Autonomous Adaptation of Surgical Device Control Algorithms

Abstract

A computing system may receive first operation data associated with a first surgical procedure and second operation data associated with a second surgical procedure. The computing system receives first result data associated with the first surgical procedure and second result data associated with the second surgical procedure. The computing system determines that each of the control algorithms for the first surgical device and the second surgical device is the latest control algorithm associated with the first surgical device type. The computing system generates first aggregated data based at least on the first operation data, the second operation data, the first result data, and the second result data. Based on the first aggregated data, the computing system determines a correlation between a first aspect of the latest control algorithm and the result data. Based on the determined correlation, the computing system generates an updated latest control algorithm.

Description

Technical Field

[0001] