 may include a wearable sensing system 20011 (which may include one or more surgeon sensing systems and one or more patient sensing systems) and an environmental sensing system 20015, as considered in Figure 1A. One or more sensing systems 20001 may measure data relating to various biomarkers 20005. One or more sensing systems 20001 may measure biomarkers 20005 using one or more sensors, such as optical sensors (e.g., photodiodes, photoresistors), mechanical sensors (e.g., motion sensors), acoustic sensors, electrical sensors, electrochemical sensors, thermoelectric sensors, infrared sensors, etc. One or more sensors may measure the biomarker 20005 as described herein, using one or more sensing techniques such as photoplethysmography, electrocardiography, electroencephalography, colorimetric analysis, impedimentary measurement, potentiometric measurement, and current measurement.

[0031] Biomarkers 20005 measured by one or more sensing systems 20001 may include, but are not limited to, sleep, central body temperature, maximal oxygen consumption, physical activity, alcohol consumption, respiratory rate, oxygen saturation, blood pressure, blood glucose, heart rate variability, hydrogen blood potential, hydration status, heart rate, skin conductance, peripheral body temperature, tissue perfusion pressure, cough and sneeze, gastrointestinal motility, gastrointestinal imaging, respiratory bacteria, edema, mental state, sweat, circulating tumor cells, autonomic nervous system tone, circadian rhythm, and / or menstrual cycle.

[0032] Biomarkers 20005 may relate to physiological systems 20007, including but not limited to behavioral and psychological, cardiovascular, renal, cutaneous, nervous, gastrointestinal, respiratory, endocrine, immune, tumor, musculoskeletal, and / or reproductive systems. Information from biomarkers may be interpreted and / or used, for example, by a computer-implemented patient and surgeon monitoring system 20000. Information from biomarkers may be interpreted and / or used by the computer-implemented patient and surgeon monitoring system 20000 to improve the system and / or improve patient outcomes, for example.

[0033] One or more sensing systems 20001, biomarkers 20005, and physiological systems 20007 are described in more detail below.

[0034] A sleep sensing system may measure sleep data including heart rate, respiratory rate, body temperature, movement, and / or brain signals. A sleep sensing system may measure sleep data using photoplethysmogram (PPG), electrocardiogram (ECG), microphone, thermometer, accelerometer, electroencephalogram (EEG), and / or equivalent devices. A sleep sensing system may include wearable devices such as wristbands.

[0035] Based on the measured sleep data, the sleep sensing system may detect sleep biomarkers, including, but not limited to, deep sleep quantifiers, REM sleep quantifiers, sleep disturbance quantifiers, and / or sleep duration. The sleep sensing system may transmit the measured sleep data to a processing unit. The sleep sensing system and / or processing unit may detect deep sleep when the sensing system detects sleep data including a decrease in heart rate, a decrease in respiratory rate, a decrease in body temperature, and / or a decrease in movement. Based on the detected sleep physiology, the sleep sensing system may generate a sleep quality score.

[0036] In the embodiment, the sleep sensing system may transmit a sleep quality score to a computing system such as a surgical hub. In the embodiment, the sleep sensing system may transmit detected sleep biomarkers to a computing system such as a surgical hub. In the embodiment, the sleep sensing system may transmit measured sleep data to a computing system such as a surgical hub. The computing system may derive sleep physiology based on the received measurement data and generate one or more sleep biomarkers, such as deep sleep quantifiers. Based on the sleep biomarkers, the computing system may generate a treatment plan, including a pain management strategy. Based on the sleep biomarkers, the surgical hub may detect potential risk factors or conditions, including systemic inflammation and / or impaired immune function.

[0037] A central temperature sensing system may measure body temperature data, including temperature, emitted frequency spectrum, and / or equivalents. A central temperature sensing system may measure body temperature data using several combinations of thermometers and / or wireless telemetry. A central temperature sensing system may include an ingestive thermometer for measuring gastrointestinal temperature. The ingestive thermometer may wirelessly transmit the measured temperature data. A central temperature sensing system may include a wearable antenna for measuring body radiation spectrum. A central temperature sensing system may include a wearable patch for measuring body radiation spectrum.

[0038] A central body temperature sensing system can calculate body temperature using body temperature data. The central body temperature sensing system can transmit the calculated body temperature to a monitoring device. The monitoring device can track the central body temperature data over time and display it to the user.

[0039] The central body temperature sensing system may process the central body temperature data locally or transmit the data to a processing unit and / or computing system. Based on the measured temperature data, the central body temperature sensing system may detect body temperature-related biomarkers, complications, and / or contextual information, including abnormal temperatures, characteristic fluctuations, infections, menstrual cycles, climate, physical activity, and / or sleep.

[0040] For example, a core temperature sensing system may detect abnormal temperatures based on the temperature being outside the range of 36.5°C to 37.5°C. For example, a core temperature sensing system may detect postoperative infection or sepsis based on certain temperature fluctuations and / or when core temperature reaches an abnormal level. For example, a core temperature sensing system may detect physical activity using measured fluctuations in core temperature.

[0041] For example, a body temperature sensing system may detect core temperature data and trigger the system to release cooling or heating elements to raise or lower body temperature in accordance with the measured ambient temperature.

[0042] In the embodiment, the body temperature sensing system may transmit body temperature-related biomarkers to a computing system such as a surgical hub. In the embodiment, the body temperature sensing system may transmit measured body temperature data to the computing system. The computer system may derive body temperature-related biomarkers based on the received body temperature data.

[0043] A VO2 maximus (VO2 max) sensing system may measure VO2 max data, including oxygen consumption, heart rate, and / or movement speed. The VO2 max sensing system may measure VO2 max data during physical activity, including running and / or walking. The VO2 max sensing system may include a wearable device. The VO2 max sensing system may process the VO2 max data locally or transmit the data to a processing unit and / or computing system.

[0044] Based on the measured VO2 maxima data, the sensing system and / or computing system may derive, detect, and / or calculate biomarkers including the VO2 maxima quantifier, VO2 maxima score, physical activity, and / or the intensity of physical activity. The VO2 maxima sensing system may select the correct VO2 maxima data measurements within the correct time segments to calculate accurate VO2 maxima information. Based on the VO2 maxima information, the sensing system may detect dominant cardiac, vascular, and / or respiratory limiting factors. Based on the VO2 maxima information, risks, including an increased risk of adverse cardiovascular events and / or in-hospital morbidity during surgery, may be predicted. For example, an increased risk of in-hospital morbidity may be detected when the calculated VO2 maxima quantifier falls below a specific threshold, such as 18.2 ml / kg-1 / min-1.

[0045] In the embodiment, the VO2 maximum sensing system may transmit VO2 maximum-associated biomarkers to a computing system such as a surgical hub. In the embodiment, the VO2 maximum sensing system may transmit measured VO2 maximum data to a computing system. The computer system may derive VO2 maximum-associated biomarkers based on the received VO2 maximum data.

[0046] A physical activity sensing system may measure physical activity data, including heart rate, exercise, location, posture, range of motion, speed of movement, and / or gait. The system may also measure physical activity data including accelerometers, magnetometers, gyroscopes, global positioning systems (GPS), PPG, and / or ECG. A physical activity sensing system may include wearable devices. These wearable devices may include, but are not limited to, watches, wristbands, vests, gloves, belts, headbands, shoes, and / or clothing. The physical activity sensing system may process the physical activity data locally or transmit the data to a processing unit and / or computing system.

[0047] Based on the measured physical activity data, the physical activity sensing system can detect biomarkers associated with physical activity, including but not limited to exercise activity, physical activity intensity, physical activity frequency, and / or physical activity duration. Based on the physical activity information, the physical activity sensing system can generate a summary of physical activity.

[0048] For example, a physical activity sensing system may transmit physical activity information to a computing system. For example, a physical activity sensing system may transmit measurement data to a computing system. Based on the physical activity information, the computing system may generate an activity summary, a training plan, and / or a recovery plan. The computing system may store the physical activity information in a user profile. The computing system may display the physical activity information in a graph. The computing system may select information on specific physical activities and display the information together or separately.

[0049] An alcohol consumption sensing system may measure alcohol consumption data, including alcohol and / or sweat. The alcohol consumption sensing system may use a pump to measure sweat. The pump may use a fuel cell that reacts with ethanol to detect the presence of alcohol in the sweat. The alcohol consumption sensing system may include a wearable device, such as a wristband. The alcohol consumption sensing system may use a microfluidic application to measure alcohol and / or sweat. The microfluidic application may measure alcohol consumption data using a commercially available ethanol sensor, sweat stimulation, and wicking. The alcohol consumption sensing system may include a wearable patch that adheres to the skin. The alcohol consumption sensing system may include a breathalyzer. The sensing system may process the alcohol consumption data locally or transmit the data to a processing unit and / or computing system.

[0050] Based on measured alcohol consumption data, the sensing system may calculate blood alcohol concentration. The sensing system may detect alcohol consumption status and / or risk factors. The sensing system may detect alcohol consumption-related biomarkers, including weakened immune function, heart failure, and / or arrhythmias. Weakened immune function may occur if a patient consumes three or more alcohol units per day. The sensing system may detect risk factors for postoperative complications, including infection, cardiopulmonary complications, and / or bleeding symptoms. Healthcare providers may use the detected risk factors to predict or detect postoperative or postoperative complications, for example, to influence decisions and precautions taken during postoperative care.

[0051] In the embodiment, the alcohol consumption sensing system may transmit alcohol consumption-related biomarkers to a computing system such as a surgical hub. In the embodiment, the alcohol consumption sensing system may transmit measured alcohol consumption data to the computing system. The computer system may derive alcohol consumption-related biomarkers based on the received alcohol consumption data.

[0052] A respiratory sensing system may measure respiratory rate data, including inspiration, expiration, chest cavity movement, and / or airflow. The respiratory sensing system may measure respiratory rate data mechanically and / or acoustically. The respiratory sensing system may measure respiratory rate data using a ventilator. The respiratory sensing system may mechanically measure respiratory data by detecting chest cavity movement. Two or more applied electrodes on the chest may detect expansion and contraction of the chest cavity during breathing by measuring changes in the distance between the electrodes. The respiratory sensing system may include a wearable skin patch. The respiratory sensing system may acoustically measure respiratory data by recording airflow sounds using a microphone. The respiratory sensing system may process respiratory data locally or transmit the data to a processing unit and / or computing system.

[0053] Based on the measured respiratory data, the respiratory sensing system can generate respiratory-related biomarkers, including breath frequency, breath pattern, and / or breath depth. Based on the respiratory rate data, the respiratory sensing system can generate a respiratory quality score.

[0054] Based on respiratory rate data, the respiratory sensing system may detect respiratory-related biomarkers, including irregular breathing, pain, air leakage, collapsed lungs, lung tissue and strength, and / or shock. For example, the respiratory sensing system may detect irregularity based on changes in breath frequency, breath pattern, and / or breath depth. For example, the respiratory sensing system may detect postoperative pain based on short, sharp breaths. For example, the respiratory sensing system may detect air leakage based on the volume difference between inspiration and expiration. For example, the respiratory sensing system may detect collapsed lungs based on increased breath frequency combined with constant volume inhalation. For example, the respiratory sensing system may detect lung tissue strength and shock, including systemic inflammatory response syndrome (SIRS), based on increased respiratory rate with a standard deviation of more than 2. In embodiments, the detections described herein may be performed by a computing system based on measurement data and / or related biomarkers generated by the respiratory sensing system.

[0055] An oxygen saturation sensing system may measure oxygen saturation data including light absorption, light transmission, and / or light reflectance. The oxygen saturation sensing system may use pulse oximetry. For example, the oxygen saturation sensing system may use pulse oximetry by measuring the absorption spectra of deoxygenated and oxygenated hemoglobin. The oxygen saturation sensing system may include one or more light-emitting diodes (LEDs) having a predetermined wavelength. The LEDs can illuminate hemoglobin. The oxygen saturation sensing system may measure the amount of illuminated light absorbed by hemoglobin. The oxygen saturation sensing system may measure the amount of transmitted and / or reflected light from the illuminated light wavelength. The oxygen saturation sensing system may include wearable devices, including earphones and / or a watch. The oxygen saturation sensing system may process the measured oxygen saturation data locally or transmit the data to a processing unit and / or computing system.

[0056] Based on oxygen saturation data, an oxygen saturation sensing system can calculate oxygen saturation-related biomarkers, including peripheral blood oxygen saturation (SpO2), hemoglobin oxygen concentration, and / or changes in oxygen saturation. For example, an oxygen saturation sensing system may calculate SpO2 using the ratio of measured absorbances for each light wavelength applied.

[0057] Based on oxygen saturation data, an oxygen saturation sensing system can predict oxygen saturation-related biomarkers, complications, and / or contextual information, including cardiothoracic function, delirium, collapsed lung, and / or recovery rate. For example, an oxygen saturation sensing system may detect postoperative delirium when the sensing system measures a preoperative SpO2 value of less than 59.5%. For example, an oxygen saturation sensing system may be useful in monitoring the recovery of a patient after surgery. Low SpO2 can reduce the tissue's ability to repair itself because low oxygen can reduce the amount of energy that cells can produce. For example, an oxygen saturation sensing system may detect collapsed lung based on low postoperative oxygen saturation. In the examples, the detections described herein may be performed by a computing system based on measurement data and / or related biomarkers generated by the oxygen saturation sensing system.

[0058] A blood pressure sensing system may measure blood pressure data including vessel diameter, tissue volume, and / or pulse wave propagation time. Blood pressure sensing systems may measure blood pressure data using oscillometric measurements, ultrasound patches, photoplethysmography, and / or arterial tonometry. A blood pressure sensing system using photoplethysmography may include a photodetector that senses light scattered by light emitted from an optical emitter. A blood pressure sensing system using arterial tonometry may utilize arterial wall flattening. A blood pressure sensing system may include an inflatable cuff, wristband, watch, and / or ultrasound patch.

[0059] Based on measured blood pressure data, the blood pressure sensing system can quantify blood pressure-related biomarkers, including systolic blood pressure, diastolic blood pressure, and / or pulse wave conduction time. The blood pressure sensing system may use these blood pressure-related biomarkers to detect blood pressure-related conditions, such as abnormal blood pressure. The system may detect abnormal blood pressure when measured systolic and diastolic blood pressure are outside the range of 90 / 60 to 120-90 (systolic / diastolic). For example, based on measured low blood pressure, the system may detect postoperative septic shock or hypovolemic shock. For example, based on detected hypertension, the system may detect the risk of edema. Based on measured blood pressure data, the system may predict the required seal strength for a harmonic seal. Higher blood pressure may require a stronger seal to overcome rupture. The system may display blood pressure information locally or transmit the data to the system. The sensing system may graphically display blood pressure information over a period of time.

[0060] The blood pressure sensing system may process the blood pressure data locally or transmit the data to a processing unit and / or a computing system. In the embodiments, the detection, prediction and / or determination described herein may be performed by the computing system based on the measurement data and / or associated biomarkers generated by the blood pressure sensing system.

[0061] A blood glucose sensing system can measure blood glucose data, including blood glucose levels and / or tissue glucose levels. A blood glucose sensing system can measure blood glucose data non-invasively. A blood glucose sensing system may use an earlobe clip. A blood glucose sensing system may display blood glucose data.

[0062] Based on measured blood glucose data, the blood glucose sensing system can infer blood glucose irregularity. Blood glucose irregularity may include blood glucose levels that fall outside a certain threshold of normally occurring values. Normal blood glucose levels may range from 70 to 120 mg / dL when fasting. Normal blood glucose levels may range from 90 to 160 mg / dL when not fasting.

[0063] For example, a blood glucose sensing system may detect a low fasting blood glucose level when the blood glucose level falls below 50 mg / dL. For example, a blood glucose sensing system may detect a high fasting blood glucose level when the blood glucose level exceeds 315 mg / dL. Based on the measured blood glucose level, the blood glucose sensing system may detect blood glucose-related biomarkers, complications, and / or contextual information, including diabetes-related peripheral artery disease, stress, agitation, reduced blood flow, risk of infection, and / or reduced recovery time.

[0064] The blood glucose sensing system may process blood glucose data locally or transmit the data to a processing unit and / or computing system. In embodiments, the detection, prediction and / or determination described herein may be performed by the computing system based on the measurement data and / or associated biomarkers generated by the blood glucose sensing system.

[0065] A heart rate variability (HRV) sensing system can measure HRV data, including heart rate and / or the duration between consecutive heartbeats. HRV sensing systems can measure HRV data electrically or optically. HRV sensing systems can electrically measure heart rate variability data using ECG traces. HRV sensing systems can measure variability in the duration between R peaks in QRS complex using ECG traces. HRV sensing systems can optically measure heart rate variability using PPG traces. HRV sensing systems can measure variability in the duration of heart rate intervals using PPG traces. HRV sensing systems can measure HRV data over a set time interval. HRV sensing systems may include wearable devices, including rings, watches, wristbands, and / or patches.

[0066] Based on HRV data, the HRV sensing system may detect HRV-related biomarkers, complications, and / or contextual information, including cardiovascular health status, HRV changes, menstrual cycle, dietary monitoring, anxiety levels, and / or physical activity. For example, the HRV sensing system may detect high cardiovascular health status based on high HRV. For example, the HRV sensing system may predict preoperative stress and use preoperative stress to predict postoperative pain. For example, the HRV sensing system may indicate postoperative infection or sepsis based on a decrease in HRV.

[0067] The HRV sensing system may process HRV data locally or transmit the data to a processing unit and / or computing system. In the embodiments, the detection, prediction, and / or determination described herein may be performed by the computing system based on the measurement data and / or associated biomarkers generated by the HRV sensing system.

[0068] A potential of hydrogen (pH) sensing system can measure pH data, including blood pH and / or sweat pH. The pH sensing system can measure pH data invasively and / or non-invasively. The pH sensing system can measure pH data non-invasively using colorimetric methods and pH-sensitive dyes within a microfluidic circuit. In the colorimetric method, the pH-sensitive dye can change color in response to the pH of sweat. The pH sensing system can measure pH using optical spectroscopy and match the color change of the pH-sensitive dye to the pH value. The pH sensing system may include a wearable patch. The pH sensing system can measure pH data during physical activity.

[0069] Based on the measured pH data, the pH sensing system can detect pH-related biomarkers, including normal blood pH, abnormal blood pH, and / or acidic blood pH. The pH sensing system can also detect pH-related biomarkers, complications, and / or contextual information by comparing the measured pH data to a standard pH scale. The standard pH scale can identify a healthy pH range, including values ​​between 7.35 and 7.45.

[0070] pH sensing systems may use pH-related biomarkers to indicate pH conditions including postoperative internal bleeding, acidosis, sepsis, pulmonary collapse, and / or hemorrhage. For example, a pH sensing system may predict postoperative internal bleeding based on preoperative acidic blood pH. Acidic blood can reduce blood clotting ability by inhibiting thrombin production. For example, a pH sensing system may predict sepsis and / or hemorrhage based on acidic pH. Lactic acidosis can cause acidic pH. A pH sensing system may continuously monitor blood pH data because acidosis can only occur during exercise.

[0071] The pH sensing system may process the pH data locally or transmit the pH data to a processing unit and / or a computing system. In the embodiments, the detection, prediction, and / or determination described herein may be performed by the computing system based on the measurement data and / or associated biomarkers generated by the pH sensing system.

[0072] A hydration sensing system may measure hydration data, including water-light absorption, water-light reflection, and / or sweat levels. The hydration sensing system may use optical spectroscopy or sweat-based colorimetric analysis. Optical spectroscopy may be used by shining synchrotron radiation onto the skin and measuring the reflected light. Optical spectroscopy can measure water content by measuring the amplitude of reflected light from certain wavelengths, including 1720 nm, 1750 nm, and / or 1770 nm. The hydration sensing system may include a wearable device capable of shining light onto the skin. The wearable device may include a watch. The hydration sensing system may use sweat-based colorimetric analysis to measure sweat levels. Sweat-based colorimetric analysis may be processed in conjunction with user activity data and / or user water intake data.

[0073] Based on hydration data, the hydration status sensing system can detect water content. Based on water content, the hydration status sensing system can identify biomarkers, complications, and / or contextual information related to hydration, including dehydration, risk of renal injury, reduced blood flow, risk of intraoperative or postoperative hypovolemic shock, and / or reduced blood volume.

[0074] For example, a hydration status sensing system may detect health risks based on identified hydration. Dehydration can have adverse effects on overall health. For example, a hydration status sensing system may predict the risk of postoperative acute kidney injury when it detects reduced blood flow resulting from low hydration levels. For example, a hydration status sensing system may calculate the risk of intraoperative or postoperative hypovolemic shock when the sensing system detects dehydration or a decrease in blood volume. A hydration status sensing system may use hydration level information to provide context for other received biomarker data, which may include heart rate. A hydration status sensing system may continuously measure hydration status data. Continuous measurement may take into account various factors, including exercise, fluid intake, and / or temperature, that may affect hydration status data.

[0075] The hydration status sensing system may process the hydration data locally or transmit the data to a processing unit and / or a computing system. In embodiments, the detection, prediction, and / or determination described herein may be performed by a computing system based on the measurement data and / or associated biomarkers generated by the hydration status sensing system.

[0076] A heart rate sensing system may measure heart rate data including cardiac chamber expansion, cardiac chamber contraction, and / or reflected light. A heart rate sensing system may use an ECG and / or PPG to measure heart rate data. For example, a heart rate sensing system using an ECG may include a wireless transmitter, a receiver, and one or more electrodes. The wireless transmitter and receiver may record the voltage between electrodes positioned on the skin, resulting from ventricular expansion and contraction. The heart rate sensing system may calculate the heart rate using the measured voltage. For example, a heart rate sensing system using a PPG may shine green light onto the skin and record the reflected light in a photodetector. The heart rate sensing system may calculate the heart rate using the measured light absorbed by the blood over a period of time. A heart rate sensing system may include a watch, a wearable elastic band, a skin patch, a bracelet, clothing, a wrist strap, earphones, and / or a headband. For example, a heart rate sensing system may include a wearable chest patch. A wearable chest patch may measure heart rate data, as well as other vital signs or critical data including respiratory rate, skin temperature, body posture, fall detection, single-lead ECG, RR interval, and step count. The wearable chest patch may process heart rate data locally or transmit the data to a processing unit. The processing unit may include a display.

[0077] Based on measured heart rate data, the heart rate sensing system can calculate heart rate-related biomarkers, including heart rate, heart rate variability, and / or mean heart rate. Based on heart rate data, the heart rate sensing system can detect biomarkers, complications, and / or contextual information, including stress, pain, infection, and / or sepsis. The heart rate sensing system can detect heart rate conditions when the heart rate exceeds a normal threshold. A normal heart rate threshold may include a range of 60 to 100 heartbeats per minute. Based on an increase in heart rate, including a heart rate exceeding 90 beats / minute, the heart rate sensing system can diagnose postoperative infection, sepsis, or hypovolemic shock.

[0078] The heart rate sensing system may process heart rate data locally or transmit the data to a processing unit and / or computing system. In embodiments, the detection, prediction, and / or determination described herein may be performed by the computing system based on the measurement data and / or associated biomarkers generated by the heart rate sensing system. The heart rate sensing system may transmit heart rate information to a computing system such as a surgical hub. The computing system may collect and display cardiovascular parameter information, including heart rate, respiration, body temperature, blood pressure, arrhythmia, and / or atrial fibrillation. Based on the cardiovascular parameter information, the computing system may generate a cardiovascular health status score.

[0079] A skin conductance sensing system can measure skin conductance data, including electrical conductivity. A skin conductance sensing system may include one or more electrodes. A skin conductance sensing system may measure electrical conductivity by applying a voltage between the electrodes. The electrodes may include silver or silver chloride. A skin conductance sensing system may be placed on one or more fingers. For example, a skin conductance sensing system may include a wearable device. A wearable device may include one or more sensors. A wearable device may be attached to one or more fingers. Skin conductance data may vary based on sweat levels.

[0080] A skin conductance sensing system can process skin conductance data locally or transmit the data to a computing system. Based on the skin conductance data, the skin conductance sensing system can calculate skin conductance-related biomarkers, including sympathetic nerve activity levels. For example, a skin conductance sensing system can detect high sympathetic nerve activity levels based on high skin conductance.

[0081] Peripheral temperature sensing systems can measure peripheral temperature data, including limb temperature. Peripheral temperature sensing systems may include thermometers, thermoelectric effects, or infrared thermometers to measure peripheral temperature data. For example, a peripheral temperature sensing system using a thermistor can measure the resistance of the thermistor. The resistance may vary as a function of temperature. For example, a peripheral temperature sensing system utilizing the thermoelectric effect can measure the output voltage. The output voltage may increase as a function of temperature. For example, a peripheral temperature sensing system using an infrared thermometer can measure the intensity of radiation emitted from the body's blackbody radiation. The intensity of the radiation may increase as a function of temperature.

[0082] Based on peripheral temperature data, the peripheral temperature sensing system can determine peripheral temperature-related biomarkers, including basal body temperature, limb skin temperature, and / or peripheral temperature patterns. Based on peripheral temperature data, the peripheral temperature sensing system can detect conditions, including diabetes.

[0083] A peripheral temperature sensing system may process peripheral temperature data and / or biomarkers locally, or transmit the data to a processing unit. For example, a peripheral temperature sensing system may transmit peripheral temperature data and / or biomarkers to a computing system such as a surgical hub. The computing system may analyze the peripheral temperature information using other biomarkers, including central temperature, sleep, and menstrual cycle. For example, the detection, prediction, and / or determination described herein may be performed by the computing system based on the measurement data and / or associated biomarkers generated by the peripheral temperature sensing system.

[0084] A tissue perfusion pressure sensing system can measure tissue perfusion pressure data, including skin perfusion pressure. The tissue perfusion pressure sensing system may use optical methods to measure tissue perfusion pressure data. For example, the system may illuminate the skin and measure the transmitted and reflected light to detect changes in blood flow. The system may apply occlusion. For example, the system may determine skin perfusion pressure based on the measured pressure used to restore blood flow after occlusion. The system may use strain gauges or laser Doppler flowmeters to measure pressure to restore blood flow after occlusion. Measured changes in the frequency of light caused by blood movement may directly correlate with the number and velocity of red blood cells, and the system may use this to calculate pressure. The system may monitor tissue valves during surgery to measure tissue perfusion pressure data.

[0085] Based on measured tissue perfusion pressure data, the tissue perfusion pressure sensing system may detect tissue perfusion pressure-related biomarkers, complications, and / or contextual information, including hypovolemia, internal bleeding, and / or tissue mechanical properties. For example, the tissue perfusion pressure sensing system may detect hypovolemia and / or internal bleeding based on a decrease in perfusion pressure. Based on measured tissue perfusion pressure data, the tissue perfusion pressure sensing system may notify surgical tool parameters and / or medical procedures. For example, the tissue perfusion pressure sensing system may determine tissue mechanical properties using tissue perfusion pressure data. Based on the determined mechanical properties, the sensing system may generate stapling procedures and / or stapling tool parameter adjustments. Based on the determined mechanical properties, the sensing system may notify incision procedures. Based on measured tissue perfusion pressure data, the tissue perfusion pressure sensing system may generate a score regarding the overall validity of perfusion.

[0086] The tissue perfusion pressure sensing system may process the tissue perfusion pressure data locally or transmit the data to a processing unit and / or computing system. In embodiments, the detection, prediction, determination, and / or generation described herein may be performed by the computing system based on the measurement data and / or associated biomarkers generated by the tissue perfusion pressure sensing system.

[0087] A cough and sneeze detection system may measure cough and sneeze data, including cough, sneeze, movement, and sound. A cough and sneeze detection system may track hand or body movements that may result from a user covering their mouth while coughing or sneezing. The detection system may include an accelerometer and / or microphone. The detection system may include a wearable device. The wearable device may include a watch.

[0088] Based on cough and sneeze data, the sensing system may detect cough and sneeze-related biomarkers, which include, but are not limited to, cough frequency, sneeze frequency, cough severity, and / or sneeze severity. The sensing system may use the cough and sneeze information to establish a cough and sneeze baseline. The cough and sneeze sensing system may process the cough and sneeze data locally or transmit the data to a computing system.

[0089] Based on cough and sneeze data, the sensing system may detect biomarkers, complications, and / or contextual information associated with cough and sneeze, including respiratory tract infections, infections, collapsed lungs, pulmonary edema, gastroesophageal reflux disease, allergic rhinitis, and / or systemic inflammation. For example, a cough and sneeze sensing system may indicate gastroesophageal reflux disease when the sensing system measures chronic cough. Chronic cough can lead to inflammation of the lower esophagus. Lower esophageal inflammation can affect the properties of gastric tissue for sleeve gastrectomy. For example, a cough and sneeze sensing system may detect allergic rhinitis based on sneezing. Sneezing may be linked to systemic inflammation. Systemic inflammation can affect the mechanical properties of the lungs and / or other tissues. In embodiments, the detection, prediction, and / or determination described herein may be performed by a computing system based on measurement data and / or associated biomarkers generated by the cough and sneeze sensing system.

[0090] A gastrointestinal (GI) motility sensing system may measure GI motility data, including pH, temperature, pressure, and / or gastric contraction. The GI motility sensing system may utilize gastric electromyography, gastrointestinal electromyography, a stethoscope, and / or ultrasound. The GI motility sensing system may include a non-digestible capsule. For example, an ingestible sensing system may adhere to the inner lining of the stomach. The ingestible sensing system may measure contraction using a piezoelectric device that generates a voltage when deformed.

[0091] Based on GI data, the sensing system may calculate GI motility-related biomarkers, including gastric, small intestine, and / or colon transit times. Based on gastrointestinal motility information, the sensing system may detect GI motility-related conditions, including intestinal obstruction. The GI motility sensing system may detect intestinal obstruction based on decreased small intestinal motility. The GI motility sensing system may notify healthcare professionals when it detects a GI motility condition. The GI motility sensing system may process GI motility data locally or transmit the data to a processing unit. In embodiments, the detection, prediction, and / or determination described herein may be performed by a computing system based on measurement data and / or related biomarkers generated by the GI motility sensing system.

[0092] The GI tube imaging / sensing system may collect images of the patient's colon. The GI tube imaging / sensing system may include an ingestible wireless camera and receiver. The GI tube imaging / sensing system may include one or more white LEDs, a battery, a wireless transmitter, and an antenna. The ingestible camera may include a pill. The ingestible camera may advance through the digestive tract and take pictures of the colon. The ingestible camera may take pictures at up to 35 frames per second during a motility cycle. The ingestible camera may transmit the pictures to a receiver. The receiver may include a wearable device. The GI tube imaging / sensing system may process the images locally or transmit the images to a processing unit. The physician may view the raw images to make a diagnosis.

[0093] Based on GI tube images, the GI tube imaging and sensing system may identify GI tube-related biomarkers, including mechanical properties of gastric or colonic tissue. Based on the collected images, the GI tube imaging and sensing system may detect GI tube-related biomarkers, complications, and / or contextual information, including mucosal inflammation, Crohn's disease, anastomotic leakage, esophagitis, and / or gastritis. The GI tube imaging / sensing system may replicate a physician's diagnosis using image analysis software. The GI tube imaging / sensing system may process images locally or transmit data to a processing unit. In embodiments, the detection, prediction, and / or determination described herein may be performed by a computing system based on measurement data and / or associated biomarkers generated by the GI tube imaging / sensing system.

[0094] An airway bacterial detection system may measure bacterial data, including foreign DNA or bacteria. The airway bacterial detection system may use radio frequency identification (RFID) tags and / or an electronic nose (e-nose). A detection system using RFID tags may include one or more gold electrodes, a graphene sensor, and / or a peptide layer. The RFID tag may bind to bacteria. When bacteria bind to the RFID tag, the graphene sensor may detect a change in the presence of the bacterial signal-to-signal pair. The RFID tag may include an implant. The implant may be bonded to a tooth. The implant may transmit bacterial data. The detection system may use a portable e-nose to measure bacterial data.

[0095] Based on measured bacterial data, the airway bacterial sensing system may detect bacterial-related biomarkers, including bacterial levels. Based on the bacterial data, the airway bacterial sensing system may generate an oral health score. Based on the detected bacterial data, the airway bacterial sensing system may identify bacterial-related biomarkers, complications, and / or contextual information, including pneumonia, lung infections, and / or pneumonia. The airway bacterial sensing system may process the bacterial information locally or transmit the data to a processing unit. In the embodiments, the detection, prediction, and / or determination described herein may be performed by a computing system based on the measured data and / or related biomarkers generated by the airway bacterial sensing system.

[0096] The edema sensing system may measure edema data, including lower limb circumference, leg volume, and / or leg water content level. The edema sensing system may include force-sensing resistors, strain gauges, accelerometers, gyroscopes, magnetometers, and / or ultrasound. The edema sensing system may include wearable devices. For example, the edema sensing system may include socks, stockings, and / or ankle bands.

[0097] Based on the measured edema data, the edema sensing system may detect edema-related biomarkers, complications, and / or contextual information, including inflammation, rate of change in inflammation, poor healing, infection, leakage, colorectal anastomosis leakage, and / or fluid accumulation.

[0098] For example, an edema sensing system may detect the risk of colorectal anastomosis leakage based on fluid accumulation. Based on the detected edema physiological state, the edema sensing system may generate a healing quality score. For example, the edema sensing system may generate a healing quality score by comparing edema information with a specific threshold lower limb circumference. Based on the detected edema information, the edema sensing system may generate edema tool parameters, including responsiveness to stapler compression. The edema sensing system may provide context for measured edema data by using measurements from an accelerometer, gyroscope, and / or magnetometer. For example, the edema sensing system may detect whether the user is sitting, standing, or lying down.

[0099] The edema sensing system may process the measured edema data locally or transmit the edema data to a processing unit. In the embodiments, the detection, prediction, and / or determination described herein may be performed by a computing system based on the measured data and / or associated biomarkers generated by the edema sensing system.

[0100] A mental state sensing system may measure mental state data, including heart rate, heart rate variability, brain activity, skin conductance, skin temperature, electrocutaneous response, movement, and / or sweat rate. The mental state sensing system may measure mental state data over a set duration to detect changes in mental state data. The mental state sensing system may include a wearable device, which may include a wristband.

[0101] Based on mental state data, the sensing system may detect mental state-related biomarkers, including emotional patterns, positivity levels, and / or optimism levels. Based on the detected mental state information, the mental state sensing system may identify mental state-related biomarkers, complications, and / or contextual information, including cognitive impairment, stress, anxiety, and / or pain. Based on the mental state information, the mental state sensing system may generate mental state scores, including positivity scores, optimism scores, confusion or delirium scores, mental agility scores, stress scores, anxiety scores, depression scores, and / or pain scores.

[0102] Using mental state data, associated biomarkers, complications, contextual information, and / or mental state scores, a course of treatment, including pain relief therapy, may be determined. For example, postoperative pain may be predicted if preoperative anxiety and / or depression are detected. For example, based on detected positivity and optimism levels, the mental state sensing system may determine mood quality and mental state. Based on mood quality and mental state, the mental state sensing system may suggest additional care interventions that would benefit the patient, including paint therapy and / or psychological support. For example, based on detected cognitive impairment, confusion, and / or mental agility, the mental state sensing system may indicate conditions including delirium, encephalopathy, and / or sepsis. Delirium may involve hyperactivity or hypoactivity. For example, based on detected stress and anxiety, the mental state sensing system may indicate conditions including hospital anxiety and / or depression. Based on detected hospital anxiety and / or depression, the mental state sensing system may generate a treatment plan, including pain relief therapy and / or preoperative support.

[0103] In the embodiments, the detection, prediction, and / or determination described herein may be performed by a computing system based on measurement data and / or associated biomarkers generated by a mental state sensing system. The mental state sensing system may process the mental state data locally or transmit the data to a processing unit.

[0104] A sweat-sensing system may measure sweat data including sweat, sweating rate, cortisol, adrenaline, and / or lactate. The sweat-sensing system may measure sweat data using microfluidic capture, saliva testing, nanoporous electrode systems, e-nose, reverse ion electrophoresis, blood tests, current-measuring thin-film biosensors, textile organic electrochemical transistor devices, and / or electrochemical biosensors. The sensing system may measure sweat data using microfluidic capture with colorimetric or impedance-measuring methods. Microfluidic capture may include flexible patches placed in contact with the skin. The sweat-sensing system may measure cortisol using saliva testing. Saliva testing may use electrochemical methods and / or molecularly selective organic electrochemical transistor devices. The sweat-sensing system may measure ion accumulation bound to cortisol in sweat to calculate cortisol levels. The sweat-sensing system may use enzymatic reactions to measure lactate. Lactate may be measured using lactate oxidase and / or lactate dehydrogenase methods.

[0105] Based on measured sweat data, the sweat sensing system or processing unit may detect sweat-related biomarkers, complications, and / or contextual information, including cortisol levels, adrenaline levels, and / or lactate levels. Based on the detected sweat data and / or related biomarkers, the sweat sensing system may indicate sweat physiological states, including sympathetic nervous system activity, psychological stress, cellular immunity, circadian rhythms, blood pressure, tissue oxygenation, and / or postoperative pain. For example, based on sweat rate data, the sweat sensing system may detect psychological stress. Based on detected psychological stress, the sweat sensing system may indicate increased sympathetic nervous system activity. Increased sympathetic nervous system activity may indicate postoperative pain.

[0106] Based on detected sweat information, the sweat-sensing system may detect sweat-related biomarkers, complications, and / or contextual information, including postoperative infection, metastasis, chronic elevation, ventricular failure, sepsis, hemorrhage, hyperlactatemia, and / or septic shock. For example, the sensing system may detect septic shock when serum lactate concentration exceeds a certain level, such as 2 mmol / L. For example, based on detected adrenaline surge patterns, the sweat-sensing system may indicate a risk of cardiac attack and / or stroke. For example, surgical tool parameter adjustments may be determined based on detected adrenaline levels. Surgical tool parameter adjustments may include setting surgical sealing tools. For example, the sweat-sensing system may predict infection risk and / or metastasis based on detected cortisol levels. The sweat-sensing system may notify healthcare professionals about the condition.

[0107] In the embodiments, the detection, prediction, and / or determination described herein may be performed by a computing system based on measurement data and / or associated biomarkers generated by the sweat-sensing system. The sweat-sensing system may process the sweat data locally or transmit the sweat data to a processing unit.

[0108] A circulating tumor cell sensing system can detect circulating tumor cells. The circulating tumor cell sensing system can detect circulating tumor cells using a contrast agent. The contrast agent may be microbubbles coated with antibodies that target circulating tumor cells. The contrast agent may be injected into the bloodstream. The contrast agent may adhere to circulating tumor cells. The circulating tumor cell sensing system may include an ultrasound transmitter and receiver. The ultrasound transmitter and receiver can detect the contrast agent attached to circulating tumor cells. The circulating tumor cell sensing system can receive circulating tumor cell data.

[0109] Based on the detected circulating tumor cell data, the circulating tumor cell sensing system may calculate the risk of metastasis. The presence of circulating cancer cells may indicate a risk of metastasis. A circulating cancer cell count exceeding a threshold per milliliter of blood may indicate a risk of metastasis. Cancer cells can circulate in the bloodstream when a tumor metastasizes. Based on the calculated risk of metastasis, the circulating tumor cell sensing system may generate a surgical risk score. Based on the generated surgical risk score, the circulating tumor cell sensing system may indicate surgical survival rates and / or proposed surgical precautions.

[0110] In the embodiments, the detection, prediction, and / or determination described herein may be performed by a computing system based on measurement data and / or associated biomarkers generated by the circulating tumor cell sensing system. The circulating tumor cell sensing system may process the circulating tumor cell data locally or transmit the circulating tumor cell data to a processing unit.

[0111] An autonomic nervous system tension sensing system may measure autonomic nervous system tension data, including skin conductance, heart rate variability, activity, and / or peripheral body temperature. The autonomic nervous system tension sensing system may include one or more electrodes, PPG traces, ECG traces, accelerometers, GPS, and / or thermometers. The autonomic nervous system tension sensing system may include wearable devices, such as wristbands and / or finger bands.

[0112] Based on autonomic nervous system tension data, the autonomic nervous system tension sensing system may detect autonomic nervous system tension-related biomarkers, complications, and / or contextual information, including sympathetic and / or parasympathetic nervous system activity levels. Autonomic nervous system tension may explain the underlying equilibrium between the sympathetic and parasympathetic nervous systems. Based on measured autonomic nervous system tension data, the autonomic nervous system tension sensing system may indicate the risk of postoperative conditions, including inflammation and / or infection. High sympathetic activity may be associated with increased inflammatory mediators, suppressed immune function, postoperative bowel obstruction, increased heart rate, increased skin conductance, increased sweating rate, and / or anxiety.

[0113] In the embodiments, the detection, prediction, and / or determination described herein may be performed by a computing system based on measurement data and / or associated biomarkers generated by an autonomic nervous system tension sensing system. The autonomic nervous system tension sensing system may process the autonomic nervous system tension data locally or transmit the data to a processing unit.

[0114] A circadian rhythm sensing system may measure circadian rhythm data, including light exposure, heart rate, central body temperature, cortisol levels, activity, and / or sleep. Based on the circadian rhythm data, the circadian rhythm sensing system may detect circadian rhythm-related biomarkers, complications, and / or contextual information, including sleep cycles, wake cycles, circadian patterns, circadian rhythm disturbances, and / or hormone activity.

[0115] For example, based on measured circadian rhythm data, a circadian rhythm sensing system can calculate the start and end of a circadian cycle. Based on measured cortisol levels, the system may indicate the start of a circadian day. Cortisol levels may peak at the start of a circadian day. Based on measured heart rate and / or core temperature, the system may indicate the end of a circadian day. Heart rate and / or core temperature may decrease at the end of a circadian day. Based on circadian rhythm-related biomarkers, the sensing system or processing unit may detect conditions that include a risk of infection and / or pain. For example, a disruption of the circadian rhythm may manifest as pain and discomfort.

[0116] In the embodiments, the detection, prediction, and / or determination described herein may be performed by a computing system based on measurement data and / or associated biomarkers generated by a circadian rhythm sensing system. The circadian rhythm sensing system may process the circadian rhythm data locally or transmit the data to a processing unit.

[0117] A menstrual cycle sensing system may measure menstrual cycle data, including heart rate, heart rate variability, respiratory rate, body temperature, and / or skin perfusion. Based on the menstrual cycle data, the menstrual cycle unit may indicate menstrual cycle-related biomarkers, complications, and / or contextual information, including menstrual cycle stages. For example, a menstrual cycle sensing system may detect ovulation in a menstrual cycle based on measured heart rate variability. Changes in heart rate variability may indicate ovulation. For example, a menstrual cycle sensing system may detect the luteal phase in a menstrual cycle based on measured wrist skin temperature and / or skin perfusion. An increase in wrist skin temperature may indicate the luteal phase. Changes in skin perfusion may indicate the luteal phase. For example, a menstrual cycle sensing system may detect ovulation based on measured respiratory rate. A low respiratory rate may indicate ovulation.

[0118] Based on menstrual cycle-related biomarkers, menstrual cycle sensing systems can determine conditions including hormonal changes, surgical bleeding, scarring, bleeding risk, and / or sensitivity levels. For example, menstrual cycle stages may affect surgical bleeding in rhinoplasty. For example, menstrual cycle stages may affect healing and scarring in chest surgery. For example, bleeding risk may decrease during the ovulation phase of the menstrual cycle.

[0119] In the embodiments, the detection, prediction, and / or determination described herein may be performed by a computing system based on measurement data and / or associated biomarkers generated by a menstrual cycle sensing system. The menstrual cycle sensing system may process the menstrual cycle data locally or transmit the data to a processing unit.

[0120] The environmental sensing system may measure environmental data including ambient temperature, humidity, mycotoxin spore count, and airborne chemical data. The environmental sensing system may include a digital thermometer, air sampling, and / or chemical sensors. The sensing system may include wearable devices. The environmental sensing system may use a digital thermometer to measure ambient temperature and / or humidity. The digital thermometer may include a metal strip with a determined resistance. The resistance of the metal strip may vary with the ambient temperature. The digital thermometer may apply the varied resistance to a calibration curve to determine the temperature. The digital thermometer may include a wet-bulb and a dry-bulb thermometer. The wet-bulb and dry-bulb thermometers may determine the temperature difference, which can then be used to calculate humidity.

[0121] The environmental sensing system may use air sampling to measure the number of mycotoxin spores. The environmental sensing system may include a sampling plate with an adhesive medium connected to a pump. The pump may draw air onto the plate at a specific flow rate for a set time. The set time may last up to 10 minutes. The environmental sensing system may analyze the sample using a microscope to count the number of spores. The environmental sensing system may use different air sampling techniques, including high-performance liquid chromatography (HPLC), liquid chromatography-tandem mass spectrometry (LC-MS / MS), and / or immunoassays and nanobody sampling.

[0122] The environmental sensing system may include a chemical sensor for measuring airborne chemical data. Airborne chemical data may include different identified airborne chemicals, including nicotine and / or formaldehyde. The chemical sensor may include an active layer and a transducer layer. The active layer may diffuse chemicals into the matrix, altering several physical or chemical properties. Altered physical properties may include refractive index and / or H-bond formation. The transducer layer may convert the physical and / or chemical variations into measurable signals, including optical or electrical signals. The environmental sensing system may include a handheld device. The handheld device may detect and identify complex chemical mixtures constituting aromas, odors, fragrances, formulations, spills, and / or leaks. The handheld device may include an array of nanocomposite sensors. The handheld device may detect and identify substances based on their chemical profiles.

[0123] Based on environmental data, the sensing system can determine environmental information including climate, mycotoxin spore count, mycotoxin identification, airborne chemical identification, airborne chemical levels, and / or inhalation of inflammatory chemicals. For example, the environmental sensing system may estimate the number of mycotoxin spores in the air based on the number of spores measured from collected samples. The sensing system may identify mycotoxin spores, which may include mold, pollen, insect parts, skin cell fragments, fibers, and / or inorganic particulate matter. For example, the sensing system may detect the inhalation of inflammatory chemicals, including cigarette smoke. The sensing system may detect secondhand or thirdhand smoke.

[0124] Based on environmental information, the sensing system may generate environmental conditional states, including inflammation, decreased lung function, airway hyperresponsiveness, fibrosis, and / or decreased immune function. For example, an environmental conditional sensing system may detect inflammation and fibrosis based on measured environmental conditional information. Based on inflammation and / or fibrosis, the sensing system may generate instructions for surgical tools, including staples and sealing tools used in partial lung resection. Inflammation and fibrosis can affect the use of surgical tools. For example, cigarette smoke can cause higher pain scores in various surgeries.

[0125] The environmental sensing system may generate an air quality score based on measured mycotoxins and / or airborne chemicals. For example, the environmental sensing system may notify of hazardous air quality if it detects a low air quality score. The environmental sensing system may send a notification when the generated air quality score falls below a certain threshold. The threshold may include mycotoxin exposure exceeding 10⁵ spores per cubic meter. The environmental sensing system may display readouts of environmental condition exposure over time.

[0126] The environmental sensing system may process environmental data locally or transmit the data to a processing unit. In the embodiments, the detection, prediction, and / or determination described herein may be performed by a computing system based on the measurement data generated by the environmental sensing system.

[0127] A light exposure sensing system can measure light exposure data. A light exposure sensing system may include one or more photodiode photosensors. For example, a light exposure sensing system using a photodiode photosensor may include a semiconductor device in which the device current can vary as a function of light intensity. Incident photons can generate electron-hole pairs flowing across a semiconductor junction, which can generate a current. The rate of electron-hole pair generation can increase as a function of the intensity of the incident light. A light exposure sensing system may include one or more photoresistor photosensors. For example, a light exposure sensing system using a photoresistor photosensor may include a light-dependent resistor whose resistance decreases as a function of light intensity. A photoresistor photosensor may include a passive device without a PN junction. A photoresistor photosensor may have lower sensitivity than a photodiode photosensor. A light exposure sensing system may include a wearable, including a necklace and / or clip-on button.

[0128] Based on the measured light exposure data, the light exposure sensing system may detect light exposure information, including the duration of light exposure, the intensity of light exposure, and / or the type of light. For example, the sensing system may determine whether the light exposure consists of natural or artificial light. Based on the detected light exposure information, the light exposure sensing system may detect light exposure-related biomarkers, including circadian rhythms. Light exposure can synchronize circadian cycles.

[0129] The light exposure sensing system may process the light exposure data locally or transmit the data to a processing unit. In the embodiments, the detection, prediction, and / or determination described herein may be performed by a computing system based on the measurement data and / or associated biomarkers generated by the light exposure sensing system.

[0130] Various sensing systems described herein can measure data, derive relevant biomarkers, and transmit the biomarkers to a computing system, such as a surgical hub as described herein with reference to Figures 1 to 12. Various sensing systems described herein can transmit measured data to a computing system. The computing system can derive relevant biomarkers based on the received measurement data.

[0131] A biomarker sensing system may include a wearable device. In an embodiment, the biomarker sensing system may include eyeglasses. The eyeglasses may include a nose pad sensor. The eyeglasses may measure biomarkers including lactate, glucose, and / or equivalents. In an embodiment, the biomarker sensing system may include a mouthguard. The mouthguard may include a sensor for measuring biomarkers including uric acid and / or equivalents. In an embodiment, the biomarker sensing system may include a contact lens. The contact lens may include a sensor for measuring biomarkers including glucose and / or equivalents. In an embodiment, the biomarker sensing system may include a tooth sensor. The tooth sensor may be graphene-based. The tooth sensor may measure biomarkers including bacteria and / or equivalents. In an embodiment, the biomarker sensing system may include a patch. The patch may be attachable to the skin of the chest or arm. For example, the patch may include a chemical-physical hybrid sensor. The chemical-physical hybrid sensor may measure biomarkers including lactate, ECG, and / or equivalents. For example, the patch may include nanomaterials. The nanomaterial patch may measure biomarkers containing glucose and / or equivalents. For example, the patch may include an iontophoresis biosensor. The iontophoresis biosensor may measure biomarkers containing glucose and / or equivalents. In an embodiment, the biomarker sensing system may include a microfluidic sensor. The microfluidic sensor may measure biomarkers containing lactate, glucose, and / or equivalents. In an embodiment, the biomarker sensing system may include an integrated sensor array. The integrated sensory array may include a wearable wristband. The integrated sensory array may measure biomarkers containing lactate, glucose, and / or equivalents. In an embodiment, the biomarker sensing system may include a wearable diagnostic device. The wearable diagnostic device may measure biomarkers containing cortisol, interleukin-6, and / or equivalents. In an embodiment, the biomarker sensing system may include a powered textile-based biosensor. The powered textile-based biosensor may include a sock.A power-integrated textile-based biosensor can measure biomarkers containing lactates and / or equivalents.

[0132] The various biomarkers described herein may be associated with a variety of physiological systems, including behavior and psychology, the cardiovascular system, the renal system, the cutaneous system, the nervous system, the GI system, the respiratory system, the endocrine system, the immune system, tumors, the musculoskeletal system, and / or the reproductive system.

[0133] Behavior and psychology may include social interactions, diet, sleep, activity, and / or psychological status. Behavior and psychology-related biomarkers, complications, contextual information, and / or status may be determined and / or predicted based on analyzed biomarker sensing system data. A computing system may select one or more biomarkers (e.g., data from a biomarker sensing system) from behavior and psychology-related biomarkers, including sleep, circadian rhythms, physical activity, and / or mental aspects, for analysis, as described herein. Behavior and psychology scores may be generated based on the analyzed biomarkers, complications, contextual information, and / or status. Behavior and psychology scores may include scores for social interactions, diet, sleep, activity, and / or psychological status.

[0134] For example, based on selected biomarker sensing system data, sleep-related biomarkers, complications, and / or contextual information, including sleep quality, sleep duration, sleep timing, immune function, and / or postoperative pain, can be determined. Based on selected biomarker sensing system data, sleep-related conditions, including inflammation, can be predicted. In an embodiment, inflammation can be predicted based on analyzed preoperative sleep. Increased inflammation can be determined and / or predicted based on disturbances in preoperative sleep. In an embodiment, immune function can be determined based on analyzed preoperative sleep. Decreased immune function can be predicted based on disturbances in preoperative sleep. In an embodiment, postoperative pain can be determined based on analyzed sleep. Postoperative pain can be determined and / or predicted based on disturbances in sleep. In an embodiment, pain and discomfort can be determined based on analyzed circadian rhythms. Decreased immune system can be determined based on analyzed circadian rhythm period disturbances.

[0135] For example, based on selected biomarker sensing system data, activity-related biomarkers, complications, and / or contextual information may be determined, including activity duration, activity intensity, activity type, activity pattern, recovery time, mental health, physical recovery, immune function, and / or inflammatory function. Based on selected biomarker sensing system data, activity-related states may be predicted. In the embodiment, improvements in physiological function may be determined based on the analyzed activity intensity. Moderate-intensity exercise may indicate shorter hospital stays, improved mental health, improved physical recovery, improved immune function, and / or improved inflammatory function. Physical activity types may include aerobic and / or non-aerobic activities. Aerobic physical activity may be determined based on analyzed physical activity, including running, cycling, and / or weight training. Non-aerobic physical activity may be determined based on analyzed physical activity, including walking and / or stretching.

[0136] For example, based on selected biomarker sensing system data, psychological status-related biomarkers, complications, and / or contextual information, including stress, anxiety, pain, positive emotions, abnormal states, and / or postoperative pain, may be determined. Based on selected biomarker sensing system data, psychological status-related states, including physical symptoms of disease, may be predicted. Higher postoperative pain may be determined and / or predicted based on analyzed high levels of preoperative stress, anxiety, and / or pain. Physical symptoms of disease may be predicted based on determined high optimism.

[0137] The detection, prediction, determination, and / or generation described herein may be performed by a computing system described herein, such as a surgical hub, a computing device, and / or a smart device, based on measured data and / or relevant biomarkers generated by a biomarker sensing system.

[0138] The cardiovascular system may include the lymphatic system, blood vessels, blood, and / or the heart. Cardiovascular-related biomarkers, complications, contextual information, and / or conditions may be determined and / or predicted based on analyzed biomarker sensing system data. Systemic circulatory status may include the state of the lymphatic system, blood vessels, and / or blood. The computing system may select one or more biomarkers (e.g., data from a biomarker sensing system) from cardiovascular-related biomarkers, including blood pressure, VO2 max, hydration status, oxygen saturation, blood pH, sweat, core temperature, peripheral temperature, edema, heart rate, and / or heart rate variability, for analysis.

[0139] For example, based on selected biomarker sensing system data, lymphoid-related biomarkers, complications, and / or contextual information, including swelling, lymphatic composition, and / or collagen deposition, may be determined. Based on selected biomarker sensing system data, lymphoid-related conditions, including fibrosis, inflammation, and / or postoperative infection, may be predicted. Inflammation may be predicted based on determined swelling. Postoperative infection may be predicted based on determined swelling. Collagen deposition may be determined based on predicted fibrosis. Increased collagen deposition may be predicted based on fibrosis. Harmonic tool parameter adjustments may be generated based on the determined increase in collagen deposition. Inflammatory states may be predicted based on analyzed lymphatic composition. Different inflammatory states may be determined and / or predicted based on changes in lymphopeptide composition. Metastatic cell spread may be predicted based on the predicted inflammatory state. Harmonic tool parameter adjustments and margin determinations may be generated based on the predicted inflammatory state.

[0140] For example, based on selected biomarker sensing system data, vascular-related biomarkers, complications, and / or contextual information, including permeability, vasomotor function, pressure, structure, healing capacity, harmonic sealing performance, and / or cardiothoracic health fitness, may be determined. Recommendations for surgical tool use and / or parameter adjustments may be generated based on the determined vascular-related biomarkers. Based on selected biomarker sensing system data, vascular-related conditions, including infection, anastomotic leakage, septic shock, and / or hypovolemic shock, may be predicted. In the example, increased vascular permeability may be determined based on analyzed edema, bradykinin, histamine, and / or endothelial adhesion molecules. Endothelial adhesion molecules may be measured using cell samples to measure transmembrane proteins. In the example, vasomotor function may be determined based on selected biomarker sensing system data. Vasomotor function may include vasodilators and / or vasoconstrictors. In the example, shock may be predicted based on determined blood pressure-related biomarkers, including vascular information and / or vascular distribution. Individual vascular structures may include arterial stiffness, collagen content, and / or vascular diameter. Cardiothoracic health fitness may be determined based on VO2 maxima. A higher risk of complications may be determined and / or predicted based on a lower VO2 maxima.

[0141] For example, based on selected biomarker sensing system data, blood-related biomarkers, complications, and / or contextual information, including volume, oxygen, pH, waste products, temperature, hormones, proteins, and / or nutrients, may be determined. Based on selected biomarker sensing system data, blood-related complications and / or contextual information, including cardiothoracic health fitness, lung function, recovery capacity, anaerobic threshold, oxygen intake, carbon dioxide (CO2) production, fitness, tissue oxygenation, colloid osmotic pressure, and / or blood coagulation capacity, may be determined. Based on the derived blood-related biomarkers, blood-related conditions, including postoperative acute kidney injury, hypovolemic shock, acidosis, sepsis, pulmonary collapse, bleeding, bleeding risk, infection, and / or anastomotic leakage, may be predicted.

[0142] For example, postoperative acute renal injury and / or hypovolemic shock can be predicted based on hydration status. For example, lung function, lung recovery capacity, cardiothoracic health fitness, anaerobic threshold, oxygen consumption, and / or CO2 production can be predicted based on cardiothoracic biomarkers, including red blood cell count and / or oxygen saturation. For example, cardiovascular complications can be predicted based on blood-related biomarkers, including red blood cell count and / or oxygen saturation. For example, acidosis can be predicted based on pH. Based on acidosis, blood-related conditions can be indicated, including sepsis, lung collapse, bleeding, and / or increased bleeding risk. For example, blood-related biomarkers, including tissue oxygenation, can be derived based on sweat. Low tissue oxygenation can be predicted based on high lactate concentration. Based on low tissue oxygenation, blood-related conditions can be predicted, including hypovolemic shock, septic shock, and / or left ventricular failure. For example, blood temperature-related biomarkers, including menstrual cycle and / or basal body temperature, can be derived based on temperature. Based on blood temperature-related biomarkers, blood temperature-related conditions, including sepsis and / or infection, can be predicted. For example, colloidal osmotic pressure can be determined based on proteins, including albumin content. Based on colloidal osmotic pressure, blood protein-related conditions, including edema risk and / or anastomotic leakage, can be predicted. Increased edema risk and / or anastomotic leakage can be predicted based on low colloidal osmotic pressure. Bleeding risk can be predicted based on blood coagulation capacity. Blood coagulation capacity can be determined based on fibrinogen content. Decreased blood coagulation capacity can be determined based on low fibrinogen content.

[0143] For example, based on selected biomarker sensing system data, a computing system may derive cardiac-related biomarkers, complications, and / or contextual information, including cardiac activity, cardiac anatomy, recovery rate, cardiothoracic health fitness, and / or risk of complications. Cardiac activity biomarkers may include electrical activity and / or stroke volume. Recovery rate may be determined based on heart rate biomarkers. Reduced blood supply to the body may be determined and / or predicted based on irregular heart rate. Delayed recovery may be determined and / or predicted based on reduced blood supply to the body. Cardiothoracic health fitness may be determined based on analyzed maximum VO2 values. Maximum VO2 values ​​below a certain threshold may indicate low cardiothoracic health fitness. Maximum VO2 values ​​below a certain threshold may indicate a higher risk of cardiac-related complications.

[0144] The detection, prediction, determination, and / or generation described herein may be performed by a computing system described herein, such as a surgical hub, a computing device, and / or a smart device, based on measurement data and / or associated biomarkers generated by a biomarker sensing system.

[0145] Kidney-related biomarkers, complications, contextual information, and / or conditions can be determined and / or predicted based on analyzed biomarker sensing system data. A computing system may select one or more biomarkers (e.g., data from a biomarker sensing system) from the kidney-related biomarkers for analysis, as described herein. Based on the selected biomarker sensing system data, kidney-related biomarkers, complications, and / or contextual information, including the ureters, urethra, bladder, kidneys, general urinary tract, and / or ureteral fragility, can be determined. Based on the selected biomarker sensing system data, kidney-related conditions, including acute kidney injury, infection, and / or kidney stones, can be predicted. In an embodiment, ureteral fragility can be determined based on urinary inflammation parameters. In an embodiment, acute kidney injury can be predicted based on analyzed Kidney Injury Molecule-1 (KIM-1) in the urine.

[0146] The detection, prediction, determination, and / or generation described herein may be performed by a computing system described herein, such as a surgical hub, a computing device, and / or a smart device, based on measured data and / or relevant biomarkers generated by a biomarker sensing system.

[0147] The skin system may include biomarkers related to the microbiome, skin, nails, hair, sweat, and / or sebum. Skin-related biomarkers may include epidermal biomarkers and / or dermal biomarkers. Sweat-related biomarkers may include activity biomarkers and / or compositional biomarkers. Skin system-related biomarkers, complications, contextual information, and / or conditions may be determined and / or predicted based on analyzed biomarker sensing system data. The computing system may select one or more biomarkers (e.g., data from a biomarker sensing system) from skin-related biomarkers, including skin conductance, skin perfusion pressure, sweat, autonomic tension, and / or pH, for analysis, as described herein.

[0148] For example, based on selected biomarker sensing system data, skin-related biomarkers, complications, and / or contextual information, including color, lesions, transepidermal water loss, sympathetic nervous system activity, elasticity, tissue perfusion, and / or mechanical properties, can be determined. Stress can be predicted based on determined skin conductance. Skin conductance can act as a surrogate for sympathetic nervous system activity. Sympathetic nervous system activity can correlate with stress. Tissue mechanical properties can be determined based on skin perfusion pressure. Skin perfusion pressure can indicate deep tissue perfusion. Deep tissue perfusion can determine tissue mechanical properties. Surgical tool parameter adjustments can be generated based on determined tissue mechanical properties.

[0149] Based on selected biomarker sensing system data, skin-related conditions can be predicted.

[0150] For example, based on selected biomarker sensing system data, sweat-related biomarkers, complications, and / or contextual information, including activity, composition, autonomic nervous system tension, stress response, inflammatory response, blood pH, vascular health, immune function, circadian rhythm, and / or blood lactate concentration, may be determined. Based on selected biomarker sensing system data, sweat-related conditions, including intestinal obstruction, cystic fibrosis, diabetes, metastasis, cardiac disease, and / or infections, may be predicted.

[0151] For example, sweat composition-related biomarkers can be determined based on selected biomarker data. Sweat composition biomarkers may include proteins, electrolytes, and / or small molecules. Based on sweat composition biomarkers, skin complications, conditions, and / or contextual information can be predicted, including intestinal obstruction, cystic fibrosis, acidosis, sepsis, lung collapse, bleeding, bleeding risk, diabetes, metastasis, and / or infection. For example, stress response can be predicted based on protein biomarkers, including sweat neuropeptide Y and / or sweat antibacterial agents. Higher sweat neuropeptide Y levels may indicate a greater stress response. Cystic fibrosis and / or acidosis can be predicted based on electrolyte biomarkers, including chloride ions, pH, and other electrolytes. High lactate concentrations can be determined based on blood pH. Acidosis can be predicted based on high lactate concentrations. Sepsis, lung collapse, bleeding, and / or bleeding risk can be predicted based on predicted acidosis. Diabetes, metastasis, and / or infection can be predicted based on small molecule biomarkers. Small molecule biomarkers may include blood glucose and / or hormones. Hormonal biomarkers may include adrenaline and / or cortisol. Vascular health can be determined based on predicted metastases. Infection due to weakened immune function can be predicted based on detected cortisol. Weakened immune function can be determined and / or predicted based on high cortisol levels. Sweat-related conditions, including stress responses, inflammatory responses, and / or intestinal obstruction, can be predicted based on determined autonomic tension. Greater stress responses, greater inflammatory responses, and / or intestinal obstruction can be determined and / or predicted based on high sympathetic tension.

[0152] The detection, prediction, determination, and / or generation described herein may be performed by a computing system described herein, such as a surgical hub, a computing device, and / or a smart device, based on measured data and / or relevant biomarkers generated by a biomarker sensing system.

[0153] Nervous system-related biomarkers, complications, contextual information, and / or conditions may be determined and / or predicted based on analyzed biomarker sensing system data. The computing system may select one or more biomarkers (e.g., data from a biomarker sensing system) for analysis from nervous system-related biomarkers, including circadian rhythms, oxygen saturation, autonomic tension, sleep, activity, and / or mental states, as described herein. The nervous system may include the central nervous system (CNS) and / or the peripheral nervous system. The CNS may include the brain and / or spinal cord. The peripheral nervous system may include the autonomic nervous system, motor system, gut system, and / or sensory system.

[0154] For example, based on selected biomarker sensing system data, CNS-related biomarkers, complications, and / or contextual information, including postoperative pain, immune function, mental health, and / or recovery rate, may be determined. Based on selected biomarker sensing system data, CNS-related conditions, including inflammation, delirium, sepsis, hyperactivity, hypoactivity, and / or physical symptoms of disease, may be predicted. In an example, a decreased immune system and / or a high pain score may be predicted based on sleep disturbance. In an example, postoperative delirium may be predicted based on oxygen saturation. Cerebral oxygenation may indicate postoperative delirium.

[0155] For example, based on selected biomarker sensing system data, peripheral nervous system-related biomarkers, complications, and / or contextual information may be determined. Based on selected biomarker sensing system data, peripheral nervous system-related conditions, including inflammation and / or intestinal obstruction, may be predicted. In the embodiment, high sympathetic nervous system tension may be predicted based on autonomic nervous system tension. A greater stress response may be predicted based on high sympathetic nervous system tension. Inflammation and / or intestinal obstruction may be predicted based on high sympathetic nervous system tension.

[0156] The detection, prediction, determination, and / or generation described herein may be performed by a computing system described herein, such as a surgical hub, a computing device, and / or a smart device, based on measured data and / or relevant biomarkers generated by a biomarker sensing system.

[0157] The GI system may include the upper GI duct, lower GI duct, accessory organs, peritoneal space, nutritional status, and microbiome. The upper GI may include the mouth, esophagus, and / or stomach. The lower GI may include the small intestine, colon, and / or rectum. Accessory organs may include the pancreas, liver, spleen, and / or gallbladder. The peritoneal space may include the mesentery and / or adipose vessels. Nutritional status may include short-term, long-term, and / or systemic. GI-related biomarkers, complications, contextual information, and / or status may be determined and / or predicted based on analyzed biomarker sensing system data. The computing system may select one or more biomarkers (e.g., data from a biomarker sensing system) from GI-related biomarkers, including cough and sneeze, respiratory bacteria, GI duct imaging / sensing, GI motility, pH, tissue perfusion pressure, environment, and / or alcohol consumption, for analysis, as described herein.

[0158] The upper GI may include the mouth, esophagus, and / or stomach. For example, based on selected biomarker sensing system data, mouth and esophagus-related biomarkers, complications, and / or contextual information may be determined, including gastric tissue characteristics, esophageal motility, colonic tissue changes, bacterial presence, tumor size, tumor location, and / or tumor tension. Based on selected biomarker sensing system data, mouth and esophagus-related conditions may be predicted, including inflammation, surgical site infection (SSI), and / or gastroesophageal disease. The mouth and esophagus may include mucosa, muscular layer, lumen, and / or mechanical properties. Luminous biomarkers may include luminal contents, luminal microbiota, and / or lumen size. In the examples, inflammation may be predicted based on analyzed cough biomarkers. Gastroesophageal reflux disease may be predicted based on inflammation. Gastric tissue characteristics may be predicted based on gastroesophageal disease. In the examples, esophageal motility may be determined based on collagen content and / or muscularis lamina function. In the examples, changes in colonic tissue may be indicated based on salivary cytokines. Inflammatory bowel disease (IBD) may be predicted based on changes in colonic tissue. Salivary cytokines may be increased in IBD. Surgical site infection (SSI) may be predicted based on analyzed bacteria. Bacteria may be identified based on analyzed bacteria. Respiratory pathogens in the oral cavity may indicate the possibility of SSI. Surgical tool parameter adjustments may be generated based on lumen size and / or location. Surgical tool parameter adjustments may include staple sizing, surgical tool fixation, and / or surgical tool approach. In the examples, surgical tool parameter adjustments for the use of auxiliary materials may be generated based on mechanical properties, including elasticity, to minimize tissue tension. Additional activating parameter adjustments may be generated based on analyzed mechanical properties to minimize tissue tension.

[0159] For example, based on selected biomarker sensing system data, gastric-related biomarkers, complications, and / or contextual information may be determined, including tissue strength, tissue thickness, recovery rate, lumen location, lumen shape, pancreatic function, presence of food in the stomach, gastric water content, gastric tissue thickness, gastric tissue shear strength, and / or gastric tissue elasticity. Based on selected biomarker sensing system data, gastric-related conditions, including ulcers, inflammation, and / or gastroesophageal reflux disease, may be predicted. The stomach may include mucosa, muscular layer, serosal membrane, lumen, and mechanical properties. Gastric-related conditions, including ulcers, inflammation, and / or gastroesophageal disease, may be predicted based on analyzed cough and / or GI tube imaging. Gastric tissue properties may be determined based on gastroesophageal reflux disease. Ulcers may be predicted based on analyzed Helicobacter pylori. Mechanical properties of gastric tissue may be determined based on GI tube imaging. Surgical tool parameter adjustments may be generated based on the determined mechanical properties of gastric tissue. The risk of postoperative leakage can be predicted based on the mechanical properties of the determined gastric tissue. In the examples, important components of tissue strength and / or thickness can be determined based on the analyzed collagen content. Important components of tissue strength and thickness can affect recovery. In the examples, blood supply and / or blood location can be determined based on serosal biomarkers. In the examples, biomarkers including capsule size, capsule volume, capsule location, pancreatic function, and / or the presence of food can be determined based on the analyzed luminal biomarkers. Luminal biomarkers may include luminal location, luminal shape, gastric emptying rate, and / or luminal contents. Capsule size can be determined based on the capsule's start and end locations. Gastric emptying rate can be determined based on GI motility. Pancreatic function can be determined based on gastric emptying rate. Luminal contents can be determined based on the analyzed gastric pH. Luminal contents may include the presence of food in the stomach. For example, the presence of solid food can be determined based on gastric pH fluctuations. Low stomach pH can be predicted based on an empty stomach. Basic stomach pH can be determined based on feeding. Buffering by food can result in basic stomach pH. Stomach pH can increase based on gastric acid secretion. Stomach pH can return to a low value when the buffering capacity of food is exceeded. Intraluminal pH sensors can detect feeding.For example, gastric water content, tissue thickness, tissue shear strength, and / or tissue elasticity can be determined based on tissue perfusion pressure. The mechanical properties of the stomach can be determined based on gastric water content. Surgical tool parameter adjustments can be generated based on the mechanical properties of the stomach. Surgical tool parameter adjustments can be generated based on the main components of tissue strength and / or fragmentation. Postoperative leakage can be predicted based on the main components of tissue strength and / or fragmentation.

[0160] The lower GI may include the small intestine, colon, and / or rectum. For example, based on selected biomarker sensing system data, small intestine-related biomarkers, complications, contextual information, and / or conditions may be determined, including calorie absorption rate, nutrient absorption rate, bacterial presence, and / or recovery rate. Based on selected biomarker sensing system data, small intestine-related conditions, including intestinal obstruction and / or inflammation, may be predicted. Small intestine biomarkers may include the muscular layer, serosa, lumen, mucosa, and / or mechanical properties. For example, postoperative small intestinal motility changes may be determined based on GI motility. Intestinal obstruction may be predicted based on postoperative small intestinal motility changes. GI motility may determine calorie and / or nutrient absorption rates. Future weight loss may be predicted based on accelerated absorption rates. Absorption rates may be determined based on fecal velocity, composition, and / or pH. Inflammation may be predicted based on luminal contents biomarkers. Luminal contents biomarkers may include pH, bacterial presence, and / or bacterial quantity. Mechanical properties can be determined based on the predicted inflammation. Mucosal inflammation can be predicted based on fecal inflammation markers. Fecal inflammation markers may include calprotectin. Tissue property changes can be determined based on mucosal inflammation. Changes in recovery rate can be determined based on mucosal inflammation.

[0161] For example, based on selected biomarker sensing system data, colon and rectum-related biomarkers, complications, and / or contextual information can be determined, including small intestinal tissue strength, small intestinal tissue thickness, contractility, water content, colon and rectal tissue perfusion pressure, colon and rectal tissue thickness, colon and rectal tissue strength, and / or colon and rectal tissue fragility. Based on selected biomarker sensing system data, colon and rectum-related conditions can be predicted, including inflammation, anastomotic leakage, ulcerative colitis, Crohn's disease, and / or infection. The colon and rectum may include mucosa, muscular layer, serosal membrane, lumen, function, and / or mechanical properties. In the embodiment, mucosal inflammation can be predicted based on fecal inflammation markers. Fecal inflammation markers may include calprotectin. An increased risk of anastomotic leakage can be determined based on inflammation.

[0162] Surgical tool parameter adjustments may be made based on the assessed increase in the risk of anastomotic leakage. Inflammatory states may be predicted based on GI tube imaging. Inflammatory states may include ulcerative colitis and / or Crohn's disease. Inflammation may increase the risk of anastomotic leakage. Surgical tool parameter adjustments may be made based on inflammation. In the examples, important components of tissue strength and / or thickness may be determined based on collagen content. In the examples, colonic contractility may be determined based on smooth muscle alpha-actin expression. In the examples, the inability of a colonic region to contract may be determined based on abnormal expression. Colonic inability to contract may be determined and / or predicted based on pseudo-obstruction and / or bowel obstruction. In the examples, adhesions, fistulas, and / or scar tissue may be predicted based on serosal biomarkers. Colonic infection may be predicted based on the presence of bacteria in the stool. Bacteria in the stool may be identified. Bacteria may include symbionts and / or pathogens. In the examples, the inflammatory state can be predicted based on pH. Mechanical properties can be determined based on the inflammatory state. Enteritis can be predicted based on ingested allergens. Consistent exposure to ingested allergens can increase enteritis. Enteritis can alter mechanical properties. In the examples, mechanical properties can be determined based on tissue perfusion pressure. Water content can be determined based on tissue perfusion pressure. Surgical tool parameter adjustments can be made based on the determined mechanical properties.

[0163] Assistive organs may include the pancreas, liver, spleen, and / or gallbladder. Based on selected biomarker sensing system data, assistive organ-related biomarkers, complications, and / or contextual information may be determined, including gastric emptying rate, liver size, liver shape, liver location, tissue health, and / or blood loss response. Based on selected biomarker sensing system data, assistive organ-related conditions, including gastroparesis, may be predicted. For example, gastric emptying rate may be determined based on enzyme loading and / or titrable base biomarkers. Gastrotroparesis may be predicted based on gastric emptying rate. Lymphoid tissue health may be determined based on lymphocyte storage status. A patient's ability to respond to SSI may be determined based on lymphoid tissue health. Venous sinus tissue health may be determined based on red blood cell storage status. A patient's response to blood loss in surgery may be predicted based on venous sinus tissue health.

[0164] Nutritional status may include short-term nutrition, long-term nutrition, and / or systemic nutrition. Based on selected biomarker sensing system data, nutritional status-related biomarkers, complications, and / or contextual information, including immune function, may be determined. Based on selected biomarker sensing system data, nutritional status-related conditions, including cardiac problems, may be predicted. Reduced immune function may be determined based on nutritional biomarkers. Cardiac problems may be predicted based on nutrient biomarkers. Nutrient biomarkers may include macronutrients, micronutrients, alcohol consumption, and / or feeding patterns.

[0165] Patients who have undergone gastric bypass surgery may have an altered gut microbiome, which can be measured in stool samples.

[0166] The detection, prediction, determination, and / or generation described herein may be performed by a computing system described herein, such as a surgical hub, a computing device, and / or a smart device, based on measured data and / or relevant biomarkers generated by a biomarker sensing system.

[0167] The respiratory system may include the upper respiratory tract, lower respiratory tract, respiratory muscles, and / or the contents of the system. The upper respiratory tract may include the pharynx, larynx, mouth and oral cavity, and / or nose. The lower respiratory tract may include the trachea, bronchi, alveoli, and / or lungs. The respiratory muscles may include the diaphragmatic muscles and / or intercostal muscles. Respiratory system-related biomarkers, complications, contextual information, and / or conditions may be determined and / or predicted based on analyzed biomarker sensing system data. The computing system may select one or more biomarkers (e.g., data from a biomarker sensing system) from respiratory system-related biomarkers, including bacteria, cough and sneeze, respiratory rate, VO2 max, and / or activity for analysis, as described herein.

[0168] The upper respiratory tract may include the pharynx, larynx, mouth and oral cavity, and / or nose. For example, upper respiratory tract-related biomarkers, complications, and / or contextual information may be determined based on selected biomarker sensing system data. Upper respiratory tract-related conditions, including SSI, inflammation, and / or allergic rhinitis, may be predicted based on selected biomarker sensing system data. In the embodiment, SSI may be predicted based on bacterial and / or tissue biomarkers. Bacterial biomarkers may include symbionts and / or pathogens. Inflammation may be indicated based on tissue biomarkers. Mucosal inflammation may be predicted based on nasal biomarkers, including cough and sneeze. General inflammation and / or allergic rhinitis may be predicted based on mucosal biomarkers. Mechanical properties of various tissues may be determined based on systemic inflammation.

[0169] The lower respiratory tract may include the trachea, bronchi, alveoli, and / or lungs. For example, based on selected biomarker sensing system data, lower respiratory tract-related biomarkers, complications, and / or contextual information, including bronchopulmonary segments, may be determined. Based on selected biomarker sensing system data, lower respiratory tract-related conditions may be predicted. Surgical tool parameter adjustments may be generated based on the determined biomarkers, complications, and / or contextual information. Surgical tool parameter adjustments may be generated based on the predicted conditions.

[0170] Based on selected biomarker sensing system data, lung-related biomarkers, complications, and / or contextual information, including low surgical resistance, can be determined. Lung-related biomarkers may include lung respiratory mechanisms, lung diseases, lung surgery, mechanical properties of the lung, and / or lung function. The pulmonary respiratory mechanism includes total lung capacity (TLC), tidal volume (TV), residual volume (RV), expiratory reserve volume (ERV), inspiratory reserve volume (IRV), inspiratory capacity (IC), inspiratory vital capacity (IVC), vital capacity (VC), functional residual capacity (FRC), residual volume expressed as a percentage of total lung capacity (RV / TLC%), alveolar gas volume (VA), lung volume (VL), forced vital capacity (FVC), and forced expiratory volume over time. This may include time (FEVt), the difference between inspiratory and expiratory volume (DLco), the volume exhaled after the first second of forced exhalation (FEV1), forced expiratory flow rate (FEFx) related to a portion of the functional residual capacity curve, maximum instantaneous flow rate in functional residual capacity (FEFmax), forced inspiratory flow rate (FIF), peak flow meter (PEF) measured by a peak flow meter, and maximal voluntary ventilation (MVV).

[0171] TLC can be determined based on lung volume at maximum expansion. TV can be determined based on the volume of air entering and leaving the lungs during resting respiration. RV can be determined based on the volume of air remaining in the lungs after maximum exhalation. ERV can be determined based on the maximum volume inhaled from the end-spiratory level. IC can be determined based on aggregated IRV and TV values. IVC can be determined based on the maximum volume of air inhaled at maximum exhalation. VC can be determined based on the difference between the RV value and the TLC value. FRC can be determined based on lung volume at the end-expiratory position. FVC can be determined based on the VC value during maximal forced expiratory activity. Low surgical tolerance can be determined based on the difference between inhaled and exhaled carbon monoxide, for example, when the difference is less than 60%. Low surgical tolerance can also be determined based on the volume exhaled at the end of the first second of forced exhalation, for example, when the volume is less than 35%. MVV can be determined based on the volume of air exhaled during a specified period of repeated maximum activity.

[0172] Based on selected biomarker sensing system data, lung-related conditions, including emphysema, chronic obstructive pulmonary disease, chronic bronchitis, asthma, cancer, and / or tuberculosis, can be predicted. Lung diseases can be predicted based on analyzed vital capacity measurements, X-rays, blood gases, and / or the diffusion capacity of the alveolar capillary membrane. Lung diseases can narrow the airways and / or cause airway resistance. Lung cancer and / or tuberculosis can be detected based on lung-related biomarkers, including persistent cough, hemoptysis, shortness of breath, chest pain, hoarseness, unintentional weight loss, bone pain, and / or headache. Tuberculosis can be predicted based on lung symptoms, including cough for 3-5 weeks, cough blood, chest pain, pain during breathing or coughing, unintentional weight loss, fatigue, fever, night sweats, chills, and / or loss of appetite.

[0173] Surgical tool parameter adjustments and surgical procedure adjustments may be generated based on lung-related biomarkers, complications, contextual information, and / or status. Surgical procedure adjustments may include lung resection, lobectomy, and / or sublocal resection. In embodiments, surgical procedure adjustments may be generated based on a cost-benefit analysis between appropriate resection and the physiological impact on the patient's ability to recover functional status. Surgical tool parameter adjustments may be generated based on determined surgical tolerance. Surgical tolerance may be determined based on FEV1 values. Surgical tolerance may be considered appropriate when FEV1 exceeds a certain threshold, which may include values ​​greater than 35%. Postoperative surgical procedure adjustments, including oxygenation and / or physical therapy, may be generated based on determined pain scores. Postoperative surgical procedure adjustments may be generated based on air leakage. Air leakage may increase costs associated with postoperative recovery and morbidity after lung surgery.

[0174] Lung mechanical property-related biomarkers may include perfusion, tissue integrity, and / or collagen content. Pleural perfusion pressure may be determined based on lung water content levels. Tissue mechanical properties may be determined based on pleural perfusion pressure. Surgical tool parameter adjustments may be generated based on pleural perfusion pressure. Lung tissue integrity may be determined based on elasticity, hydrogen peroxide (H2O2) in exhaled air, lung tissue thickness, and / or lung tissue shear strength. Tissue fragmentation may be determined based on elasticity. Surgical tool parameter adjustments may be generated based on postoperative leakage. Postoperative leakage may be predicted based on elasticity. In the examples, fibrosis may be predicted based on exhaled H2O2. Fibrosis may be determined and / or predicted based on an increase in H2O2 concentration. Surgical tool parameter adjustments may be generated based on predicted fibrosis. Increased scarring in lung tissue may be determined based on predicted fibrosis. Surgical tool parameter adjustments can be generated based on the determined lung tissue strength. Lung tissue strength can be determined based on lung thickness and / or lung tissue shear strength. Postoperative leakage can be predicted based on lung tissue strength.

[0175] Respiratory muscles may include the diaphragmatic muscles and / or intercostal muscles. Based on selected biomarker sensing system data, respiratory muscle-related biomarkers, complications, and / or contextual information may be determined. Based on selected biomarker sensing system data, respiratory muscle-related conditions, including respiratory infections, collapsed lungs, pulmonary edema, postoperative pain, air leaks, and / or severe pneumonia, may be predicted. Respiratory muscle-related conditions, including respiratory infections, collapsed lungs, and / or pulmonary edema, may be predicted based on diaphragm-related biomarkers, including cough and / or sneezing. Respiratory muscle-related conditions, including postoperative pain, air leaks, collapsed lungs, and / or severe pneumonia, may be predicted based on intercostal muscle biomarkers, including respiratory rate.

[0176] Based on selected biomarker sensing system data, respiratory contents-related biomarkers, complications, and / or contextual information, including postoperative pain, healing capacity, and / or response to surgical injury, can be determined. Based on selected biomarker sensing system data, respiratory contents-related conditions, including inflammation and / or fibrosis, can be predicted. Selected biomarker sensing system data may include environmental data, including mycotoxins and / or airborne chemicals. Respiratory contents-related conditions can be predicted based on airborne chemicals. Inflammation and / or fibrosis can be predicted based on environmental irritants. Tissue mechanical properties can be determined based on inflammation and / or fibrosis. Postoperative pain can be determined based on environmental irritants. Airway inflammation can be predicted based on analyzed mycotoxins and / or arsenic. Surgical tool parameter adjustments can be generated based on airway inflammation. Tissue property alterations can be determined based on analyzed arsenic.

[0177] The detection, prediction, determination, and / or generation described herein may be performed by a computing system described herein, such as a surgical hub, a computing system, and / or a smart device, based on measured data and / or relevant biomarkers generated by a biomarker sensing system.

[0178] The endocrine system may include the hypothalamus, pituitary gland, thymus, adrenal gland, pancreas, testis, intestine, ovary, thyroid gland, parathyroid gland, and / or stomach. Endocrine system-related biomarkers, complications, and / or contextual information may be determined based on analyzed biomarker sensing system data, including immune system function, metastasis, infection risk, insulin secretion, collagen production, menstruation, and / or hypertension. Endocrine system-related conditions may be predicted based on analyzed biomarker sensing system data. The computing system may select one or more biomarkers (e.g., data from a biomarker sensing system) from endocrine system-related biomarkers, including hormones, blood pressure, adrenaline, cortisol, blood glucose, and / or menstrual cycle, for analysis, as described herein. Surgical tool parameter adjustments and / or surgical procedure adjustments may be generated based on endocrine system-related biomarkers, complications, contextual information, and / or conditions.

[0179] For example, based on selected biomarker sensing system data, hypothalamic-related biomarkers, complications, and / or contextual information, including blood pressure regulation, renal function, osmolality, pituitary control, and / or pain tolerance, may be determined. Based on selected biomarker sensing system data, hypothalamic-related conditions, including edema, may be predicted. Hormonal biomarkers may include antidiuretic hormone (ADH) and / or oxytocin. ADH may affect blood pressure regulation, renal function, osmolality, and / or pituitary control. Pain tolerance may be determined based on analyzed oxytocin. Oxytocin may have analgesic effects. Surgical tool parameter adjustments may be generated based on predicted edema.

[0180] For example, based on selected biomarker sensing system data, pituitary-related biomarkers, complications, and / or contextual information, including circadian rhythm synchronization, menstruation, and / or healing rate, may be determined. Based on selected biomarker sensing system data, pituitary-related conditions may be predicted. Circadian rhythm synchronization may be determined based on adrenocorticotropic hormone (ACTH). Circadian rhythm synchronization may provide context for various surgical outcomes. Menstruation may be determined based on reproductive hormone biomarkers. Reproductive hormone biomarkers may include luteinizing hormone and / or follicle-stimulating hormone. Menstruation may provide context for various surgical outcomes. The menstrual cycle may provide context for biomarkers, complications, and / or conditions, including those related to the reproductive system. Wound healing rate may be determined based on thyroid-regulating hormones, including thyrotropin-releasing hormone (TRH).

[0181] For example, based on selected biomarker sensing system data, thymus-related biomarkers, complications, and / or contextual information, including immune system function, may be determined. Based on selected biomarker sensing system data, thymus-related conditions may be predicted. Immune system function may be determined based on thymosine. Thymosine may influence the development of adaptive immunity.

[0182] For example, based on selected biomarker sensing system data, adrenal-related biomarkers, complications, and / or contextual information, including metastasis, vascular health, immune level, and / or infection risk, can be determined. Based on selected biomarker sensing system data, adrenal-related conditions, including edema, can be predicted. Metastasis can be determined based on analyzed adrenaline and / or noradrenaline. Vascular health can be determined based on analyzed adrenaline and / or noradrenaline. A vascular health score can be generated based on the determined vascular health. Immune capacity can be determined based on analyzed cortisol. Infection risk can be determined based on analyzed cortisol. Metastasis can be predicted based on analyzed cortisol. Circadian rhythm can be determined based on measured cortisol. High cortisol levels can impair immunity, increase infection risk, and / or lead to metastasis. High cortisol levels can affect circadian rhythms. Edema can be predicted based on analyzed aldosterone. Aldosterone may promote fluid retention. Fluid retention may be associated with blood pressure and / or edema.

[0183] For example, based on selected biomarker sensing system data, pancreatic-related biomarkers, complications, and / or contextual information, including blood glucose, hormones, polypeptides, and / or blood glucose control, can be determined. Based on selected biomarker sensing system data, pancreatic-related conditions can be predicted. Pancreatic-related biomarkers can provide contextual information for various surgical outcomes. Blood glucose biomarkers may include insulin. Hormone biomarkers may include somatostatin. Polypeptide biomarkers may include pancreatic polypeptides. Blood glucose control can be determined based on insulin, somatostatin, and / or pancreatic polypeptides. Blood glucose control can provide contextual information for various surgical outcomes.

[0184] For example, based on selected biomarker sensing system data, testicular-related biomarkers, complications, and / or contextual information, including reproductive development, sexual arousal, and / or immune system modulation, may be determined. Based on selected biomarker sensing system data, testicular-related conditions may be predicted. Testicular-related biomarkers may include testosterone. Testosterone may provide contextual information about biomarkers, complications, and / or conditions, including those related to the reproductive system. High levels of testosterone may suppress the immune system.

[0185] For example, based on selected biomarker sensing system data, gastric / testicular biomarkers, complications, and / or contextual information, including glucose processing, satiety, insulin secretion, digestion rate, and / or sleeve gastrectomy outcomes, may be determined. Glucose processing and satiety biomarkers may include glucagon-like peptide-1 (GLP-1), cholecystokinin (CCK), and / or peptide YY. Appetite and / or insulin secretion may be determined based on analyzed GLP-1. Increased GLP-1 may be determined based on enhanced appetite and insulin secretion. Sleeve gastrectomy outcomes may be determined based on analyzed GLP-1. Satiety and / or sleeve gastrectomy outcomes may be determined based on analyzed CCK. Increased CCK levels may be predicted based on previous sleeve gastrectomy. Appetite and digestion rate may be determined based on analyzed peptide YY. An increase in peptide YY may reduce appetite and / or increase the rate of digestion.

[0186] For example, based on selected biomarker sensing system data, hormone-related biomarkers, complications, and / or contextual information, including estrogen, progesterone, collagen products, fluid retention, and / or menstruation, may be determined. Collagen production may be determined based on estrogen. Fluid retention may be determined based on estrogen. Surgical tool parameter adjustments may be generated based on the determined collagen production and / or fluid retention.

[0187] For example, based on selected biomarker sensing system data, thyroid and parathyroid-related biomarkers, complications, and / or contextual information, including calcium processing, phosphate processing, metabolism, blood pressure, and / or surgical complications, may be determined. Metabolic biomarkers may include triiodothyronine (T3) and / or thyroxine (T4). Blood pressure may be determined based on analyzed T3 and T4. Hypertension may be determined based on increased T3 and / or increased T4. Surgical complications may be determined based on analyzed T3 and / or T4.

[0188] For example, based on selected biomarker sensing system data, gastric-related biomarkers, complications, and / or contextual information, including appetite, may be determined. Gastric-related biomarkers may include ghrelin, which can induce appetite.

[0189] The detection, prediction, determination, and / or generation described herein may be performed by a computing system described herein, such as a surgical hub, a computing system, and / or a smart device, based on measured data and / or relevant biomarkers generated by a biomarker sensing system.

[0190] Immune system-related biomarkers may be associated with antigens and irritants, antimicrobial enzymes, complement systems, chemokines and cytokines, lymphoid systems, bone marrow, pathogens, damage-associated molecular patterns (DAMPs), and / or cells. Immune system-related biomarkers, complications, and / or contextual information may be determined based on analyzed biomarker sensing system data. The computing system may select one or more biomarkers (e.g., data from a biomarker sensing system) from immune system-related biomarkers, including alcohol consumption, pH, respiratory rate, edema, sweat, and / or environment, for analysis, as described herein. Antigens / irritants

[0191] For example, based on selected biomarker sensing system data, antigen and irritant-related biomarkers, complications, and / or contextual information, including healing capacity, immune function, and / or cardiac problems, can be determined. Based on selected biomarker sensing system data, antigen and irritant-related conditions, including inflammation, can be predicted. Antigen and irritant-related biomarkers may include inhaled chemicals, inhaled irritants, ingested chemicals, and / or ingested irritants. Inhaled chemicals or irritants can be determined based on analyzed environmental data, including airborne chemicals, mycotoxins, and / or arsenic. Airborne chemicals may include cigarette smoke, asbestos, crystalline silica, alloy particles, and / or carbon nanotubes. Pneumonia can be predicted based on analyzed airborne chemicals. Surgical tool parameter adjustments can be made based on the determined pneumonia. Airway inflammation can be predicted based on analyzed mycotoxins and / or arsenic. Surgical tool parameter adjustments can be made based on the determined airway inflammation. Arsenic exposure can be determined by analyzing urine, saliva, and / or ambient air samples.

[0192] For example, based on selected biomarker sensing system data, antimicrobial enzyme-related biomarkers, complications, and / or contextual information, including colonic status, can be determined. Based on selected biomarker sensing system data, antimicrobial enzyme-related statuses, including GI inflammation, acute kidney injury, Enterococcus faecalis infection, and / or Staphylococcus aureus infection, can be predicted. Antimicrobial biomarkers may include lysozymes, lipocalin-2 (NGAL), and / or orosomucoids. GI inflammation can be predicted based on analyzed lysozyme. Increased lysozyme levels can be determined and / or predicted based on GI inflammation. Colonic status can be determined based on analyzed lysozyme. Surgical tool parameter adjustments can be generated based on analyzed lysozyme levels. Acute kidney injury can be predicted based on analyzed NGAL. NGAL can be detected from serum and / or urine.

[0193] For example, based on selected biomarker sensing system data, complement system-related biomarkers, complications, and / or contextual information, including bacterial infection susceptibility, can be determined. Bacterial infection susceptibility can be determined based on analyzed complement system deficiencies.

[0194] For example, based on selected biomarker sensing system data, chemokine and cytokine-related biomarkers, complications, and / or contextual information may be determined, including infectious load, inflammatory load, vascular permeability regulation, omentins, colon tissue characteristics, and / or postoperative recovery. Based on selected biomarker sensing system data, chemokine and cytokine-related conditions may be predicted, including inflammatory bowel disease, postoperative infection, pulmonary fibrosis, pulmonary scarring, pulmonary fibrosis, gastroesophageal reflux disease, cardiovascular disease, edema, and / or hyperplasia. Infection and / or inflammatory load biomarkers may include oral, saliva, exhaled, and / or C-reactive protein (CRP) data. Salivary cytokines may include interleukin-1 beta (IL-1β), interleukin-6 (IL-6), tumor necrosis factor alpha (TNF-α), and / or interleukin-8 (IL-8).

[0195] In the examples, inflammatory bowel disease can be predicted based on analyzed salivary cytokines. Increased salivary cytokines can be determined based on inflammatory bowel disease. Colon tissue characteristics can be determined based on predicted inflammatory bowel disease. Colon tissue characteristics may include scarring, edema, and / or ulcers. Postoperative recovery and / or infection can be determined based on predicted inflammatory bowel disease. Tumor size and / or pulmonary scarring can be determined based on analyzed exhaled biomarkers. Pulmonary fibrosis, pulmonary fibrosis, and / or gastroesophageal reflux disease can be predicted based on analyzed exhaled biomarkers. Exhaled biomarkers may include exhaled cytokines, pH, hydrogen peroxide (H2O2), and / or nitric oxide. Exhaled cytokines may include IL-6, TNF-α, and / or interleukin-17 (IL-17). Pulmonary fibrosis can be predicted based on pH and / or H2O2 measured from exhaled breath. Fibrosis can be predicted based on an increase in H2O2 concentration. Increased lung tissue scarring can be predicted based on fibrosis. Surgical tool parameter adjustments can be made based on predicted pulmonary fibrosis. In the examples, pulmonary fibrosis and / or gastroesophageal reflux disease can be predicted based on analyzed exhaled nitric oxide. Pulmonary fibrosis can be predicted based on the determined increase in nitrates and / or nitrites. Gastroesophageal disease can be predicted based on the determined decrease in nitrates and / or nitrites. Surgical tool parameter adjustments can be made based on predicted pulmonary fibrosis and / or gastroesophageal reflux disease. Cardiovascular disease, inflammatory bowel disease, and / or infections can be predicted based on analyzed CRP biomarkers. The risk of serious cardiovascular disease may increase with high CRP concentrations. Inflammatory bowel disease can be predicted based on elevated CRP concentrations. Infection can be predicted based on elevated CRP levels. In the examples, edema can be predicted based on analyzed vascular permeability regulatory biomarkers. Increased vascular permeability during inflammation can be determined based on analyzed bradykinin and / or histamine. Edema can be predicted based on increased vascular permeability during inflammation. Vascular permeability can be determined based on endothelial adhesion molecules. Endothelial adhesion molecules can be determined based on cell samples.Endothelial adhesion molecules can influence vascular permeability, immune cell reinforcement, and / or fluid retention in edema. Surgical tool parameter adjustments can be generated based on analyzed vascular permeability-regulating biomarkers. In the examples, hyperplasia can be predicted based on analyzed omentins. Hyperplasia can alter tissue properties. Surgical tool parameter adjustments can be generated based on predicted hyperplasia.

[0196] For example, based on selected biomarker sensing system data, lymphatic system-related biomarkers, complications, and / or contextual information, including lymph nodes, lymphatic composition, lymphatic location, and / or lymphatic swelling, may be determined. Based on selected biomarker sensing system data, lymphatic system-related conditions, including postoperative inflammation, postoperative infection, and / or fibrosis, may be predicted. Postoperative inflammation and / or infection may be predicted based on determined lymphatic swelling. Surgical tool parameter adjustments may be generated based on analyzed lymphatic swelling. Surgical tool parameter adjustments, including harmonic tool parameter adjustments, may be generated based on determined collagen deposition. Collagen deposition may increase with lymphatic fibrosis. Inflammatory status may be predicted based on lymphatic composition. The spread of metastatic cells may be determined based on lymphatic composition. Surgical tool parameter adjustments may be generated based on the lymphopeptideome. The lymphopeptideome may change based on the inflammatory status.

[0197] For example, based on selected biomarker sensing system data, pathogen-associated biomarkers, complications, and / or contextual information, including pathogen-associated molecular patterns (PAMPs), pathogen load, Helicobacter pylori, and / or gastric tissue characteristics, can be determined. Based on selected biomarker sensing system data, pathogen-associated conditions, including infection, gastritis, and / or ulcers, can be predicted. PAMP biomarkers may include pathogen antigens. Pathogen antigens may influence the pathogen load. Gastritis and / or potential ulcers can be predicted based on the predicted infection. Alterations in gastric tissue characteristics can be determined based on the predicted infection.

[0198] For example, based on selected biomarker sensing system data, DAMP-related biomarkers, complications, and / or contextual information, including stress (e.g., cardiovascular, metabolic, blood glucose, and / or cellular) and / or necrosis, can be determined. Based on selected biomarker sensing system data, DAMP-related conditions, including acute myocardial infarction, enteritis, and / or infection, can be predicted. Cellular stress biomarkers may include creatine kinase MB, pyruvate kinase isoenzyme type M2 (M2-PK), irisin, and / or microRNA. In the example, acute myocardial infarction can be predicted based on the analyzed creatine kinase MB biomarker. Enteritis can be predicted based on the analyzed M2-PK biomarker. Stress can be determined based on the analyzed irisin biomarker. Inflammatory diseases and / or infections can be predicted based on the analyzed microRNA biomarker. Surgical tool parameter adjustments can be generated based on the predicted inflammation and / or infection. Inflammation and / or infection can be predicted based on analyzed necrotic biomarkers. Necrotic biomarkers may include reactive oxygen species (ROS). Inflammation and / or infection can be predicted based on an increase in ROS. Postoperative recovery can be determined based on analyzed ROS.

[0199] For example, based on the selected biomarker sensing system, cell-related biomarkers, complications, and / or contextual information, including granulocytes, natural killer cells (NK cells), macrophages, lymphocytes, and / or colon tissue characteristics, may be determined. Based on the selected biomarker sensing system, cell-related conditions, including postoperative infection, ulcerative colitis, inflammation, and / or inflammatory bowel disease, may be predicted. Granulocyte biomarkers may include eosinophilia and / or neutrophils. Eosinophilia biomarkers may include sputum cell count, eosinophilic cationic proteins, and / or exhaled nitric oxide fraction. Neutrophil biomarkers may include S100 protein, myeloperoxidase, and / or human neutrophil lipocalin. Lymphocyte biomarkers may include antibodies, adaptive responses, and / or immunological memory. Antibodies may include immunoglobulin A (IgA) and / or immunoglobulin M (IgM). In the examples, postoperative infection and / or preoperative inflammation can be predicted based on the analyzed sputum cell count. Ulcerative colitis can be predicted based on the analyzed eosinophilic cationic protein. Alteration of colon tissue properties can be determined based on predicted ulcerative colitis. Eosinophils may produce eosinophilic cationic proteins that can be determined based on ulcerative colitis. Inflammation can be predicted based on the analyzed exhaled nitric oxide fraction. Inflammation may include type 1 asthma-like inflammation. Surgical tool parameter adjustments can be generated based on predicted inflammation. In the examples, inflammatory bowel disease can be predicted based on the S100 protein. The S100 protein may include calprotectin. Colon tissue properties can be determined based on predicted inflammatory bowel disease. Ulcerative colitis can be predicted based on analyzed myeloperoxidase and / or human neutrophil lipocalin. Alterations in colon tissue characteristics can be determined based on predicted ulcerative colitis. In the examples, inflammation can be predicted based on antibody biomarkers. Intestinal inflammation can be predicted based on IgA. Cardiovascular inflammation can be predicted based on IgM.

[0200] The detection, prediction, determination, and / or generation described herein may be performed by a computing system described herein, such as a surgical hub, a computing device, and / or a smart device, based on measured data and / or relevant biomarkers generated by a biomarker sensing system.

[0201] Tumors may include benign and / or malignant tumors. Tumor-related biomarkers, complications, contextual information, and / or conditions may be determined and / or predicted based on analyzed biomarker sensing system data. The computing system may select one or more biomarkers (e.g., data from a biomarker sensing system) from tumor-related biomarkers, including circulating tumor cells, for analysis, as described herein.

[0202] For example, based on selected biomarker sensing system data, benign tumor-related biomarkers, states, and / or contextual information, including benign tumor replication, benign tumor metabolism, and / or benign tumor synthesis, may be determined. Benign tumor replication may include mitotic activity, mitotic metabolism, and / or synthesis biomarker rates. Benign tumor metabolism may include metabolic demand and / or metabolite biomarkers. Benign tumor synthesis may include protein expression and / or gene expression biomarkers.

[0203] For example, based on selected biomarker sensing system data, malignant tumor-related biomarkers, complications, and / or contextual information may be determined, including malignant tumor synthesis, malignant tumor metabolism, malignant tumor replication, microsatellite stability, metastasis risk, metastatic tumors, tumor growth, tumor regression, and / or metastatic activity. Based on selected biomarker sensing system data, malignant tumor-related conditions, including cancer, may be predicted. Malignant tumor synthesis may include gene expression and / or protein expression biomarkers. Gene expression may be determined based on tumor biopsy and / or genomic analysis. Protein expression biomarkers may include cancer antigen 125 (CA-125) and / or carcinoembryonic antigen (CEA). CEA may be measured based on urine and / or saliva. Malignant tumor replication data may include mitotic activity rate, mitotic inclusions, tumor mass, and / or microRNA 200c.

[0204] In the examples, microsatellite stability may be determined based on analyzed gene expression. Metastasis risk may be determined based on determined microsatellite stability. A higher metastasis risk may be determined and / or predicted based on low microsatellite instability. In the examples, metastatic tumors, tumor growth, tumor metastasis, and / or tumor regression may be determined based on analyzed protein expression. Metastatic tumors may be determined and / or predicted based on elevated CA-125. Cancer may be predicted based on CA-125. Cancer may be predicted based on specific levels of CEA. Tumor growth, metastasis, and / or regression may be monitored based on detected changes in CEA. Metastatic activity may be determined based on malignant tumor replication. Cancer may be predicted based on malignant tumor replication. MicroRNA 200c may be released into the bloodstream by certain cancers. Metastatic activity may be determined and / or predicted based on the presence of circulating tumor cells.

[0205] The detection, prediction, determination, and / or generation described herein may be performed by a computing system described herein, such as a surgical hub, a computing device, and / or a smart device, based on measured data and / or relevant biomarkers generated by a biomarker sensing system.

[0206] The musculoskeletal system may include muscle, bone, bone marrow, and / or cartilage. Muscle may include smooth muscle, cardiac muscle, and / or skeletal muscle. Smooth muscle may include calmodulin, connective tissue, structural features, hyperplasia, actin, and / or myosin. Bone may include calcified bone, osteoblasts, and / or osteoclasts. Bone marrow may include red bone marrow and / or yellow bone marrow. Cartilage may include cartilage tissue and / or chondrocytes. Musculoskeletal-related biomarkers, complications, contextual information, and / or conditions may be determined and / or predicted based on analyzed biomarker sensing system data. A computing system may select one or more biomarkers (e.g., data from a biomarker sensing system) from the musculoskeletal-related biomarkers for analysis, as described herein.

[0207] For example, based on selected biomarker sensing system data, muscle-related biomarkers, complications, and / or contextual information, including serum calmodulin levels, mechanical strength, muscle body, hyperplasia, muscle contraction ability, and / or muscle injury, can be determined. Based on selected biomarker sensing system data, muscle-related conditions can be predicted. In the examples, neurological conditions can be predicted based on analyzed serum calmodulin levels. Mechanical strength can be determined based on analyzed smooth muscle collagen levels. Collagen can affect mechanical strength because it can bind smooth muscle filaments together. Muscle body can be determined based on analyzed structural features. Muscle body may include intermediates and / or compacts. Hyperplasia can be determined based on analyzed omentin levels. Omentin may indicate hyperplasia. Hyperplasia can be determined and / or predicted based on thickened areas of smooth muscle. Muscle contraction ability can be determined based on analyzed smooth muscle alpha-actin expression. Muscle contraction failure may be due to abnormal actin expression in smooth muscle. In the examples, muscle damage may be determined based on analyzed circulating smooth muscle myosin and / or skeletal muscle myosin. Muscle strength may be determined based on analyzed circulating smooth muscle myosin. Muscle damage and / or weak and brittle smooth muscle may be determined and / or predicted based on circulating smooth muscle myosin and / or skeletal muscle myosin. Smooth muscle myosin may be measured from urine. In the examples, muscle damage may be determined based on cardiac biomarkers and / or skeletal muscle biomarkers. Cardiac biomarkers and / or skeletal muscle biomarkers may include circulating troponin. Muscle damage may be determined and / or predicted based on circulating troponin along with myosin.

[0208] For example, based on selected biomarker sensing system data, bone-related biomarkers, complications, and / or contextual information, including calcification bone characteristics, calcification bone function, osteoblast count, osteoid secretion, osteoclast count, and / or secretory osteoclasts, can be determined.

[0209] For example, based on selected biomarker sensing system data, bone marrow-related biomarkers, complications, and / or contextual information, including tissue destruction and / or collagen secretion, can be determined. Arthritis-induced destruction of cartilage tissue can be determined based on analyzed cartilage tissue biomarkers. Collagen secretion by muscle cells can be determined based on analyzed chondrocyte biomarkers.

[0210] The detection, prediction, determination, and / or generation described herein may be performed by a computing system described herein, such as a surgical hub, a computing device, and / or a smart device, based on measured data and / or relevant biomarkers generated by a biomarker sensing system.

[0211] Reproductive system-related biomarkers, complications, contextual information, and / or conditions may be determined and / or predicted based on analyzed biomarker sensing system data. A computing system may select one or more biomarkers (e.g., data from a biomarker sensing system) from the reproductive system-related biomarkers for analysis, as described herein. Reproductive system-related biomarkers, complications, and / or contextual information may be determined based on analyzed biomarker sensing system data, including female anatomical structures, female function, menstrual cycle, pH, bleeding, wound healing, and / or scarring. Female anatomical biomarkers may include the ovaries, vagina, cervix, fallopian tube, and / or uterus. Female function biomarkers may include reproductive hormones, pregnancy, menopause, and / or menstrual cycle. Reproductive system-related conditions may be predicted based on analyzed biomarker sensing system data, including endometriosis, adhesions, vaginosis, bacterial infection, SSI, and / or pelvic abscess.

[0212] In the embodiments, endometriosis can be predicted based on female anatomical biomarkers. Adhesions can be predicted based on female anatomical biomarkers. Adhesions may include sigmoid colon adhesions. Endometriosis can be predicted based on menstrual blood. Menstrual blood may contain molecular signals from endometriosis. Sigmoid colon adhesions can be predicted based on predicted endometriosis. In the embodiments, menstrual period and / or menstrual cycle length can be determined based on the menstrual cycle. Bleeding, wound healing, and / or scarring can be determined based on the analyzed menstrual period. The risk of endometriosis can be predicted based on the analyzed menstrual cycle. A higher risk of endometriosis can be predicted based on a shorter menstrual cycle length. Molecular signals can be determined based on the pH of the analyzed menstrual blood and / or discharge. Endometriosis can be predicted based on the determined molecular signals. Vaginal pH can be determined based on the pH of the analyzed discharge. Vaginosis and / or bacterial infection can be predicted based on the analyzed vaginal pH. Vaginosis and / or bacterial infection can be predicted based on changes in vaginal pH. The risk of SSI and / or pelvic abscess during gynecological procedures can be predicted based on the predicted vaginosis.

[0213] The detection, prediction, determination, and / or generation described herein may be performed by any computing system within any of the computer-implemented patient and surgeon monitoring systems described herein, such as a surgical hub, a computing device, and / or a smart device, based on measurement data and / or associated biomarkers generated by one or more sensing systems.

[0214] Figure 2A shows an embodiment of a surgical monitoring system 20002 in a surgical operating room. As illustrated in Figure 2A, a patient is being operated on by one or more healthcare professionals (HCPs). The HCPs are monitored by one or more surgical sensing systems 20020 worn by the HCPs. The HCPs and the environment surrounding them may also be monitored by one or more environmental sensing systems, including, for example, a set of cameras 20021, a set of microphones 20022, and other sensors, which may be deployed in the operating room. As shown in Figure 1, the surgical sensing systems 20020 and the environmental sensing systems may be in communication with a surgical hub 20006, which may then be in communication with one or more cloud servers 20009 of a cloud computing system 20008. Environmental sensing systems can be used to measure one or more environmental attributes, such as the location of the HCP in the operating room, the movement of the HCP, ambient noise in the operating room, and the temperature / humidity in the operating room.

[0215] As illustrated in Figure 2A, the primary display 20023 and one or more audio output devices (e.g., speakers 20019) are positioned in the sterile field so as to be visible to the operator of the operating table 20024. In addition, a visualization / notification tower 20026 is positioned outside the sterile field. The visualization / notification tower 20026 may include a first non-sterile human interactive device (HID) 20027 and a second non-sterile HID 20029, which may face in opposite directions from each other. The HIDs may be displays or displays having touchscreens that allow humans to interface directly with the HIDs. The human interface system guided by the surgical hub 20006 may be configured to utilize the HIDs 20027, 20029, and 20023 to coordinate the flow of information to operators inside and outside the sterile field. In an embodiment, the surgical hub 20006 may cause the HID (e.g., primary HID 20023) to display notifications and / or information relating to the patient and / or surgical procedure. In an embodiment, the surgical hub 20006 may prompt and / or receive input from personnel in a sterile or non-sterile area. In an embodiment, the surgical hub 20006 may cause the HID to display snapshots of the surgical site recorded by the imaging device 20030 on a non-sterile HID 20027 or 20029 while maintaining live video of the surgical site on the primary HID 20023. The snapshots on the non-sterile display 20027 or 20029 may, for example, allow a non-sterile operator to perform diagnostic steps related to the surgical procedure.

[0216] In one embodiment, the surgical hub 20006 may be configured to send diagnostic input or feedback entered by a non-sterile operator in the visualization tower 20026 to a primary display 20023 in the sterile field, which can then be viewed by a sterile operator on the operating table. In one embodiment, the input may take the form of modifications to a snapshot displayed on a non-sterile display 20027 or 20029, which can then be sent to the primary display 20023 by the surgical hub 20006.

[0217] Referring to Figure 2A, surgical instrument 20031 is used as part of a surgeon monitoring system 20002 in a surgical procedure. Hub 20006 may be configured to coordinate the flow of information to the display of surgical instrument 20031. For example, in U.S. Patent Application Publication 2019-0200844(A1) (U.S. Patent Application No. 16 / 209,385), filed 4 December 2018, entitled "METHOD OF HUB COMMUNICATION, PROCESSING, STORAGE AND DISPLAY," the disclosure of which is incorporated herein by reference in its entirety. Diagnostic input or feedback entered by a non-sterile operator in the visualization tower 20026 can be sent by Hub 20006 to a surgical instrument display in the sterile field, which can then be viewed by the operator of surgical instrument 20031. Examples of surgical instruments suitable for use with surgical system 20002 are described, for example, under the heading "Surgical Instrument Hardware" in U.S. Patent Application Publication No. 2019-0200844(A1) (U.S. Patent Application No. 16 / 209,385) filed December 4, 2018, entitled "METHOD OF HUB COMMUNICATION, PROCESSING, STORAGE AND DISPLAY," the disclosure of which is incorporated herein by reference in its entirety.

[0218] Figure 2A illustrates an embodiment of a surgical system 20002 used to perform a surgical procedure on a patient lying on an operating table 20024 in a surgical operating room 20035. A robotic system 20034 may be used as part of the surgical system 20002 in the surgical procedure. The robotic system 20034 may include a surgeon's console 20036, a patient-side cart 20032 (surgical robot), and a surgical robot hub 20033. The patient-side cart 20032 allows the surgeon to manipulate at least one detachably coupled surgical tool 20037 through a minimally invasive incision in the patient's body while viewing the surgical site through the surgeon's console 20036. Images of the surgical site can be acquired by a medical imaging device 20030, which can be manipulated by the patient-side cart 20032 to align the orientation of the imaging device 20030. The robot hub 20033 can be used to process images of the surgical site, which can then be displayed to the surgeon via the surgeon's console 20036.

[0219] Other types of robotic systems can be readily adapted for use with surgical systems 20002. Various examples of robotic systems and surgical tools suitable for use with this disclosure are described in U.S. Patent Application Publication No. 2019-0201137(A1) (U.S. Patent Application No. 16 / 209,407), filed December 4, 2018, entitled "METHOD OF ROBOTIC HUB COMMUNICATION, DETECTION, AND CONTROL," the disclosure of which is incorporated herein by reference in its entirety.

[0220] Various examples of cloud-based analytical methods performed by Cloud Computing System 20008 and suitable for use with this disclosure are described in U.S. Patent Application Publication No. 2019-0206569(A1) (U.S. Patent Application No. 16 / 209,403), filed on 4 December 2018, entitled "METHOD OF CLOUD BASED DATA ANALYTICS FOR USE WITH THE HUB," which is incorporated herein by reference in its entirety.

[0221] In various embodiments, the imaging device 20030 may include at least one image sensor and one or more optical components. Suitable image sensors include, but are not limited to, charge-coupled device (CCD) sensors and complementary metal-oxide-semiconductor (CMOS) sensors.

[0222] The optical components of the imaging device 20030 may include one or more illumination sources and / or one or more lenses. One or more illumination sources may be directed to illuminate a portion of the surgical field. One or more image sensors may receive light reflected or refracted from the surgical field, including light reflected or refracted from tissue and / or surgical instruments.

[0223] One or more illumination sources may be configured to emit electromagnetic energy in the visible and invisible spectra. The visible spectrum, sometimes also called the light spectrum or emission spectrum, is the portion of the electromagnetic spectrum that is visible to the human eye (i.e., detectable by the human eye), and is sometimes called visible light, or simply light. The typical human eye responds to wavelengths in air from about 380 nm to about 750 nm.

[0224] The invisible spectrum (e.g., the non-emission spectrum) is a portion of the electromagnetic spectrum located below and above the visible spectrum (i.e., wavelengths below approximately 380 nm and above approximately 750 nm). The invisible spectrum is undetectable to the human eye. Wavelengths above approximately 750 nm are longer than the red visible spectrum and consist of invisible infrared (IR), microwaves, and radio electromagnetic radiation. Wavelengths below approximately 380 nm are shorter than the violet spectrum and consist of invisible ultraviolet, X-ray, and gamma-ray electromagnetic radiation.

[0225] In various embodiments, the imaging device 20030 is configured for use in minimally invasive surgery. Examples of imaging devices suitable for use with this disclosure include, but are not limited to, arthroscopes, angioscopes, bronchoscopes, cholangioscopies, colonoscopes, cytoscopes, duodenoscopes, enteroscopes, esophagogastroduodenoscopes (gastroscopy), endoscopes, laryngoscopes, nasopharyngo-neproscopes, sigmoidoscopy, thoracoscopy, and ureteroscopes.

[0226] The imaging device may employ multispectral monitoring to distinguish between topography and underlying structures. Multispectral imaging captures image data within a specific wavelength range from the entire electromagnetic spectrum. Wavelengths can be separated by filters or by using instruments sensitive to specific wavelengths, including frequencies beyond the visible light range, such as IR and ultraviolet light. Spectral imaging makes it possible to extract additional information that cannot be captured by the red, green, and blue receptors of the human eye. The use of multispectral imaging is described in detail under the heading "Advanced Imaging Acquisition Module" in U.S. Patent Application Publication No. 2019-0200844(A1) (U.S. Patent Application No. 16 / 209,385), filed on 4 December 2018, entitled "METHOD OF HUB COMMUNICATION, PROCESSING, STORAGE AND DISPLAY," which is incorporated herein by reference in its entirety. Multispectral monitoring can be a useful tool for repositioning the surgical field after the surgical task is completed to perform one or more of the tests described above on the treated tissue. It is self-evident that strict sterilization of the operating room and surgical instruments is necessary in any surgical procedure. The strict sanitary and sterilization conditions required in the “operating room,” i.e., the operating room or treatment room, require the highest possible sterility of all medical devices and instruments. Part of the sterilization process described above includes the need to sterilize everything that comes into contact with the patient or enters the sterile field, including imaging devices and their accessories and components. It will be understood that the sterile field may be considered a specific area deemed free of microorganisms, such as within a tray or on a sterile towel, or it may be considered the area immediately surrounding the patient when the surgical procedure is ready. The sterile field may include cleaned team members wearing appropriate clothing, as well as all equipment and restraints within that area.

[0227] The wearable sensing system 20011 illustrated in Figure 1 may include one or more sensing systems, for example, a surgical sensing system 20020 as shown in Figure 2A. The surgical sensing system 20020 may include sensing systems for monitoring and detecting a set of physical and / or physiological conditions of a healthcare provider (HCP). An HCP is generally a surgeon, or one or more healthcare professionals assisting a surgeon or other healthcare provider. In one embodiment, the sensing system 20020 may measure a set of biomarkers to monitor the HCP's heart rate. In another embodiment, the sensing system 20020 worn on the surgeon's wrist (e.g., a watch or wristband) may use an accelerometer to detect hand movements and / or tremors and determine the magnitude and frequency of the tremors. The sensing system 20020 may transmit the measurement data associated with the set of biomarkers and the data associated with the surgeon's physical condition to a surgical hub 20006 for further processing. One or more environmental sensing devices may transmit environmental information to the surgical hub 20006. For example, an environmental sensing device may include a camera 20021 for detecting the position of the HCP's hand / body. An environmental sensing device may include a microphone 20022 for measuring ambient noise in the operating room. Other environmental sensing devices may include, for example, a thermometer for measuring temperature and a hygrometer for measuring ambient humidity in the operating room. The surgical hub 20006 may use surgeon biomarker measurement data and / or environmental sensing information, either alone or in communication with a cloud computing system, to correct the averaged delay of a handheld instrument control algorithm or a robotic interface, for example, to minimize tremor. In an embodiment, a surgeon sensing system 20020 may measure one or more surgeon biomarkers associated with the HCP and transmit the measurement data associated with the surgeon biomarkers to the surgical hub 20006.The surgical sensing system 20020 may use one or more of the following RF protocols to communicate with the surgical hub 20006: Bluetooth, Bluetooth Low-Energy (BLE), Bluetooth Smart, Zigbee, Z-wave, IPv6 Low-power wireless Personal Area Network (6LoWPAN), and Wi-Fi. Surgical biomarkers may include one or more of the following: stress, heart rate, etc. Environmental measurements from the operating room may include ambient noise levels associated with the surgeon or patient, surgeon and / or staff movements, surgeon and / or staff attention levels, etc.

[0228] The surgical hub 20006 may use surgeon biomarker measurement data associated with HCP to adaptively control one or more surgical instruments 20031. For example, the surgical hub 20006 may transmit a control program to the surgical instrument 20031 to control the actuators of the surgical hub 20006 to limit or compensate for fatigue and the use of fine motor skills. The surgical hub 20006 may transmit a control program based on context regarding situational awareness and / or the importance or urgency of the task. The control program may instruct the instrument to modify its operation to provide more control when control is needed.

[0229] Figure 2B shows an embodiment of patient monitoring system 20003 (e.g., a controlled patient monitoring system). As illustrated in Figure 2B, a patient in a controlled environment (e.g., a hospital recovery room) may be monitored by multiple sensing systems (e.g., patient sensing system 20041). Patient sensing system 20041 (e.g., a headband) may be used to measure the electrical activity of the patient's brain by measuring electroencephalography (EEG). Patient sensing system 20042 may be used to measure various biomarkers of the patient, including, for example, heart rate, VO2 levels, etc. Patient sensing system 20043 (e.g., a flexible patch attached to the patient's skin) may be used to measure sweat lactate and / or potassium levels by analyzing small amounts of sweat captured from the skin surface using microfluidic channels. Patient sensing system 20044 (e.g., a wristband or watch) may be used to measure blood pressure, heart rate, heart rate variability, VO2 levels, etc., using various techniques as described herein. Patient sensing systems 20045 (e.g., a ring on a finger) may be used to measure peripheral temperature, heart rate, heart rate variability, VO2 levels, etc., using various techniques as described herein. Patient sensing systems 20041-20045 may use a radio frequency (RF) link to establish communication with the surgical hub 20006. Patient sensing systems 20041-20045 may use one or more of the following RF protocols for communication with the surgical hub 20006: Bluetooth, Bluetooth Low Energy (BLE), Bluetooth Smart, Zigbee, Z-wave, IPv6 Low Power Wireless Personal Area Network (6LoWPAN), Thread, Wi-Fi, etc.

[0230] Sensing systems 20041-20045 may be in communication with surgical hub 20006, which may then be in communication with remote server 20009 of remote cloud computing system 20008. Surgical hub 20006 is also in communication with HID20046. HID20046 may display measurement data associated with one or more patient biomarkers. For example, HID20046 may display blood pressure, oxygen saturation level, respiratory rate, etc. HID20046 may display notifications for the patient or HCP and provide information about the patient, such as recovery milestones or complications. In embodiments, information about recovery milestones or complications may be associated with surgical procedures the patient may have undergone. In embodiments, HID20046 may display commands for the patient to perform activities. For example, HID20046 may display inhalation commands and exhalation commands. In this embodiment, HID20046 may be part of a sensing system.

[0231] As illustrated in Figure 2B, the patient and the environment surrounding the patient may be monitored by one or more environmental sensing systems 20015, including, for example, microphones (e.g., for detecting ambient noise associated with or around the patient), temperature / humidity sensors, and cameras for detecting the patient's breathing patterns. The environmental sensing systems 20015 may be in communication with a surgical hub 20006, which in turn is in communication with a remote server 20009 of a remote cloud computing system 20008.

[0232] In the embodiment, the patient sensing system 20044 may receive notification information from the surgical hub 20006 for display on the display unit or HID of the patient sensing system 20044. The notification information may include, for example, notifications about recovery milestones or notifications about complications in the case of postoperative recovery. In the embodiment, the notification information may include a manageable severity level associated with the notification. The patient sensing system 20044 may display the notification and the manageable severity level to the patient. The patient sensing system may alert the patient using tactile feedback. Visual and / or tactile notifications may be accompanied by audible notifications prompting the patient to pay attention to the visual notification provided on the sensing system's display unit.

[0233] Figure 2C shows an embodiment of a patient monitoring system (e.g., an uncontrolled patient monitoring system 2004). As illustrated in Figure 2C, a patient in an uncontrolled environment (e.g., the patient's residence) is monitored by multiple patient sensing systems 20041–20045. Patient sensing systems 20041–20045 may measure and / or monitor measurement data associated with one or more patient biomarkers. For example, patient sensing system 20041, a headband, may be used to measure an electroencephalogram (EEG). Other patient sensing systems 20042, 20043, 20044, and 20045 are embodiments in which various patient biomarkers are monitored, measured, and / or reported, as shown in Figure 2B. One or more of the patient sensing systems 20041-20045 may be in a state to transmit measurement data associated with the monitored patient biomarker to a computing device 20047, which may then be in a state to communicate with a remote server 20009 of a remote cloud computing system 20008. The patient sensing systems 20041-20045 may use a radio frequency (RF) link to communicate with the computing device 20047 (e.g., a smartphone, tablet, etc.). The patient sensing systems 20041-20045 may use one or more of the RF protocols such as Bluetooth, Bluetooth Low Energy (BLE), Bluetooth Smart, Zigbee, Z-wave, IPv6 Low Power Wireless Personal Area Network (6LoWPAN), Thread, and Wi-Fi for communication with the computing device 20047. In this embodiment, patient sensing systems 20041-20045 may be connected to a computing device 20047 via a wireless router, wireless hub, or wireless bridge.

[0234] Computing device 20047 may be in communication with remote server 20009, which is part of cloud computing system 20008. In an embodiment, computing device 20047 may be in communication with remote server 20009 via a cable / FIOS networking node of an Internet service provider. In an embodiment, a patient sensing system may communicate directly with remote server 20009. Computing device 20047 or the sensing system may communicate with remote server 20009 via a cellular transmission / reception point (TRP) or base station using one or more of the following cellular protocols: GSM / GPRS / EDGE (2G), UMTS / HSPA (3G), Long Term Evolution (LTE) or 4G, LTE-Advanced (LTE-A), New Radio (NR) or 5G.

[0235] In the embodiment, the computing device 20047 may display information associated with patient biomarkers. For example, the computing device 20047 may display blood pressure, oxygen saturation level, respiratory rate, etc. The computing device 20047 may display notifications for the patient or HCP and provide information about the patient, such as recovery milestones or information about complications.

[0236] In the embodiment, the computing device 20047 and / or the patient sensing system 20044 may receive notification information from the surgical hub 20006 for display on the display unit of the computing device 20047 and / or the patient sensing system 20044. The notification information may include, for example, notifications about recovery milestones or notifications about complications in the case of postoperative recovery. The notification information may also include a manageable severity level associated with the notification. The computing device 20047 and / or the sensing system 20044 may display the notifications and manageable severity levels to the patient. The patient sensing system may also alert the patient using tactile feedback. Visual and / or tactile notifications may be accompanied by audible notifications prompting the patient to pay attention to the visual notifications provided on the display unit of the sensing system.

[0237] Figure 3 shows an exemplary surgical monitoring system 20002 having a surgical hub 20006 paired with a wearable sensing system 20011, an environmental sensing system 20015, a human interface system 20012, a robotic system 20013, and an intelligent instrument 20014. The hub 20006 includes a display 20048, an imaging module 20049, a generator module 20050, a communication module 20056, a processor module 20057, a storage array 20058, and an operating room mapping module 20059. In certain embodiments, as illustrated in Figure 3, the hub 20006 further includes a fume extraction module 20054 and / or a suction / irrigation module 20055. During surgical procedures, applying energy to tissue for sealing and / or cutting is generally associated with fume extraction, suction of excess fluid, and / or tissue irrigation. Fluid lines, power lines, and / or data lines from different sources often become entangled during surgical procedures. Dealing with this problem during a surgical procedure can result in the loss of valuable time. Untangling lines may require disconnecting them from their corresponding modules, which may necessitate resetting the modules. The modular enclosure of the hub 20060 provides a unified environment for managing power lines, data lines, and fluid lines, reducing the frequency of such line entanglement. Aspects of this disclosure present a surgical hub 20006 for use in surgical procedures involving energy application to tissue at a surgical site. The surgical hub 20006 includes a hub enclosure 20060 and a combination generator module slidably receivable within a docking station of the hub enclosure 20060. The docking station includes data and power contacts. The combination generator module includes two or more ultrasonic energy generator components, bipolar RF energy generator components, and unipolar RF energy generator components housed within a single unit.In one embodiment, the combined generator module also includes a fume exhaust component, at least one energy supply cable for connecting the combined generator module to a surgical instrument, at least one fume exhaust component configured to discharge smoke, fluid and / or particulate matter generated by the application of therapeutic energy to tissue, and a fluid line extending from the remote surgical site to the fume exhaust component. In one embodiment, the above fluid line may be a first fluid line, and a second fluid line may extend from the remote surgical site to a suction and irrigation module 20055 slidably received within the hub enclosure 20060. In one embodiment, the hub enclosure 20060 may include a fluid interface. Certain surgical procedures may require the application of two or more energy types to tissue. One energy type may be more beneficial for cutting tissue, while another different energy type may be more beneficial for sealing tissue. For example, a bipolar generator can be used to seal tissue, while an ultrasonic generator can be used to cut sealed tissue. Aspects of the present disclosure present a solution in which a modular enclosure 20060 of a hub is configured to house different generators and to facilitate bidirectional communication between them. One of the advantages of the modular enclosure 20060 of a hub is that it allows for the rapid removal and / or replacement of various modules. Aspects of the present disclosure present a modular surgical enclosure for use in surgical procedures involving the application of energy to tissue. The modular surgical enclosure includes a first energy generator module configured to generate first energy for application to tissue, and a first docking station having a first docking port including first data and power contacts, wherein the first energy generator module is slidably movable to electrically engage with the power and data contacts, and the first energy generator module is slidably movable to disengage from the electrical engagement with the first power and data contacts.In addition to the above, the modular surgical enclosure also includes a second energy generator module configured to generate a second energy for application to tissue, distinct from a first energy, and a second docking station having a second docking port including a second data contact and a second power contact, wherein the second energy generator module is slidably movable to electrically engage with the power contact and the data contact, and the second energy generator module is slidably movable to disengage from the electrical engagement with the second power contact and the second data contact. In addition, the modular surgical enclosure also includes a communication bus between the first docking port and the second docking port, configured to facilitate communication between the first energy generator module and the second energy generator module. Referring to Figure 3, an aspect of the present disclosure relating to a modular enclosure 20060 of a hub enabling modular integration of a generator module 20050, a fume extraction module 20054, and a suction / irrigation module 20055 is presented. The modular enclosure 20060 of the hub further facilitates bidirectional communication between modules 20059, 20054, and 20055. The generator module 20050 may be a generator module 20050 comprising integrated unipolar, bipolar, and ultrasonic components supported within a single housing unit that is slidably inserted into the modular enclosure 20060 of the hub. The generator module 20050 may be configured to connect to unipolar device 20051, bipolar device 20052, and ultrasonic device 20053. Alternatively, the generator module 20050 may comprise a series of unipolar generator modules, bipolar generator modules, and / or ultrasonic generator modules that interact via the modular enclosure 20060 of the hub. The modular enclosure 20060 of the hub may be configured to facilitate the insertion of multiple generators and bidirectional communication between generators docked to the modular enclosure 20060 of the hub, so that multiple generators function as a single generator.

[0238] Figure 4 illustrates a surgical data network according to at least one aspect of the present disclosure, having a set of communication hubs configured to connect a set of sensing systems, an environmental sensing system, and a set of other modular devices to the cloud, located in one or more operating rooms, patient recovery rooms, or rooms within a medical facility specifically equipped for surgical procedures.

[0239] As illustrated in Figure 4, the surgical hub system 20060 may include a modular communication hub 20065 configured to connect modular devices located within a medical facility to a cloud-based system (e.g., a cloud computing system 20064 which may include a remote server 20067 coupled to remote storage 20068). The modular communication hub 20065 and devices may be connected within a room in a medical facility specifically equipped for surgical procedures. In one embodiment, the modular communication hub 20065 may include a network hub 20061 and / or a network switch 20062 in communication with a network router 20066. The modular communication hub 20065 may be coupled to a local computer system 20063 to provide local computer processing and data manipulation. The surgical data network associated with the surgical hub system 20060 may be configured as a passive surgical data network, an intelligent surgical data network, or a switching surgical data network. A passive surgical data network acts as a data conduit, enabling data to travel from one device (or segment) to another and to cloud computing resources. An intelligent surgical data network allows traffic passing through the surgical data network to be monitored and includes additional mechanisms that configure each port within the network hub 20061 or network switch 20062. An intelligent surgical data network may be referred to as a manageable hub or switch. The switching hub reads the destination address of each packet and then forwards the packet to the correct port.

[0240] Modular devices 1a-1n located in the operating room may be coupled to a modular communication hub 20065. Network hub 20061 and / or network switch 20062 may be coupled to a network router 20066 to connect devices 1a-1n to a cloud 20064 or a local computer system 20063. Data associated with devices 1a-1n may be transferred to a cloud-based computer via the router for remote data processing and manipulation. Data associated with devices 1a-1n may also be transferred to a local computer system 20063 for local data processing and manipulation. Modular devices 2a-2m located in the same operating room may also be coupled to a network switch 20062. Network switch 20062 may be coupled to network hub 20061 and / or network router 20066 to connect devices 2a-2m to a cloud 20064. Data associated with devices 2a-2m may be transferred to a cloud 20064 via the network router 20066 for data processing and manipulation. Data associated with devices 2a-2m may also be transferred to the local computer system 20063 for local data processing and local operations.

[0241] The wearable sensing system 20011 may include one or more sensing systems 20069. The sensing systems 20069 may include a surgeon sensing system and / or a patient sensing system. One or more sensing systems 20069 may be in communication with the computer system 20063 or cloud server 20067 of the surgical hub system 20060, either directly via one of the network routers 20066, or via a network hub 20061 or network switch 20062 that is in communication with the network router 20066.

[0242] The sensing system 20069 may be coupled to a network router 20066 to connect the sensing system 20069 to a local computer system 20063 and / or a cloud computing system 20064. Data associated with the sensing system 20069 may be transferred to the cloud computing system 20064 via the network router 20066 for data processing and manipulation. Data associated with the sensing system 20069 may also be transferred to the local computer system 20063 for local data processing and local manipulation.

[0243] As illustrated in Figure 4, the surgical hub system 20060 can be expanded by interconnecting multiple network hubs 20061 and / or multiple network switches 20062 with multiple network routers 20066. The modular communication hub 20065 can be housed in a modular control tower configured to accept multiple devices 1a-1n / 2a-2m. The local computer system 20063 can also be housed in the modular control tower. The modular communication hub 20065 can be connected to a display 20068 to display images acquired by some of the devices 1a-1n / 2a-2m, for example, during a surgical procedure. In various embodiments, devices 1a-1n / 2a-2m may include a variety of modules, among other modular devices that can be connected to the modular communication hub 20065 of the surgical data network, such as imaging modules coupled to an endoscope, generator modules coupled to energy-based surgical devices, smoke extraction modules, suction / irrigation modules, communication modules, processor modules, storage arrays, surgical devices coupled to displays, and / or non-contact sensor modules.

[0244] In one embodiment, the surgical hub system 20060 illustrated in Figure 4 may include a combination of a network hub, network switch, and network router connecting devices 1a-1n / 2a-2m or sensing systems 20069 to a cloud-based system 20064. One or more of the devices 1a-1n / 2a-2m or sensing systems 20069 coupled to the network hub 20061 or network switch 20062 may collect data in real time and transfer the data to a cloud computer for data processing and manipulation. It will be understood that cloud computing relies on sharing computing resources rather than having local servers or personal devices to handle software applications. The term “cloud” may be used as a metaphor for “Internet,” but the term is not limited to that. Accordingly, the term “cloud computing” may be used herein to refer to “a type of internet-based computing” in which different services such as servers, storage, and applications are delivered via the internet to a modular communication hub 20065 and / or computer system 20063 located within an operating room (e.g., a fixed, mobile, temporary, or on-site operating room or space), and to devices connected to the modular communication hub 20065 and / or computer system 20063. The cloud infrastructure may be maintained by a cloud service provider. In this context, the cloud service provider may be an entity that coordinates the use and control of devices 1a-1n / 2a-2m located within one or more operating rooms. The cloud computing service can perform numerous calculations based on data collected by smart surgical instruments, robots, sensing systems, and other computerized devices located within the operating room. The hub hardware enables multiple devices, sensing systems, and / or connections to connect to a computer that communicates with cloud computing resources and storage.

[0245] By applying cloud computing data processing technology to data collected by devices 1a-1n / 2a-2m, surgical data networks can provide improved surgical outcomes, reduced costs, and increased patient satisfaction. At least some of devices 1a-1n / 2a-2m may be employed to observe the condition of tissue after tissue sealing and cutting procedures and to assess leakage or perfusion of sealed tissue. At least some of devices 1a-1n / 2a-2m may be employed to examine data, including images of body tissue samples, for diagnostic purposes using cloud-based computing to identify pathologies such as the effects of disease. Such data may include tissue localization and margin confirmation, as well as phenotype. At least some of devices 1a-1n / 2a-2m may be employed to identify anatomical structures of the body using various sensors integrated with imaging devices and techniques such as overlaying images captured by multiple imaging devices. Data collected by devices 1a-1n / 2a-2m, including image data, may be transferred to the cloud computing system 20064 or the local computer system 20063, or both, for data processing and manipulation, including image processing and manipulation. The data may be analyzed to improve surgical outcomes by determining whether further treatments, such as endoscopic interventions, emerging technologies, targeted radiation, targeted interventions, and the application of precision robots, can be performed on tissue-specific sites and conditions. Such data analysis may further employ prognostic analysis processing, and the use of standardized methods may provide useful feedback for either confirming surgical treatment and surgeon behavior, or suggesting modifications to surgical treatment and surgeon behavior.

[0246] By applying cloud computing data processing technology to measurement data collected by sensing systems 20069, surgical data networks can provide improved surgical outcomes, reduced costs, and increased patient satisfaction. At least some of the sensing systems 20069 may be employed to assess the physiological state of a surgeon performing surgery on a patient, or a patient being prepared for a surgical procedure, or a patient recovering after a surgical procedure. Cloud-based computing systems 20064 may be used to monitor biomarkers associated with the surgeon or patient in real time, generate surgical plans based at least on measurement data collected before the surgical procedure, provide control signals to surgical instruments during the surgical procedure, and notify the patient of complications during the postoperative period.

[0247] Operating room devices 1a-1n may be connected to the modular communication hub 20065 via a wired or wireless channel, depending on the configuration of devices 1a-1n with respect to the network hub 20061. In one embodiment, the network hub 20061 may be implemented as a local network broadcast device operating on the physical layer of the Open System Interconnection (OSI) model. The network hub can provide connectivity to devices 1a-1n located within the same operating room network. The network hub 20061 may collect data in packet form and transmit that data to the router in half-duplex mode. The network hub 20061 may not store any media access control / Internet Protocol (MAC / Internet Protocol, IP) for transferring device data. Only one of devices 1a-1n can transmit data through the network hub 20061 at a time. Network hub 20061 may lack routing tables or intelligence regarding where to send information, and broadcasts all network data to remote servers 20067 of cloud computing system 20064 through each connection. While network hub 20061 can detect basic network errors such as collisions, broadcasting all information to multiple ports poses a security risk and can cause bottlenecks.

[0248] Operating room devices 2a-2m can be connected to network switch 20062 via a wired or wireless channel. Network switch 20062 operates at the data link layer of the OSI model. Network switch 20062 may be a multicast device for connecting devices 2a-2m located in the same operating room to a network. Network switch 20062 can transmit data in the form of frames to network router 20066 and may operate in full-duplex mode. Multiple devices 2a-2m can transmit data simultaneously through network switch 20062. Network switch 20062 stores and uses the MAC addresses of devices 2a-2m to transfer data.

[0249] Network hub 20061 and / or network switch 20062 may be coupled to network router 20066 to connect to cloud computing system 20064. Network router 20066 operates at the network layer of the OSI model. Network router 20066 creates a path for transmitting data packets received from network hub 20061 and / or network switch 20062 to cloud-based computing resources for further processing and manipulation of data collected by any one of all devices 1a-1n / 2a-2m and wearable sensing system 20011. Network router 20066 may be employed to connect two or more different networks located in different locations, for example, different networks located in different operating rooms of the same medical facility or different operating rooms of different medical facilities. Network router 20066 can transmit data in packet form to cloud computing system 20064 and operates in full-duplex mode. Multiple devices can transmit data simultaneously. Network router 20066 may use IP addresses to transfer data.

[0250] In one embodiment, the network hub 20061 may be implemented as a USB hub that allows multiple USB devices to be connected to a host computer. The USB hub may extend a single USB port into several layers so that there are more ports available for connecting devices to the host system computer. The network hub 20061 may include wired or wireless functionality for receiving information via a wired or wireless channel. In one embodiment, a wireless USB short-range high-bandwidth wireless communication protocol may be employed for communication between devices 1a-1n and devices 2a-2m located in the operating room.

[0251] In the embodiment, the operating room devices 1a-1n / 2a-2m and / or sensing system 20069 can exchange data over short distances from fixed and mobile devices (using short-wavelength UHF radio waves in the 2.4-2.485 GHz ISM band) and communicate with the modular communication hub 20065 via the Bluetooth wireless technology standard to build a personal area network (PAN). The operating room devices 1a-1n / 2a-2m and / or sensing systems 20069 may communicate with the modular communication hub 20065 via several wireless or wired communication standards or protocols, including but not limited to Bluetooth, Low-Energy Bluetooth, near-field communication (NFC), Wi-Fi (IEEE 802.11 family), WiMAX (IEEE 802.16 family), IEEE 802.20, new radio (NR), Long-Term Evolution (LTE), and Ev-DO, HSPA+, HSDPA+, HSUPA+, EDGE, GSM, GPRS, CDMA, TDMA, DECT, and their Ethernet derivatives, as well as any other wireless and wired protocols designated as 3G, 4G, 5G, and later. The computing module may include multiple communication modules. For example, the first communication module may be dedicated to short-range wireless communication such as Wi-Fi, Bluetooth Low-Energy Bluetooth, and Bluetooth Smart, while the second communication module may be dedicated to long-range wireless communication such as GPS, EDGE, GPRS, CDMA, WiMAX, LTE, Ev-DO, HSPA+, HSDPA+, HSUPA+, EDGE, GSM, GPRS, CDMA, and TDMA.

[0252] The modular communication hub 20065 may serve as a central connection for one or more of the operating room devices 1a-1n / 2a-2m and / or sensing systems 20069, and may handle data types known as frames. Frames may carry data generated by the devices 1a-1n / 2a-2m and / or sensing systems 20069. Once a frame is received by the modular communication hub 20065, it may be amplified and / or transmitted to a network router 20066, which may transfer the data to a cloud computing system 20064 or a local computer system 20063 by using some wireless or wired communication standards or protocols as described herein.

[0253] The modular communication hub 20065 can be used as a standalone device or connected to compatible network hubs 20061 and network switches 20062 to form a larger network. The modular communication hub 20065 is generally easy to install, configure, and maintain, making it a good choice for networking operating room devices 1a-1n / 2a-2m.

[0254] Figure 5 illustrates a computer-implemented bidirectional surgical system 20070, which may be part of the surgeon monitoring system 20002. The computer-implemented bidirectional surgical system 20070 is similar in many respects to the surgeon monitoring system 20002. For example, the computer-implemented bidirectional surgical system 20070 may include one or more surgical subsystems 20072 that are similar in many respects to the surgeon monitoring system 20002. Each sub-surgical system 20072 includes at least one surgical hub 20076 in communication with a cloud computing system 20064, which may include a remote server 20077 and remote storage 20078. In one embodiment, the computer-implemented bidirectional surgical system 20070 may include a modular control tower 20085 connected to multiple operating room devices, such as sensing systems (e.g., surgeon sensing system 20002 and / or patient sensing system 20003), intelligent surgical instruments, robots, and other computerized devices located in the operating room. As shown in Figure 6A, the modular control tower 20085 may include a modular communication hub 20065 coupled to the local computing system 20063.

[0255] As illustrated in the embodiment of Figure 5, the modular control tower 20085 may be coupled to an imaging module 20088 which can be coupled to an endoscope 20087, a generator module 20090 which can be coupled to an energy device 20089, a fume exhaust module 20091, a suction / irrigation module 20092, a communication module 20097, a processor module 20093, a storage array 20094, smart devices / instruments 20095 which can be optionally coupled to displays 20086 and 20084, respectively, and a non-contact sensor module 20096. The modular control tower 20085 may also be in communication with one or more sensing systems 20069 and an environmental sensing system 20015. The sensing system 20069 may be connected to the modular control tower 20085 directly via a router or via the communication module 20097. Operating room devices may be coupled to cloud computing resources and data storage via the modular control tower 20085. The robotic surgery hub 20082 may also be connected to the modular control tower 20085 and cloud computing resources. In particular, devices / instruments 20095 or 20084 and human interface systems 20080 may be coupled to the modular control tower 20085 via wired or wireless communication standards or protocols as described herein. The human interface system 20080 may include a display subsystem and a notification subsystem. The modular control tower 20085 may be coupled to a hub display 20081 (e.g., a monitor, screen) to display and overlay images received from the imaging module 20088, device / instrument display 20086 and / or other human interface system systems 20080. The hub display 20081 may also display data received from devices connected to the modular control tower 20085, along with images and overlay images.

[0256] Figure 6A illustrates a surgical hub 20076 comprising multiple modules coupled to a modular control tower 20085. As shown in Figure 6A, the surgical hub 20076 may be connected to a generator module 20090, a smoke exhaust module 20091, a suction / irrigation module 20092, and a communication module 20097. The modular control tower 20085 may comprise a modular communication hub 20065, e.g., a network connectivity device, and a computer system 20063 for providing, e.g., local wireless connectivity with sensing systems, local processing, complexity monitoring, visualization, and imaging. As shown in Figure 6A, the modular communication hub 20065 may extend to several modules (e.g., devices) and several sensing systems 20069 that can be connected to the modular communication hub 20065, and may be connected in a configuration (e.g., a tiered configuration) for transferring measurement data associated with the sensing systems 20069 to the computer system 20063, cloud computing resources, or both. As shown in Figure 6A, each of the network hubs / switches 20061 / 20062 within the modular communication hub 20065 may include three downstream ports and one upstream port. The upstream network hub / switch may be connected to processor 20102 to provide communication connectivity to cloud computing resources and local display 20108. At least one of the network / hub switches 20061 / 20062 within the modular communication hub 20065 may have at least one wireless interface to provide communication connectivity between sensing system 20069 and / or device 20095 and cloud computing system 20064. Communication to cloud computing system 20064 may be via either a wired or wireless communication channel.

[0257] The surgical hub 20076 may employ a non-contact sensor module 20096 to measure the dimensions of an operating room and generate a map of the operating room using either an ultrasonic or laser-type non-contact measuring device. The ultrasonic-based non-contact sensor module may scan an operating room by transmitting bursts of ultrasound and receiving echoes as the ultrasound bursts reflect off the surrounding walls of the operating room, as described under the title "Surgical Hub Spatial Awareness Within an Operating Room" of U.S. Provisional Patent Application No. 62 / 611,341, filed December 28, 2017, titled "INTERACTIVE SURGICAL PLATFORM," which is incorporated herein by reference in its entirety. The sensor module is configured to determine the size of the operating room and adjust the Bluetooth pairing distance limit. The laser-based non-contact sensor module may scan an operating room, for example, by transmitting laser light pulses, receiving laser light pulses reflected off the outer walls of the operating room, comparing the phase of the transmitted pulses with the received pulses to determine the size of the operating room and adjust the Bluetooth pairing distance limit.

[0258] Computer system 20063 may comprise a processor 20102 and a network interface 20100. The processor 20102 may be coupled via a system bus to a communication module 20103, storage 20104, memory 20105, non-volatile memory 20106, and an input / output (I / O) interface 20107. The system bus can be any of several types of bus structures, including a memory bus or memory controller, peripheral bus or external bus, and / or local bus, using any various available bus architectures, including, but not limited to, 9-bit buses, Industrial Standard Architecture (ISA), Micro-Charmel Architecture (MSA), Extended ISA (EISA), Intelligent Drive Electronics (IDE), VESA Local Bus (VLB), Peripheral Component Interconnect (PCI), USB, Advanced Graphics Port (AGP), Personal Computer Memory Card International Association (PCMCIA) bus, Small Computer Systems Interface (SCSI), or any other dedicated bus.

[0259] Processor 20102 can be any single-core or multi-core processor, such as those known by the trade name ARM Cortex from Texas Instruments. In one embodiment, the processor may be, for example, the LM4F230H5QR ARM Cortex-M4F processor core available from Texas Instruments, which includes on-chip memory of 256KB single-cycle flash memory or other non-volatile memory up to 40MHz, a prefetch buffer to improve performance to above 40MHz, 32KB single-cycle serial random access memory (SRAM), internal read-only memory (ROM) with StellarisWare® software, 2KB electrically erasable programmable read-only memory (EEPROM), and / or one or more pulse width modulation (PWM) modules, one or more quadrature encoder input (QEI) analogs, and one or more 12-bit analog-to-digital converters (ADCs) with 12 analog input channels, the details of which are available in the product datasheet.

[0260] In the embodiment, the 20102 processor may include a safety controller, which may include two controller-based families, such as the TMS570 and RM4x, also known by the trade names Hercules ARM Cortex R4, from Texas Instruments. The safety controller may be configured specifically for IEC61508 and ISO26262 safety limit applications, among other things, to provide a highly integrated safety mechanism while offering scalable performance, connectivity, and memory options.

[0261] System memory can be categorized into volatile and non-volatile memory. The basic input / output system (BIOS), which includes basic routines for transferring information between elements within the computer system during startup, is stored in non-volatile memory. Examples of non-volatile memory include ROM, programmable ROM (PROM), electrically programmable ROM (EPROM), EEPROM, or flash memory. Examples of volatile memory include random-access memory (RAM), which functions as external cache memory. Furthermore, RAM is available in many forms, such as SRAM, dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct rambus RAM (DRRAM).

[0262] Computer System 20063 may also include removable / non-removable, volatile / non-volatile computer storage media, such as disk storage devices. Examples of disk storage devices include, but are not limited to, magnetic disk drives, floppy disk drives, tape drives, Jaz drives, Zip drives, LS-60 drives, flash memory cards, or memory sticks. In addition, disk storage devices may include the above-mentioned storage media independently or in combination with other storage media. Examples of other storage media include, but are not limited to, optical disk drives such as compact disc ROM devices (CD-ROM), compact disc recordable drives (CD-R drives), compact disc rewritable drives (CD-RW drives), or digital versatile disc ROM drives (DVD-ROM). Removable or non-removable interfaces may be employed to facilitate connection of disk storage devices to the system bus.

[0263] It should be understood that computer system 20063 may include software that acts as an intermediary between the user and the basic computer resources described in a suitable operating environment. Such software may include an operating system. An operating system, which may be stored on disk storage, may function to control and allocate the resources of the computer system. System applications may leverage resource management by the operating system through program modules and program data stored either in system memory or on disk storage. It should be understood that the various components described herein can be implemented in various operating systems or combinations of operating systems.

[0264] The user can input commands or information to the computer system 20063 via input devices coupled to the I / O interface 20107. Examples of input devices include, but are not limited to, pointing devices such as mice, trackballs, styluses, and touchpads; keyboards; microphones; joysticks; gamepads; satellite receivers; scanners; TV tuner cards; digital cameras; digital video cameras; and webcams. These and other input devices connect to the processor 20102 via interface ports and the system bus. Examples of interface ports include serial ports, parallel ports, game ports, and USB. Output devices utilize some of the same types of ports as the input devices. For example, a USB port can be used to provide input to the computer system 20063, and information can be output from the computer system 20063 to an output device. Output adapters may be provided to illustrate that several output devices, such as monitors, displays, speakers, and printers, may exist, among others that may require special adapters. Examples of output adapters include video and audio cards that provide a means of connection between the output device and the system bus; however, this is illustrative and not limiting. Note that other devices and / or systems of devices, such as remote computers, may provide both input and output functions.

[0265] Computer System 20063 can operate in a networked environment using logical connections to one or more remote computers, such as cloud computers, or to local computers. Remote cloud computers may be personal computers, servers, routers, network PCs, workstations, microprocessor-based devices, peer devices, or other common network nodes, but typically include many or all of the elements described in relation to computer systems. For brevity, only memory storage devices are illustrated in relation to remote computers. Remote computers may be logically connected to the computer system via a network interface, and subsequently physically connected via a communication interface. Network interfaces may include communication networks such as local area networks (LANs) and wide area networks (WANs). LAN technologies may include Fiber Distributed Data Interfaces (FDDI), Copper Distributed Data Interfaces (CDDI), Ethernet / IEEE 802.3, and Token Ring / IEEE 802.5. WAN technologies include, but are not limited to, point-to-point links, integrated services digital networks (ISDN) and their variations, circuit-switched networks, packet-switched networks, and digital subscriber lines (DSL).

[0266] In various embodiments, the computer system 20063, imaging module 20088, and / or human interface system 20080 in Figures 4, 6A, and 6B, and / or processor module 20093 in Figures 5 and 6A may comprise an image processor, image processing engine, media processor, or any dedicated digital signal processor (DSP) used for processing digital images. The image processor may employ parallel computing using single-instruction, multiple data (SIMD) or multiple-instruction, multiple data (MIMD) techniques to increase speed and efficiency. The digital image processing engine can perform a variety of tasks. The image processor may be a system on a chip with a multi-core processor architecture.

[0267] The communication connection section may refer to the hardware / software used to connect the network interface to the bus. While the communication connection section is shown internally within computer system 20063 for illustrative purposes, it can also be external to computer system 20063. Hardware / software required for connection to the network interface may include, for illustrative purposes only, internal and external technologies such as modems including typical telephone-grade modems, cable modems, fiber optic modems, and DSL modems, ISDN adapters, and Ethernet cards. In some embodiments, the network interface may also be provided using an RF interface.

[0268] Figure 6B illustrates an example of a wearable monitoring system, such as a controlled patient monitoring system. A controlled patient monitoring system may be a sensing system used to monitor a set of patient biomarkers while a patient is in a medical facility. The controlled patient monitoring system may be deployed for pre-surgical patient monitoring while the patient is preparing for a surgical procedure, intra-surgical monitoring while the patient is undergoing surgery, or post-surgical monitoring, for example, while the patient is recovering. As illustrated in Figure 6B, the controlled patient monitoring system may include a surgical hub system 20076, which may include one or more routers 20066 and a computer system 20063 of a modular communication hub 20065. Routers 20065 may include wireless routers, wired switches, wired routers, wired network hubs, or wireless network hubs. In the embodiment, routers 20065 may be part of the infrastructure. Computing system 20063 may provide local processing for monitoring various biomarkers associated with the patient or surgeon, and a notification mechanism for informing the patient and / or healthcare provider (HCP) that milestones (e.g., recovery milestones) are being met or complications are being detected. Computing system 20063 of surgical hub system 20076 may also be used to generate notifications, for example, a severity level associated with a notification that a complication has been detected.

[0269] The computing system 20063 in Figures 4 and 6B, the computing device 20200 in Figure 6C, and the hub / computing device 20243 in Figures 7B, 7C, or 7D may be a surgical computing system or hub device, laptop, tablet, smartphone, etc.

[0270] As shown in Figure 6B, a pair of sensing systems 20069 and / or an environmental sensing system 20015 (as described in Figure 2A) may be connected to the surgical hub system 20076 via a router 20065. The router 20065 may also provide a direct communication connection between the sensing system 20069 and the cloud computing system 20064 without involving, for example, the local computer system 20063 of the surgical hub system 20076. Communication from the surgical hub system 20076 to the cloud 20064 may be conducted via either a wired or wireless communication channel.

[0271] As shown in Figure 6B, the computer system 20063 may include a processor 20102 and a network interface 20100. The processor 20102 may be coupled via a system bus to a radio frequency (RF) interface or communication module 20103, storage 20104, memory 20105, non-volatile memory 20106, and input / output interface 20107, as shown in Figure 6A. The computer system 20063 may be connected to a local display unit 20108. In some embodiments, the display unit 20108 may be replaced by a HID. Further details regarding the hardware and software components of the computer system are provided in Figure 6A.

[0272] As shown in Figure 6B, the sensing system 20069 may include a processor 20110. The processor 20110 may be coupled via a system bus to a radio frequency (RF) interface 20114, storage 20113, memory (e.g., non-volatile memory) 20112, and an I / O interface 20111. The system bus can be any of several types of bus structures, including a memory bus or memory controller, a peripheral bus or external bus, and / or a local bus, as described herein. The processor 20110 may be any single-core or multi-core processor as described herein.

[0273] It should be understood that the sensing system 20069 may include software that functions as a mediator between the sensing system user and the basic computer resources described in a suitable operating environment. Such software may include an operating system. The operating system that may be stored on a disk storage device may function to control and allocate the resources of the computer system. System applications may utilize the resource management by the operating system via program modules and program data stored either in the system memory or on the disk storage device. It should be understood that the various components described herein can be implemented with various operating systems or combinations of operating systems.

[0274] The sensing system 20069 may be connected to a human interface system 20115. The human interface system 20115 may be a touch screen display. The human interface system 20115 includes a human interface display for displaying information associated with surgeon biomarkers and / or patient biomarkers, displays prompts for user actions by the patient or surgeon, or may display notifications to the patient or surgeon indicating information about recovery milestones or complications. The human interface system 20115 may be used to receive input from the patient or surgeon. Other human interface systems may be connected to the sensing system 20069 via the I / O interface 20111. For example, the human interface device 20115 may include a device for providing tactile feedback as a mechanism to prompt the user to pay attention to a notification displayed on the display unit.

[0275] The sensing system 20069 may operate in a networked environment using logical connections to one or more remote computers, such as cloud computers, or to local computers. Remote cloud computers may include personal computers, servers, routers, network PCs, workstations, microprocessor-based devices, peer devices, or other common network nodes, but typically include many or all of the elements described with respect to computer systems. Remote computers may be logically connected to computer systems via network interfaces. Network interfaces may include communication networks such as local area networks (LANs), wide area networks (WANs), and / or mobile networks. LAN technologies may include fiber optic distributed data interfaces (FDDI), copper distributed data interfaces (CDDI), Ethernet / IEEE 802.3, Token Ring / IEEE 802.5, and Wi-Fi / IEEE 802.11. WAN technologies may include, but are not limited to, point-to-point links, integrated service digital networks (ISDN) and their variations, packet-switched networks, and digital subscriber lines (DSL). A mobile network may include communication links based on one or more of the following mobile communication protocols: GSM / GPRS / EDGE (2G), UMTS / HSPA (3G), Long-Term Evolution (LTE) or 4G, LTE Advanced (LTE-A), new radio (NR) or 5G, etc.

[0276] Figure 6C illustrates an exemplary uncontrolled patient monitoring system, for example, when the patient is away from the medical facility. An uncontrolled patient monitoring system may be used for pre-surgical patient monitoring when the patient is being prepared for a surgical procedure but is away from the medical facility, or for example, in post-surgical monitoring when the patient is away from the medical facility recovering.

[0277] As illustrated in FIG. 6C, one or more sensing systems 20069 are in communication with a computing device 20200, such as a personal computer, laptop, tablet, or smartphone. The computing system 20200 may provide processing for monitoring various biomarkers associated with a patient, a notification mechanism indicating that milestones (e.g., recovery milestones) are met, or that a complication has been detected. The computing system 20200 may also provide instructions for the user of the sensing system to follow. Communication between the sensing system 20069 and the computing device 20200 may be established directly using a wireless protocol as described herein or via a wireless router / hub 20211.

[0278] As shown in FIG. 6C, the sensing system 20069 may be connected to the computing device 20200 via a router 20211. The router 20211 may include, for example, a wireless router, a wired switch, a wired router, a wired network hub, or a wireless network hub. The router 20211 may provide, for example, a direct communication connection between the sensing system 20069 and the cloud server 20064 without involving the local computing device 20200. The computing device 20200 may be in communication with the cloud server 20064. For example, the computing device 20200 may be in communication with the cloud 20064 via a wired communication channel or a wireless communication channel. In an embodiment, the sensing system 20069 may be in direct communication with the cloud over a cellular network, for example, via a cellular base station 20210.

[0279] As shown in Figure 6C, the computing device 20200 may include a processor 20203 and a network interface or RF interface 20201. The processor 20203 may be coupled via a system bus to storage 20202, memory 20212, non-volatile memory 20213, and input / output interface 20204, as shown in Figures 6A and 6B. Details of the hardware and software components of the computer system are provided in Figure 6A. The computing device 20200 may include a set of sensors, e.g., sensor #1 20205, sensor #2 20206 through sensor #n 20207. These sensors may be part of the computing device 20200 and may be used to measure one or more attributes associated with the patient. The attributes may provide context for biomarker measurements performed by one of the sensing systems 20069. For example, sensor #1 may be an accelerometer that can be used to measure acceleration forces to sense movement or vibration associated with the patient. In the embodiments, sensors 20205 to 20207 may include one or more of the following: pressure sensors, altimeters, thermometers, lidars, or equivalents.

[0280] As shown in Figure 6B, the sensing system 20069 may include a processor, a radio frequency interface, storage, memory or non-volatile memory, and an input / output interface via a system bus, as described in Figure 6A. The sensing system may include a sensor unit, a processing unit, and a communication unit, as described in Figures 7B to 7D. The system bus may be any of several types of bus structures, including a memory bus or memory controller, a peripheral bus or external bus, and / or a local bus, as described herein. The processor may be any single-core or multi-core processor, as described herein.

[0281] The sensing system 20069 may be in communication with the human interface system 20215. The human interface system 20215 may be a touchscreen display. The human interface system 20215 may be used to display information associated with patient biomarkers, to display prompts for user actions by the patient, or to display notifications to the patient indicating information about recovery milestones or complications. The human interface system 20215 may be used to receive input from the patient. Other human interface systems may be connected to the sensing system 20069 via I / O interfaces. For example, a human interface system may include a device for providing haptic feedback as a mechanism to prompt the user to pay attention to notifications that may be displayed on the display unit. The sensing system 20069 may operate in a networked environment using logical connections to one or more remote computers, such as cloud computers, or to local computers, as shown in Figure 6B.

[0282] Figure 7A illustrates a logic diagram of a control system 20220 for a surgical instrument or surgical tool according to one or more embodiments of the present disclosure. The surgical instrument or surgical tool may be configurable. The surgical instrument may include surgical fixation devices specific to the hand procedure, such as imaging devices, surgical staplers, energy devices, endocutter devices, or equivalents. For example, the surgical instrument may include any of the following: electric staplers, electric stapler generators, energy devices, advanced energy devices, advanced energy jaw devices, endocutter clamps, energy device generators, intraoperative imaging systems, smoke exhausters, suction and irrigation devices, air supply systems, or equivalents. The system 20220 may include a control circuit. The control circuit may include a microcontroller 20221 having a processor 20222 and memory 20223. For example, one or more of the sensors 20225, 20226, and 20227 provide real-time feedback to the processor 20222. Motor 20230, driven by motor driver 20229, drives the I-beam knife element by operably coupling a longitudinally movable displacement member. A tracking system 20228 may be configured to determine the position of the longitudinally movable displacement member. Position information may be provided to processor 20222, which can be programmed or configured to determine the position of the longitudinally movable drive member, as well as the positions of the launch member, launch bar, and I-beam knife element. Additional motors may be provided to the tool driver interface to control I-beam firing, occluder advancement, shaft rotation, and joint movement. Display 20224 may display various operating states of the instrument and may include touchscreen functionality for data input. Information displayed on display 20224 may be overlaid with images acquired via the endoscopic imaging module.

[0283] In one embodiment, the microcontroller 20221 may be any single-core or multi-core processor, such as those known by the trade name ARM Cortex from Texas Instruments. In one embodiment, the main microcontroller 20221 may be, for example, an LM4F230H5QR ARM Cortex-M4F processor core available from Texas Instruments, comprising on-chip memory of 256KB single-cycle flash memory or other non-volatile memory up to 40MHz, a prefetch buffer to improve performance to above 40MHz, 32KB single-cycle SRAM, internal ROM with StellarisWare® software, 2KB EEPROM, one or more PWM modules, one or more QEI analogs, and / or one or more 12-bit ADCs having 12 analog input channels, details of which are available in the product datasheet.

[0284] In one embodiment, the microcontroller 20221 may include safety controllers, including two controller-based families such as the TMS570 and RM4x, also known by the trade names Hercules ARM Cortex R4, from Texas Instruments. The safety controllers may be configured specifically for IEC61508 and ISO26262 safety limit applications, in particular, to provide advanced integrated safety mechanisms while offering scalable performance, connectivity, and memory options.

[0285] Microcontroller 20221 can be programmed to perform various functions, including precise control of the speed and position of the knife and joint motion systems. In one embodiment, microcontroller 20221 may include a processor 20222 and memory 20223. Electric motor 20230 may be a brushed direct current (DC) motor having a gearbox and a mechanical link to the joint motion or knife system. In one embodiment, motor driver 20229 may be A3941, available from Allegro Microsystems, Inc. Other motor drivers can be readily substituted for use in tracking system 20228 with an absolute positioning system. A detailed description of the absolute positioning system is provided in U.S. Patent Application Publication 2017 / 0296213, published October 19, 2017, entitled “SYSTEMS AND METHODS FOR CONTROLLING A SURGICAL STAPLING AND CUTTING INSTRUMENT,” which is incorporated herein by reference in its entirety.

[0286] The microcontroller 20221 can be programmed to provide precise control over the velocity and position of displaced members and joint motion systems. The microcontroller 20221 can be configured to calculate responses within its software. The calculated response can be compared to the measured response of the actual system to obtain an "observed" response, which is used for actual feedback decisions. The observed response may be a well-adjusted value that balances the smooth and continuous nature of the simulated response with the measured response, and this can detect external influences on the system.

[0287] In some embodiments, the motor 20230 may be controlled by a motor driver 20229 and may be employed by a launching system for a surgical instrument or surgical tool. In various forms, the motor 20230 may be, for example, a brushed DC-driven motor having a maximum rotational speed of approximately 25,000 RPM. In some embodiments, the motor 20230 may be a brushless motor, a cordless motor, a synchronous motor, a stepper motor, or any other suitable electric motor. The motor driver 20229 may include, for example, an H-bridge driver including field-effect transistors (FETs). The motor 20230 may be powered by a power supply assembly removably mounted on a handle assembly or tool housing to supply control power to a surgical instrument or surgical tool. The power supply assembly may comprise a battery that may include a number of battery cells connected in series, which can be used as a power source to power the surgical instrument or tool. Under certain circumstances, the battery cells of the power supply assembly may be replaceable and / or rechargeable. In at least one embodiment, the battery cell may be a lithium-ion battery that can be coupled to and detached from a power supply assembly.

[0288] The motor driver 20229 may be the A3941, available from Allegro Microsystems, Inc. The A3941 may be a full-bridge controller for use with an external N-channel power metal-oxide-semiconductor field-effect transistor (MOSFET), specifically designed for inductive loads such as brushed DC motors. The driver 20229 may feature a proprietary charge pump regulator that can provide full (>10V) gate drive for battery voltages up to 7V and allow the A3941 to operate with reduced gate drive down to 5.5V. Bootstrap capacitors may be employed to provide the above battery supply voltage required for the N-channel MOSFET. An internal charge pump for high-side drive enables DC (100% duty cycle) operation. The full bridge may be driven in fast or slow decay mode using diodes or synchronous rectification. In slow decay mode, current recirculation is possible by either the high-side or low-side FET. The power FET may be protected from shoot-through by a dead time adjustable with resistors. The integrated diagnostics indicate undervoltage, overtemperature, and power bridge anomalies and can be configured to protect power MOSFETs under most short-circuit conditions. Other motor drivers can be readily substituted for use in the 20228 tracking system with an absolute positioning system.

[0289] The tracking system 20228 may comprise a controlled motor drive circuit array comprising a position sensor 20225 according to one aspect of the present disclosure. The position sensor 20225 for the absolute positioning system may provide a unique position signal corresponding to the position of the displacement member. In some embodiments, the displacement member may represent a longitudinally movable drive member comprising a rack of drive teeth for meshing and engaging with a corresponding drive gear of a gear reducer assembly. In some embodiments, the displacement member may represent a launch member which may be adapted and configured to include a rack of drive teeth. In some embodiments, the displacement member may represent a launch bar or an I-beam, each of which may be adapted and configured to include a rack of drive teeth. Thus, as used herein, the term displacement member may be used to refer to any movable member of a surgical instrument or tool, such as a drive member, launch member, launch bar, I-beam, or any element which may be displaced. In one aspect, the longitudinally movable drive member may be coupled to a launch member, launch bar, and I-beam. Thus, the absolute positioning system can, in practice, track the linear displacement of the I-beam by tracking the linear displacement of the longitudinally movable drive member. In various embodiments, the displacement member may be coupled to any position sensor 20225 suitable for measuring linear displacement. Thus, a longitudinally movable drive member, launch member, launch bar, or I-beam, or a combination thereof, may be coupled to any suitable linear displacement sensor. The linear displacement sensor may include contact-type or non-contact-type displacement sensors.A linear displacement sensor may include a linear variable differential transformer (LVDT), a differential variable reluctance transducer (DVRT), a slide potentiometer, a magnetic sensing system comprising a movable magnet and a series of linearly arranged Hall effect sensors, a magnetic sensing system comprising a fixed magnet and a series of movable linearly arranged Hall effect sensors, a light sensing system comprising a movable light source and a series of linearly arranged photodiodes or photodetectors, a light sensing system comprising a fixed light source and a series of movable linearly arranged photodiodes or photodetectors, or any combination thereof.

[0290] The electric motor 20230 may include a rotary shaft that operably interfaces with a gear assembly mounted on a displacement member in a meshing engagement with a pair of drive teeth on a rack. A sensor element may be operably coupled to the gear assembly such that one rotation of the position sensor 20225 element corresponds to some linear longitudinal translation of the displacement member. The gearing and sensor array may be connected to a linear actuator by a rack and pinion array, or to a rotary actuator by a spur gear or other connection. A power supply provides power to the absolute positioning system, and an output indicator may display the output of the absolute positioning system. The displacement member may represent a longitudinally movable drive member having a rack of drive teeth formed thereon for meshing engagement with the corresponding drive gear of a gear reducer assembly. The displacement member may represent a longitudinally movable launch member, launch bar, I-beam, or a combination thereof.

[0291] One rotation of the sensor element associated with the position sensor 20225 may correspond to a longitudinal linear displacement d1 of the displacement member, where d1 is the longitudinal linear distance the displacement member moves from point "a" to point "b" after one rotation of the sensor element coupled to the displacement member. The sensor array may be connected via a gear reduction that results in the position sensor 20225 completing one or more rotations over the full stroke of the displacement member. The position sensor 20225 may complete multiple rotations over the full stroke of the displacement member.

[0292] To provide a unique position signal exceeding one rotation of the position sensor 20225, a series of switches where n is an integer greater than 1 may be employed alone or in combination with gear reduction. The state of the switches may be fed back to the microcontroller 20221, which applies logic to determine a unique position signal corresponding to the longitudinal linear displacement d1+d2+...dn of the displacement member. The output of the position sensor 20225 is provided to the microcontroller 20221. The position sensor 20225 in sensor arrays may comprise an array of magnetic sensors, analog rotation sensors such as potentiometers, or analog Hall effect elements, which output a unique combination of position signals or values.

[0293] Position sensor 20225 may comprise any number of magnetic sensing elements, such as magnetic sensors classified according to whether they measure the total magnetic field or the vector component of the magnetic field. The techniques used to produce both types of magnetic sensors may involve numerous aspects of physics and electronics. Techniques used to sense magnetic fields include, among others, probe coils, flux gates, optical pumping, nuclear perturbation, SQUID, Hall effect, anisotropic magnetoresistance, colossal magnetoresistance, magnetic tunnel junctions, colossal magnetoimpedance, magnetostrictive / piezoelectric composites, magnetic diodes, magnetic transistors, optical fibers, magneto-optics, and micro-electromechanical system-based magnetic sensors.

[0294] In one embodiment, the position sensor 20225 of a tracking system 20228 equipped with an absolute positioning system may be equipped with a magnetic rotation absolute positioning system. The position sensor 20225 may be implemented as an AS5055EQFT single-chip magnetic rotation position sensor available from Austria Microsystems, AG. The position sensor 20225 is interfaced with a microcontroller 20221 to provide an absolute positioning system. The position sensor 20225 may be a low-voltage, low-power component and may include four Hall effect elements in the area of ​​the position sensor 20225 that may be located above the magnet. A high-resolution ADC and a smart power management controller may also be provided on the chip. A coordinate rotation digital computer (CORDIC) processor, also known as the digit-by-digit method and the Boulder algorithm, may be provided to implement a simple and efficient algorithm for calculating hyperbolic and trigonometric functions that requires only addition, subtraction, bit shifting, and table lookup operations. Angular position, alarm bits, and magnetic field information can be transmitted to the microcontroller 20221 via a standard serial communication interface, such as a serial peripheral interface (SPI). The position sensor 20225 may offer 12-bit or 14-bit resolution. The position sensor 20225 may be an AS5055 chip available in a small QFN 16-pin 4x4x0.85mm package.

[0295] The tracking system 20228, which includes an absolute positioning system, may include and / or be programmed to implement feedback controllers such as PID, state feedback, and adaptive controllers. The power supply converts signals from the feedback controllers into physical inputs to the system, in this case voltage. Other embodiments include PWM of voltage, current, and force. In addition to the position measured by the position sensor 20225, other sensors may be provided to measure physical parameters of the physical system. In some embodiments, other sensors include sensor arrays described in U.S. Patent No. 9,345,481, issued May 24, 2016, entitled "STAPLE CARTRIDGE TISSUE THICKNESS SENSOR SYSTEM," which is incorporated herein by reference in its entirety; U.S. Patent Application Publication No. 2014 / 0263552, published September 18, 2014, entitled "STAPLE CARTRIDGE TISSUE THICKNESS SENSOR SYSTEM," which is incorporated herein by reference in its entirety; and U.S. Patent Application No. 15 / 628,175, filed June 20, 2017, entitled "TECHNIQUES FOR ADAPTIVE CONTROL OF MOTOR VELOCITY OF A SURGICAL STAPLING AND CUTTING INSTRUMENT," which is incorporated herein by reference in its entirety. In a digital signal processing system, the absolute positioning system is coupled to a digital data acquisition system, where the output of the absolute positioning system has a finite resolution and sampling frequency. The absolute positioning system may include comparison and combinational circuits to combine the calculated response with the measured response, using algorithms such as weighted averaging and theoretical control loops that drive the calculated response toward the measured response. The calculated response of a physical system may take into account properties such as mass, inertia, viscous friction, and inductive resistance to predict what the state and output of the physical system will be by knowing the input.

[0296] The absolute positioning system can provide the absolute position of the displacement member when the device is powered on, without requiring the displacement member to be moved back or forward to a reset (zero or home) position, as may be necessary with conventional rotary encoders that simply count the number of forward or backward strokes taken by the motor 20230 to estimate the position of the device actuator, drive bar, knife, or equivalent.

[0297] For example, a sensor 20226, such as a strain gauge or micro-strain gauge, may be configured to measure one or more parameters of an end effector, such as the amplitude of strain applied to the anvil during clamping, where the parameter may indicate the closing force applied to the anvil. The measured strain may be converted into a digital signal and provided to a processor 20222. Alternatively, or in addition to sensor 20226, a sensor 20227, such as a load sensor, may measure the closing force applied to the anvil by the closing drive system. For example, a sensor 20227, such as a load sensor, may measure the firing force applied to the I-beam during the firing stroke of a surgical instrument or surgical tool. The I-beam is configured to engage with a wedge-shaped thread, which is configured to cam upward a staple driver to push the staple out and deform into contact with the anvil. The I-beam may also include a sharp cutting edge that can be used to cut tissue as the I-beam is advanced distally by the firing bar. Alternatively, a current sensor 20231 can be used to measure the current drawn in by the motor 20230. The force required to propel the launching member forward may correspond, for example, to the current drawn in by the motor 20230. The measured force can be converted into a digital signal and provided to the processor 20222.

[0298] In one configuration, a strain gauge sensor 20226 can be used to measure the force applied to tissue by an end effector. A strain gauge can be coupled to the end effector to measure the force exerted by the end effector on the tissue being treated. A system for measuring the force applied to tissue grasped by an end effector can include, for example, a strain gauge sensor 20226 such as a micro strain gauge, and the strain gauge sensor 20226 can be configured to measure one or more parameters of the end effector. In one aspect, the strain gauge sensor 20226 can measure the amplitude or magnitude of the strain exerted on a jaw member of the end effector during a clamping operation, and the amplitude or magnitude of the strain can indicate tissue compression. The measured strain can be converted to a digital signal and provided to a processor 20222 of a microcontroller 20221. A load sensor 20227 can measure the force used to operate a knife element, for example, to cut tissue captured between an anvil and a staple cartridge. A magnetic field sensor can be employed to measure the thickness of the captured tissue. The measurements of the magnetic field sensor can also be converted to a digital signal and provided to the processor 20222.

[0299] The measurements of tissue compression, tissue thickness, and / or the force required to close the end effector on the tissue, respectively measured by sensors 20226, 20227, can be used by the microcontroller 20221 to characterize corresponding values of a selected position of a firing member and / or the velocity of a firing member. In one example, the memory 20223 can store techniques, equations, and / or look-up tables that can be employed by the microcontroller 20221 during evaluation.

[0300] A control system 20220 for a surgical instrument or surgical tool can also include a wired communication circuit or a wireless communication circuit for communicating with a modular communication hub 20065, as shown in FIGS. 5-6A.

[0301] Figure 7B shows an exemplary sensing system 20069. The sensing system may be a surgeon sensing system or a patient sensing system. Sensing system 20069 may include a sensor unit 20235 and a human interface system 20242 that are in communication with a data processing and communication unit 20236. The data processing and communication unit 20236 may include an analog-to-digital converter 20237, a data processing unit 20238, a storage unit 20239, an input / output interface 20241, and a transceiver 20240. Sensing system 20069 may be in communication with a surgical hub or computing device 20243, which is then in communication with a cloud computing system 20244. The cloud computing system 20244 may include a cloud storage system 20078 and one or more cloud servers 20077.

[0302] The sensor unit 20235 may include one or more in vitro or intra vivo sensors for measuring one or more biomarkers. Biomarkers may be measured using one or more sensors, such as blood pH, hydration status, oxygen saturation, core temperature, heart rate, heart rate variability, sweat rate, skin conductance, blood pressure, light exposure, ambient temperature, respiratory rate, cough and sneeze, gastrointestinal motility, gastrointestinal imaging, tissue perfusion pressure, airway bacteria, alcohol consumption, lactate (sweat), peripheral temperature, positivity and optimism, adrenaline (sweat), cortisol (sweat), edema, mycotoxins, VO2 maxima, preoperative pain, airborne chemicals, circulating tumor cells, stress and anxiety, confusion and delirium, physical activity, autonomic tension, circadian rhythm, menstrual cycle, and sleep. These biomarkers may be measured using one or more sensors, such as optical sensors (e.g., photodiodes, photoresistors), mechanical sensors (e.g., motion sensors), acoustic sensors, electrical sensors, electrochemical sensors, thermoelectric sensors, infrared sensors, etc. The sensors may measure the biomarkers described herein using one or more of the following sensing techniques: photoplethysmography, electrocardiography, electroencephalography, colorimetric analysis, impedimentary methods, potentiometric measurements, current measurements, etc.

[0303] As illustrated in Figure 7B, the sensors within the sensor unit 20235 may measure physiological signals (e.g., voltage, current, PPG signal, etc.) associated with a biomarker to be measured. The physiological signals to be measured may depend on the sensing technique used, as described herein. The sensor unit 20235 of the sensing system 20069 may be in communication with the data processing and communication unit 20236. In the embodiment, the sensor unit 20235 may communicate with the data processing and communication unit 20236 using a wireless interface. The data processing and communication unit 20236 may include an analog-to-digital converter (ADC) 20237, a data processing unit 20238, storage 20239, an I / O interface 20241, and an RF transceiver 20240. The data processing unit 20238 may include a processor and a memory unit.

[0304] Sensor unit 20235 may transmit the measured biosignals to ADC 20237 of data processing and communication unit 20236. In the embodiment, the measured physiological signals may be passed through one or more filters (e.g., RC low-pass filters) before being transmitted to the ADC. The ADC may convert the measured physiological signals into measurement data associated with biomarkers. The ADC may pass the measurement data to data processing unit 20238 for processing. In the embodiment, data processing unit 20238 may transmit the measurement data associated with biomarkers to a surgical hub or computing device 20243, which may then transmit the measurement data to a cloud computing system 20244 for further processing. The data processing unit may transmit the measurement data to the surgical hub or computing device 20243 using one of the wireless protocols as described herein. In this embodiment, the data processing unit 20238 may first process the raw measurement data received from the sensor unit and then transmit the processed measurement data to a surgical hub or computing device 20243.

[0305] In the embodiment, the data processing and communication unit 20236 of the sensing system 20069 may receive thresholds associated with biomarkers for monitoring from the surgical hub, computing device 20243, or directly from the cloud server 20077 of the cloud computing system 20244. The data processing unit 20236 may compare the measurement data associated with the biomarker to be monitored with the corresponding threshold received from the surgical hub, computing device 20243, or cloud server 20077. The data processing and communication unit 20236 may send a notification message to HID20242 indicating that the measurement data value has exceeded the threshold. The notification message may include the measurement data associated with the biomarker being monitored. The data processing and communication unit 20236 may send the notification via transmission to the surgical hub or computing device 20243 using one of the following RF protocols: Bluetooth, Bluetooth Low Energy (BLE), Bluetooth Smart, Zigbee, Z-wave, IPv6 Low Power Wireless Personal Area Network (6LoWPAN), or Wi-Fi. The data processing unit 20238 may directly transmit notifications (e.g., notifications to HCPs) to a cloud server via transmission to a cellular transmit / receive point (TRP) or base station using one or more of the following cellular protocols: GSM / GPRS / EDGE (2G), UMTS / HSPA (3G), Long-Term Evolution (LTE) or 4G, LTE Advanced (LTE-A), New Radio (NR) or 5G. In the embodiment, the sensing unit may be in communication with a hub / computing device via a router, as shown in Figures 6A to 6C.

[0306] Figure 7C shows an exemplary sensing system 20069 (e.g., a surgical sensing system or a patient sensing system). The sensing system 20069 may include a sensor unit 20245, a data processing and communication unit 20246, and a human interface device 20242. The sensor unit 20245 may include a sensor 20247 and an analog-to-digital converter (ADC) 20248. The ADC 20248 in the sensor unit 20245 can convert physiological signals measured by the sensor 20247 into measurement data associated with biomarkers. The sensor unit 20245 may transmit the measurement data to the data processing and communication unit 20246 for further processing. In the embodiment, the sensor unit 20245 may transmit the measurement data to the data processing and communication unit 20246 using an inter-integrated circuit (I2C) interface.

[0307] The data processing and communication unit 20246 includes a data processing unit 20249, a storage unit 20250, and an RF transceiver 20251. The sensing system may be in communication with a surgical hub or computing device 20243, which may then be in communication with a cloud computing system 20244. The cloud computing system 20244 may include a remote server 20077 and associated remote storage 20078. The sensor unit 20245 may include one or more in vitro or intra vivo sensors for measuring one or more biomarkers, as described herein.

[0308] After processing the measurement data received from the sensor unit 20245, the data processing and communication unit 20246 may further process the measurement data and / or transmit it to the smart hub or computing device 20243, as shown in Figure 7B. In this embodiment, the data processing and communication unit 20246 may transmit the measurement data received from the sensor unit 20245 to a remote server 20077 of the cloud computing system 20244 for further processing and / or monitoring.

[0309] Figure 7D shows an exemplary sensing system 20069 (e.g., a surgeon sensing system or a patient sensing system). Sensing system 20069 may include a sensor unit 20252, a data processing and communication unit 20253, and a human interface system 20261. Sensor unit 20252 may include a plurality of sensors 20254, 20255-20256 to measure one or more physiological signals associated with a patient or surgeon biomarker, and / or one or more physical state signals associated with the patient or surgeon's physical state. Sensor unit 20252 may also include one or more analog-to-digital converters (ADCs) 20257. The list of biomarkers may include biomarkers such as those disclosed herein. The ADC 20257 in sensor unit 20252 may convert each of the physiological signals and / or physical state signals measured by sensors 20254-20256 into their respective measurement data. Sensor unit 20252 may transmit measurement data associated with one or more biomarkers and the physical condition of the patient or surgeon to data processing and communication unit 20253 for further processing. Sensor unit 20252 may transmit measurement data to data processing and communication unit 20253 individually for each of sensors 1 20254 to N 20256, or in combination for all sensors. In the embodiment, sensor unit 20252 may transmit measurement data to data processing and communication unit 20253 via an I2C interface.

[0310] The data processing and communication unit 20253 may include a data processing unit 20258, a storage unit 20259, and an RF transceiver 20260. The sensing system 20069 may be in communication with a surgical hub or computing device 20243, which may then be in communication with a cloud computing system 20244 comprising at least one remote server 20077 and at least one storage unit 20078. The sensor unit 20252 may include one or more in vitro or in vivo sensors for measuring one or more biomarkers, as described herein.

[0311] Figure 8 illustrates an example of using surgical task situation recognition and measurement data from one or more surgeon sensing systems to coordinate surgical instrument control. Figure 8 illustrates an illustrative surgical procedure timeline and contextual information that the surgical hub can derive from data received from one or more surgical devices, one or more surgeon sensing systems, and / or one or more environmental sensing systems at each step of the surgical procedure. Devices that can be controlled by the surgical hub may include high-energy devices, endocutter clamps, etc. The surgeon sensing system may include sensing systems for measuring one or more biomarkers associated with the surgeon, e.g., heart rate, sweat composition, respiratory rate, etc. The environmental sensing system may include systems for measuring one or more environmental attributes, e.g., cameras for detecting the surgeon's position / movement / breathing patterns, spatial microphones for measuring ambient noise in the operating room and / or the tone of the healthcare provider's voice, ambient temperature / humidity, etc.

[0312] In the following description of Timeline 20265 illustrated in Figure 8, please also refer to Figure 5. Figure 5 provides various components used in a surgical procedure. Timeline 20265 depicts steps that may be taken individually and / or collectively by nurses, surgeons, and other healthcare professionals during an exemplary colorectal surgical procedure. In a colorectal surgical procedure, the situation-aware surgical hub 20076 may receive data from various data sources throughout the surgical procedure, including data generated each time a healthcare provider (HCP) uses the modular device / instrument 20095 paired with the surgical hub 20076. The surgical hub may receive this data from the paired modular device 20095. The surgical hub may receive measurement data from the sensing system 20069. The surgical hub can use data from modular device / instrument 20095 and / or measurement data from sensing system 20069 to continuously derive inferences (i.e., contextual information) regarding the HCP's stress level and the procedure in progress as new data is received, thereby obtaining the surgeon's stress level for the steps of the procedure being performed. The context awareness system of the surgical hub 20076 can perform one or more of the following: record procedure data for generating a report; verify the steps being taken by the healthcare professional; provide data or prompts that may be relevant to a particular step of the procedure (e.g., via a display screen); adjust modular devices based on context (e.g., activating a monitor, adjusting the FOV of a medical imaging device, or changing the energy level of an ultrasonic surgical instrument or RF electrosurgical instrument); or perform any other such actions described herein. In embodiments, these steps are performed by a remote server 20077 of cloud system 20064, which may communicate with the surgical hub 20076.

[0313] In the first step (not shown in Figure 8 for brevity), hospital staff may retrieve the patient's EMR from the hospital's EMR database. Based on the selected patient data in the EMR, the surgical hub 20076 may determine that the procedure to be performed is a colorectal procedure. Staff may scan the medical supplies coming in for the procedure. The surgical hub 20076 may cross-reference the scanned supplies with a list of supplies that can be used for various types of procedures and confirm that the combination of supplies corresponds to a colorectal procedure. The surgical hub 20076 may pair each of the sensing systems 20069 worn by different HCPs.

[0314] Once each device is prepared and pre-surgical preparations are complete, the surgical team may begin by making an incision and positioning the trocar. The surgical team may access and prepare by dissecting any adhesions and identifying the inferior mesenteric artery (IMA) branch, if any. The surgical hub 20076 may infer that the surgeon is in the process of dissecting the adhesions, based at least on data that can be received from the RF or ultrasound generator indicating that an energy instrument is being emitted. The surgical hub 20076 may cross-reference the received data with the read-out steps of the surgical procedure to determine that the energy instrument being emitted at this point in the process (e.g., after the completion of a step considered further in the procedure) corresponds to the dissection step.

[0315] Following incision, the HCP may proceed to the ligation step of the procedure (e.g., indicated by A1). As illustrated in Figure 8, the HCP may begin by ligating the IMA. The surgical hub 20076 may receive data from the advanced energy jaw device and / or endocutter indicating that the instrument is being fired, thus allowing it to infer that the surgeon is ligating the arteries and veins. The surgical hub may also receive measurement data from one of the HCP's sensing systems indicating a higher stress level for the HCP (e.g., indicated by the B1 mark on the time axis). For example, a higher stress level may be indicated by a change in the HCP's heart rate from a baseline. The surgical hub 20076 may derive this inference by cross-referencing the data received from the surgical stapling and cutting instruments with the read steps in the process (e.g., as indicated by A2 and A3), as with the previous step. The surgical hub 20076 can monitor the advanced energy jaw trigger ratio and / or end cutter clamp and firing rate during periods of high stress. In embodiments, the surgical hub 20076 may transmit support signals to the advanced energy jaw device and / or end cutter device to control the device in operation. The surgical hub may transmit support signals based on the stress level of the HCP operating the surgical device and / or the situational awareness known to the surgical hub. For example, the surgical hub 20076 may transmit control support signals to the advanced energy device or end cutter clamp as shown by A2 and A3 in Figure 8.

[0316] The HCP may proceed to the next step of releasing the upper sigmoid colon, followed by the descending colon, rectum, and sigmoid colon. The surgical hub 20076 may continue to monitor the HCP's high-stress markers (e.g., as indicated by D1, E1a, E1b, F1). During the high-stress period, the surgical hub 20076 may transmit support signals to the advanced energy jaw device and / or end-cutting device, as illustrated in Figure 8.

[0317] After mobilizing the colon, the HCP may proceed to the segmental resection portion of the procedure. For example, the surgical hub 20076, based on data from surgical stapling and cutting instruments, including data from its cartridge, can infer that the HCP is transversely cutting the intestine and performing sigmoid colon removal. The cartridge data may correspond, for example, to the size or type of staples fired by the instrument. Since different types of staples are used for different types of tissue, the cartridge data can indicate the type of tissue being stapled and / or transversely cut. It should be noted that surgeons should periodically alternate between surgical stapling / cutting instruments and surgical energy (e.g., RF or ultrasound) instruments depending on the specific stage of the procedure, as different instruments are better suited to specific tasks. Thus, the order in which stapling / cutting instruments and surgical energy instruments are used can indicate which stage of the procedure the surgeon is performing.

[0318] The surgical hub may determine and transmit control signals to surgical devices based on the stress level of the HCP. For example, during period G1b, control signal G2b may be transmitted to the endocutter clamp. Once the sigmoid colon has been removed, the incision may be closed and the postoperative portion of the procedure may begin. The patient may be awakened from anesthesia. The surgical hub 20076 may infer that the patient is awake from anesthesia based on one or more sensing systems attached to the patient.

[0319] Figure 9 is a block diagram of a computer-implemented bidirectional surgical system according to at least one aspect of the present disclosure. In one aspect, the computer-implemented bidirectional surgical system may be configured to monitor surgeon biomarkers and / or patient biomarkers using one or more sensing systems 20069. The surgeon biomarkers and / or patient biomarkers may be measured before, after, and / or during surgical procedures. In one aspect, the computer-implemented bidirectional surgical system may be configured to monitor and analyze data relating to the operation of various surgical systems 20069, including surgical hubs, surgical instruments, robotic devices, and operating rooms or medical facilities. The computer-implemented bidirectional surgical system may include a cloud-based analysis system. The cloud-based analysis system may include one or more analysis servers.

[0320] As illustrated in Figure 9, a cloud-based monitoring and analysis system may comprise multiple sensing systems 20268 (which may be the same as or similar to sensing system 20069), surgical instruments 20266 (which may be the same as or similar to instrument 20031), surgical instruments 20266 (which may be the same as or similar to instrument 20031), multiple surgical hubs 20270 (which may be the same as or similar to hub 20006), and a surgical data network 20269 (which may be the same as or similar to network 4) for connecting the surgical hubs 20270 to a cloud 20271 (which may be the same as or similar to cloud 20064). Each of the multiple surgical hubs 20270 may be communicatively coupled to one or more surgical instruments 20266. Each of the multiple surgical hubs 20270 may also be communicatively coupled via network 20269 to one or more sensing systems 20268 and a cloud 20271 of a computer-implemented bidirectional surgical system. The surgical hub 20270 and the sensing system 20268 may be coupled together in a communicative manner using wireless protocols as described herein. The cloud system 20271 may be a remote centralized source of hardware and software for storing, processing, manipulating, and communicating data generated from the sensing system 20268 based on various operations of the surgical system 20268.

[0321] As shown in Figure 9, access to the cloud system 20271 may be achieved via network 20269, which may be the Internet or some other suitable computer network. A surgical hub 20270, which may be coupled to the cloud system 20271, can be considered the client side of a cloud computing system (e.g., a cloud-based analytics system). Surgical instruments 20266 may be paired with the surgical hub 20270 for the control and implementation of various surgical procedures or surgeries, as described herein. Sensing systems 20268 may be paired with the surgical hub 20270 for surgeon-in-surgery monitoring, pre-surgical patient monitoring, intra-surgical patient monitoring, or post-surgical monitoring of patient biomarkers for surgeon-related biomarkers, to track and / or measure various milestones and / or detect various complications. The environmental sensing system 20267 can be paired with a surgical hub 20270 that measures environmental attributes associated with a surgeon or patient for surgeon monitoring, pre-surgical patient monitoring, intra-surgical patient monitoring, or post-surgical patient monitoring.

[0322] The surgical instruments 20266, the environmental sensing system 20267, and the sensing system 20268 may be equipped with wired or wireless transceivers for data transmission to and from their corresponding surgical hub 20270 (which may also be equipped with transceivers). One or more combinations of the surgical instruments 20266, the sensing system 20268, or the surgical hub 20270 may indicate a specific location, such as an operating room, an intensive care unit (ICU) room, or a recovery room within a medical facility (e.g., a hospital), in order to provide medical surgery, pre-surgical preparation, and / or post-surgical recovery. For example, the memory of the surgical hub 20270 may store location data.

[0323] As shown in Figure 9, the cloud system 20271 may include one or more central servers 20272 (which may be the same as or similar to the remote server 20067), a surgical hub application server 20276, a data analysis module 20277, and an input / output ("I / O") interface 20278. The central servers 20272 of the cloud 20271 may collectively manage the cloud computing system, which includes monitoring requests from client surgical hubs 20270 and managing the processing power of the cloud 20271 to perform those requests. Each of the central servers 20272 may comprise one or more processors 20273 coupled to a preferred memory device 20274, which may include volatile memory such as random access memory (RAM) and non-volatile memory such as magnetic storage devices. Memory device 20274 may contain machine-executable instructions that, when executed, cause processor 20273 to run data analysis module 20277 for cloud-based data analysis, real-time monitoring of measurement data received from sensing system 20268, operation, recommendation, and other operations described below. Processor 20273 can run data analysis module 20277 independently or in conjunction with hub applications running independently by hub 20270. Central server 20272 may also contain aggregated medical data database 20275, which may reside within memory 20274.

[0324] Based on connections to various surgical hubs 20270 via network 20269, cloud 20271 can aggregate data from specific data generated by various surgical instruments 20266 and / or monitor real-time data from sensing systems 20268, as well as from surgical hubs 20270 associated with surgical instruments 20266 and / or sensing systems 20268. Such aggregated data from surgical instruments 20266 and / or measurement data from sensing systems 20268 can be stored in the aggregated medical database 20275 of cloud 20271. In particular, cloud 20271 can advantageously track real-time measurement data from sensing systems 20268 and / or perform data analysis and manipulation on the measurement data and / or aggregated data to gain insights and / or perform functions that individual hubs 20270 cannot achieve on their own. For this purpose, as shown in Figure 9, cloud 20271 and surgical hubs 20270 are communicably coupled to transmit and receive information. The I / O interface 20278 is connected to multiple surgical hubs 20270 via network 20269. In this way, the I / O interface 20278 can be configured to transfer information between the surgical hubs 20270 and the aggregated medical data database 20275. This allows the I / O interface 20278 to facilitate read / write operations for a cloud-based analysis system. Such read / write operations may be performed in response to requests from the hubs 20270. These requests may be transmitted to the surgical hubs 20270 through the hub application. The I / O interface 20278 may include one or more high-speed data ports, including a universal serial bus (USB) port, an IEEE 1394 port, and Wi-Fi and Bluetooth I / O interfaces for connecting the cloud 20271 to the surgical hubs 20270.The hub application server 20276 of Cloud 20271 may be configured to host and provide sharing capabilities to software applications (e.g., hub applications) running on the surgical hub 20270. For example, the hub application server 20276 may manage requests made by hub applications through hub 20270, control access to the aggregated medical data database 20275, and perform load balancing.

[0325] The cloud computing system configurations described in this disclosure may be designed to address various issues arising in the context of medical surgeries and procedures performed using medical devices such as surgical instruments 20266, 20031 (e.g., pre-surgical monitoring, intra-surgical monitoring, and post-surgical monitoring). In particular, surgical instrument 20266 may be a digital surgical device configured to interact with cloud 20271 in order to implement techniques to improve the performance of surgical procedures. Sensing system 20268 may be a system having one or more sensors configured to measure one or more biomarkers associated with a surgeon performing a medical surgery and / or a patient in whom a medical surgery is scheduled, is being performed, or has been performed. Various surgical instruments 20266, sensing system 20268, and / or surgical hub 20270 may include a human interface system (e.g., having a touch-controlled user interface) so that a clinician and / or patient can control the manner of interaction between surgical instrument 20266 or sensing system 20268 and cloud 20271. Other suitable user interfaces for control, such as auditory-controlled user interfaces, may also be used.

[0326] The cloud computing system configurations described herein may be designed to address a variety of issues arising in the context of monitoring one or more biomarkers associated with a healthcare professional (HCP) or patient during pre-surgical, intra-surgical, and post-surgical procedures using sensing system 20268. Sensing system 20268 may be a surgeon sensing system or a patient sensing system configured to interact with a surgical hub 20270 and / or cloud system 20271 to implement a technology for monitoring surgeon biomarkers and / or patient biomarkers. Various sensing systems 20268 and / or surgical hub 20270 may include a touch-controlled human interface system so that the HCP or patient can control the manner of interaction between sensing system 20268 and surgical hub 20270 and / or cloud system 20271. Other suitable user interfaces for control, such as an auditory-controlled user interface, may also be used.

[0327] Figure 10 illustrates a surgical system 20280 according to the present disclosure, which may include a surgical instrument 20282 that can communicate with a console 20294 or a portable device 20296 via a local area network 20292 or a cloud network 20293 via a wired or wireless connection. In various embodiments, the console 20294 and the portable device 20296 may be any suitable computing device. The surgical instrument 20282 may include a handle 20297, an adapter 20285, and a loading unit 20287. The adapter 20285 is releasably coupled to the handle 20297, and the loading unit 20287 is releasably coupled to the adapter 20285, so that the adapter 20285 transmits force from the drive shaft to the loading unit 20287. The adapter 20285 or loading unit 20287 may include an internally positioned force gauge (not explicitly shown) for measuring the force exerted on the loading unit 20287. The loading unit 20287 may include an end effector 20289 having a first jaw 20291 and a second jaw 20290. The loading unit 20287 may be a multi-firing loading unit (MFLU) that allows a clinician to fire multiple fasteners multiple times without requiring the clinician to remove the loading unit 20287 from the surgical site and reload the loading unit 20287.

[0328] The first jaw 20291 and the second jaw 20290 may be configured to clamp tissue between them, fire fasteners through the clamped tissue, and cut the clamped tissue. The first jaw 20291 may be configured to include a replaceable multi-fire fastener cartridge containing multiple fasteners (e.g., staples, clips, etc.) which may be configured to fire at least one fastener multiple times, or which may be fired two or more times before being replaced. The second jaw 20290 may include an anvil which deforms or otherwise secures the fasteners as they are ejected from the multi-fire fastener cartridge.

[0329] The handle 20297 may include a motor coupled to the drive shaft so as to affect the rotation of the drive shaft. The handle 20297 may include a control interface for selectively operating the motor. The control interface may include buttons, switches, levers, sliders, touchscreens, and any other suitable input mechanisms or user interfaces, which can be engaged by a clinician to start the motor.

[0330] The control interface of the handle 20297 may be in communication with the controller 20298 of the handle 20297 in order to selectively operate the motor and influence the rotation of the drive shaft. The controller 20298 may be located within the handle 20297 and is configured to receive input from the control interface and adapter data from adapter 20285 or loading unit data from loading unit 20287. The controller 20298 may analyze the input from the control interface and the data received from adapter 20285 and / or loading unit 20287 in order to selectively operate the motor. The handle 20297 may also include a display that can be viewed by a clinician while the handle 20297 is in use. The display may be configured to show a portion of the adapter or loading unit data before, during, or after firing the instrument 20282.

[0331] Adapter 20285 may include an internally disposed adapter identification device 20284, and loading unit 20287 may include an internally disposed loading unit identification device 20288. Adapter identification device 20284 may be in communication with controller 20298, and loading unit identification device 20288 may be in communication with controller 20298. It will be understood that loading unit identification device 20288 may be in communication with adapter identification device 20284, which relays or passes communication from loading unit identification device 20288 to controller 20298.

[0332] Adapter 20285 may also include a plurality of sensors 20286 (one shown) disposed around it to detect various states of Adapter 20285 or the environment (e.g., whether Adapter 20285 is connected to a loading unit, whether Adapter 20285 is connected to a handle, whether the drive shaft is rotating, the torque of the drive shaft, the strain of the drive shaft, the temperature inside Adapter 20285, the number of times Adapter 20285 has fired, the peak force of Adapter 20285 during firing, the total amount of force applied to Adapter 20285, the peak recoil force of Adapter 20285, the number of pauses of Adapter 20285 during firing, etc.). The plurality of sensors 20286 may provide input to Adapter Identification Device 20284 in the form of data signals. The data signals from the plurality of sensors 20286 may be stored in Adapter Identification Device 20284 or used to update Adapter data stored in Adapter Identification Device 20284. The data signals from the plurality of sensors 20286 may be analog or digital. Multiple sensors 20286 may include force gauges for measuring the force exerted on the loading unit 20287 during firing.

[0333] The handle 20297 and adapter 20285 may be configured to interconnect the adapter identification device 20284 and the loading unit identification device 20288 with the controller 20298 via an electrical interface. The electrical interface may be a direct electrical interface (i.e., including electrical contacts that engage with each other to transmit energy and signals between them). Additionally or alternatively, the electrical interface may be a non-contact electrical interface for transmitting energy and signals between them wirelessly (e.g., inductively). It is also intended that the adapter identification device 20284 and the controller 20298 may communicate wirelessly with each other via a wireless connection separate from the electrical interface.

[0334] Handle 20297 may include a transceiver 20283 configured to transmit instrument data from controller 20298 to other components of system 20280 (e.g., LAN 20292, cloud 20293, console 20294, or portable device 20296). Controller 20298 may also transmit instrument data and / or measurement data associated with one or more sensors 20286 to surgical hub 20270, as illustrated in Figure 9. Transceiver 20283 may receive data from surgical hub 20270 (e.g., cartridge data, loading unit data, adapter data, or other notifications). Transceiver 20283 may also receive data from other components of system 20280 (e.g., cartridge data, loading unit data, or adapter data). For example, controller 20298 may transmit instrument data to console 20294, including the serial number of an attached adapter (e.g., adapter 20285) attached to handle 20297, the serial number of a loading unit (e.g., loading unit 20287) attached to adapter 20285, and the serial number of a multi-shot fastener cartridge loaded into the loading unit. Console 20294 may then send data associated with the attached cartridge, loading unit, and adapter (e.g., cartridge data, loading unit data, or adapter data) back to controller 20298. Controller 20298 may display the message on the local instrument display, or transmit the message via transceiver 20283 to console 20294 or portable device 20296, which will then display the message on display 20295 or the portable device screen, respectively.

[0335] Figures 11A to 11D illustrate embodiments of wearable sensing systems, such as surgical sensing systems or patient sensing systems. Figure 11A is an embodiment of a glasses-based sensing system 20300 that may be based on an electrochemical sensing platform. The sensing system 20300 may be capable of monitoring (e.g., real-time monitoring) sweat electrolytes and / or metabolites using a plurality of sensors 20304 and 20305 that are in contact with the skin of the surgeon or patient. For example, the sensing system 20300 may use an ampereometry-based biosensor 20304 and / or a potentiometry-based biosensor 20305 integrated into the nose bridge pad of the glasses 20302 to measure current and / or voltage.

[0336] The current-measuring biosensor 20304 can be used to measure sweat lactate levels (e.g., in mmol / L units). Lactate is a product of lactic acidosis, which can result from decreased tissue oxygenation, potentially caused by sepsis or hemorrhage. For example, a patient's lactate level (e.g., >2 mmol / L) may be used to monitor the development of sepsis during postoperative monitoring. The potentiometric biosensor 20305 can be used to measure potassium levels in a patient's sweat. A voltage follower circuit with an operational amplifier may be used to measure the potential signal between a reference electrode and a working electrode. The output of the voltage follower circuit may be filtered and converted to a digital value using an ADC.

[0337] Current measuring sensor 20304 and potentiometer measuring sensor 20305 may be connected to circuit 20303 located on each arm of the eyeglasses. Electrochemical sensors may be used for simultaneous real-time monitoring of sweat lactate and potassium levels. Electrochemical sensors may be screen-printed on stickers and placed on each side of the nose pads of the eyeglasses to monitor sweat metabolites and electrolytes. Electronic circuit 20303 located on the arms of the eyeglass frame may include a wireless data transceiver (e.g., a low-energy Bluetooth transceiver) which can be used to transmit lactate and / or potassium measurement data to a surgical hub or intermediate device, which can then transmit the measurement data to the surgical hub. The eyeglass-based sensing system 20300 may use, for example, a signal conditioning unit for filtering and amplifying the electrical signals generated from the electrochemical sensors 20305 or 20304, a microcontroller for digitizing the analog signals, and a wireless (e.g., low-energy Bluetooth) module for transferring the data to a surgical hub or computing device, as shown in Figures 7B-7D.

[0338] Figure 11B shows an embodiment of a wristband-type sensing system 20310 comprising a sensor assembly 20312 (e.g., a photoplethysmography (PPG)-based sensor assembly or an electrocardiogram (ECG)-based sensor assembly). For example, in the sensing system 20310, the sensor assembly 20312 may collect and analyze arterial pulses at the wrist. The sensor assembly 20312 may be used to measure one or more biomarkers (e.g., heart rate, heart rate variability (HRV)). In the case of a sensing system with a PPG-based sensor assembly 20312, light (e.g., green light) may pass through the skin. A certain percentage of the green light may be absorbed by blood vessels, and some of the green light may be reflected and detected by a photodetector. These differences or reflections are associated with variations in tissue blood perfusion, and these variations may be used to detect cardiovascular information (e.g., heart rate). For example, the amount absorbed may vary depending on the blood volume. The sensing system 20310 can determine heart rate by measuring light reflectance as a function of time. HRV can be determined as the time-period variation (e.g., standard deviation) between the steepest signal gradients before the peak, known as the inter-beat interval (IBI).

[0339] In the case of a sensing system having an ECG-based sensor assembly 20312, a pair of electrodes may be placed in contact with the skin. The sensing system 20310 may measure the voltage across the pair of electrodes placed on the skin in order to determine the heart rate. In this case, the HRV may be measured as the time-period variation (e.g., standard deviation) between R peaks in a group of QRS complexes, which is known as the RR interval.

[0340] The sensing system 20310 may, for example, use a signal conditioning unit for filtering and amplifying the analog PPG signal, a microcontroller for digitizing the analog PPG signal, and a wireless (e.g., Bluetooth) module for transferring the data to a surgical hub or computing device, as shown in Figures 7B to 7D.

[0341] Figure 11C shows an exemplary ring sensing system 20320. The ring sensing system 20320 may include a sensor assembly (e.g., a heart rate sensor assembly) 20322. The sensor assembly 20322 may include a light source (e.g., a red or green light-emitting diode (LED)) and a photodiode for detecting reflected and / or absorbed light. The LED in the sensor assembly 20322 may illuminate the finger, and the photodiode in the sensor assembly 20322 may measure heart rate and / or blood oxygen levels by detecting changes in blood volume. The ring sensing system 20320 may include other sensor assemblies for measuring other biomarkers, e.g., a thermistor or infrared thermometer for measuring body surface temperature. The ring sensing system 20320 may use, for example, a signal conditioning unit for filtering and amplifying the analog PPG signal, a microcontroller for digitizing the analog PPG signal, and a wireless (e.g., low-energy Bluetooth) module for transferring the data to a surgical hub or computing device, as shown in Figures 7B to 7D.

[0342] Figure 11D shows an embodiment of the electroencephalogram (EEG) sensing system 20315. As illustrated in Figure 11D, the sensing system 20315 may include one or more EEG sensor units 20317. The EEG sensor unit 20317 may include a plurality of conductive electrodes positioned in contact with the scalp. The conductive electrodes may be used to measure minute electrical potentials that may be generated outside the head due to neuronal activity in the brain. The EEG sensing system 20315 may measure biomarkers, such as delirium, by identifying certain brain patterns, such as delays or absences of posterior dominant rhythms and loss of responsiveness to eye opening and closing. The ring sensing system 20315 may include, for example, a signal conditioning unit for filtering and amplifying electrical potentials, a microcontroller for digitizing electrical signals, and a wireless (e.g., low-energy Bluetooth) module for transferring data to a smart device, as shown in Figures 7B to 7D.

[0343] Figure 12 illustrates a block diagram of a computer-implemented patient / surgeon monitoring system 20325 for monitoring one or more patient biomarkers or surgeon biomarkers before, during, and / or after a surgical procedure. As illustrated in Figure 12, one or more sensing systems 20336 may be used to measure and monitor patient biomarkers, for example, to facilitate patient preparation before a surgical procedure and recovery after a surgical procedure. Sensing systems 20336 may be used to assist surgical tasks by measuring and monitoring surgeon biomarkers in real time and communicating relevant biomarkers (e.g., surgeon biomarkers) to a surgical hub 20326 and / or surgical devices 20337 to adjust their functions. Adjustable surgical device functions may include power levels, forward speed, closing speed, load, latency, or other tissue-dependent operating parameters. Sensing systems 20336 may also measure one or more physical attributes associated with the surgeon or patient. Patient biomarkers and / or physical attributes can be measured in real time.

[0344] The computer-implemented wearable patient / surgeon wearable sensing system 20325 may include a surgical hub 20326, one or more sensing systems 20336, and one or more surgical devices 20337. The sensing systems and surgical devices may be communicatively coupled to the surgical hub 20326. For example, one or more analysis servers 20338, which are part of an analysis system, may also be communicatively coupled to the surgical hub 20326. Although a single surgical hub 20326 is depicted, it should be noted that the wearable patient / surgeon wearable sensing system 20325 may include any number of surgical hubs 20326 that can be connected to form a network of surgical hubs 20326 that are communicatively coupled to one or more analysis systems 20338 as described herein.

[0345] In the embodiment, the surgical hub 20326 may be a computing device. The computing device may be a personal computer, laptop, tablet, smart mobile device, etc. In the embodiment, the computing device may be a client computing device of a cloud-based computing system. The client computing device may be a thin client.

[0346] In an embodiment, the surgical hub 20326 may include a processor 20327 coupled to memory 20330 for executing stored instructions, storage 20331 for storing one or more databases such as an EMR database, and a data relay interface 20329 through which data is transmitted to an analysis system 20338. In an embodiment, the surgical hub 20326 may further include an I / O interface 20333 having an input device 20341 (e.g., a capacitive touchscreen or keyboard) for receiving input from a user and an output device 20335 (e.g., a display screen) for providing output to the user. In an embodiment, the input device and the output device may be a single device. The output may include data from queries entered by the user, suggestions for products or combinations of products for use in a given procedure, and / or instructions for actions to be performed before, during, or after a surgical procedure. The surgical hub 20326 may further include a device interface 20332 for communicatively coupling surgical devices 20337 to the surgical hub 20326. In one embodiment, the device interface 20332 may include transceivers that enable one or more surgical devices 20337 to connect to the surgical hub 20326 via a wired or wireless interface using one of the wired or wireless communication protocols described herein. The surgical devices 20337 may include, for example, a motorized stapler, an energy device or its generator, an imaging system, or other linked systems, such as a smoke exhauster, a suction-irrigation device, or an air supply system.

[0347] In the embodiment, the surgical hub 20326 may be communicatively coupled to one or more surgeons and / or patient sensing systems 20336. The sensing system 20336 may be used to measure and / or monitor in real time various biomarkers associated with the surgeon performing the surgical procedure or the patient on whom the surgical procedure is being performed. A list of patient / surgeon biomarkers measured by the sensing system 20336 is provided herein. In the embodiment, the surgical hub 20326 may be communicatively coupled to an environmental sensing system 20334. The environmental sensing system 20334 may be used to measure and / or monitor in real time environmental attributes, such as temperature / humidity in the operating room, surgeon movements, and ambient noise in the operating room caused by the breathing patterns of the surgeon and / or patient.

[0348] When the sensing system 20336 and the surgical device 20337 are connected to the surgical hub 20326, the surgical hub 20326 may receive from the sensing system 20336, for example, measurement data associated with one or more patient biomarkers, physical conditions associated with the patient, measurement data associated with surgeon biomarkers, and / or physical conditions associated with the surgeon, as illustrated in Figures 7B to 7D. The surgical hub 20326 may, for example, associate the measurement data associated with the surgeon with other relevant pre-surgical data and / or data from the situation awareness system to generate control signals for controlling the surgical device 20337, as illustrated in Figure 8.

[0349] In the embodiment, the surgical hub 20326 may compare measurement data from the sensing system 20336 to one or more thresholds defined based on baseline values, pre-surgical measurement data, and / or intra-surgical measurement data. The surgical hub 20326 may compare measurement data from the sensing system 20336 to one or more thresholds in real time. The surgical hub 20326 may generate notifications for display. For example, if the measurement data exceeds a defined threshold (e.g., above or below it), the surgical hub 20326 may send a notification for delivery to the human interface system for patient 20339 and / or the human interface system for the surgeon or HCP 20340. The determination of whether the notification will be sent to one or more of the human interface systems for patient 20339 and / or HCP 20340 may be based on the severity level associated with the notification. The surgical hub 20326 may also generate a severity level associated with the notification for display. The generated severity level may be displayed to the patient and / or surgeon or ...

Claims

1. A computing device, A processor is provided, and the processor is Receiving first sensor data from the first sensor system, Receiving second sensor data from a second sensor system different from the first sensor system, wherein the first sensor system is either a first patient sensor system or a first environmental sensor system, and the second sensor system is either a second patient sensor system or a second environmental sensor system. Based on the first sensor data and the second sensor data, the surgical device settings are determined. The system is configured to optionally transmit a signal indicating the determined surgical device settings to the surgical device, A computing device comprising the first sensor system comprising the first patient sensor system and the second sensor system comprising the second environmental sensor system.

2. The computing device according to claim 1, wherein the signal, when received by the surgical device, represents information that enables the surgical device to perform actions according to the determined surgical device settings.

3. The computing device according to claim 2, wherein the surgical device is any one of the following: an electric stapler, an electric stapler generator, an energy device, an energy device generator, an in-operating imaging system, a smoke exhaust system, a suction and irrigation device, or a blowing system.

4. The computing device according to claim 2 or 3, wherein the information indicates any of the following: power level, forward speed, closing speed, closing load, or standby time.

5. The computing device according to any one of claims 1 to 4, wherein the processor is further configured to receive procedural information, and the processor is configured to determine the surgical device settings based on the first sensor data, the second sensor data, and the procedural information.

6. The computing device according to claim 5, wherein the signal includes information indicating an alert, the alert represents an identified patient complication, which is associated with the surgical device being used for a surgical task represented by the procedure information without the surgical device switching to the determined surgical device settings.

7. A computer implementation method, Receiving first sensor data from the first sensor system, Receiving second sensor data from a second sensor system different from the first sensor system, wherein the first sensor system is either a first patient sensor system or a first environmental sensor system, and the second sensor system is either a second patient sensor system or a second environmental sensor system. Based on the first sensor data and the second sensor data, the surgical device settings are determined. This includes optionally transmitting a signal indicating the determined surgical device settings to the surgical device, A computer implementation method in which the first sensor system comprises the first patient sensor system, and the second sensor system comprises the second environmental sensor system.

8. The computer implementation method according to claim 7, further comprising receiving procedural information and determining the surgical device settings based on the first sensor data, the second sensor data, and the procedural information.

9. The computer implementation method according to claim 8, further comprising identifying complications and / or physiological comorbidities based on the first sensor data and the second sensor data, wherein the determination of the surgical device settings is additionally based on the procedure information and the identified complications and / or physiological comorbidities.

10. The computer implementation method according to claim 8 or 9, wherein the signal includes information indicating an alert, the alert represents an identified patient complication, which is associated with the surgical device being used for a surgical task represented by the procedure information without the surgical device being switched to the determined surgical device settings.

11. The computer implementation method according to any one of claims 7 to 10, wherein the signal represents setting information that enables the surgical device to perform the operation according to the determined surgical device settings when the signal is received by the surgical device.

12. The computer implementation method according to claim 11, wherein the surgical device is any one of the following: an electric stapler, an electric stapler generator, an energy device, an energy device generator, an intraoperative imaging system, a smoke exhauster, a suction and irrigation device, or a blowing system.

13. The computer implementation method according to claim 11 or 12, wherein the setting information indicates any of the following: power level, forward speed, closing speed, closing load, or standby time.

14. A surgical device, It is a processor, Receiving first sensor data from the first sensor system, Receiving second sensor data from a second sensor system different from the first sensor system, wherein the first sensor system is either a first patient sensor system or a first environmental sensor system, and the second sensor system is either a second patient sensor system or a second environmental sensor system. A processor is configured to determine surgical device settings based on the first sensor data and the second sensor data, A driver that performs surgical actions based on the determined surgical device settings, A surgical device wherein the surgical action is motorized stapling, and the surgical device setting indicates a closing load.

15. The surgical device according to claim 14, wherein the processor is further configured to receive procedural information, and the processor is configured to determine the surgical device settings based on the first sensor data, the second sensor data, and the procedural information.

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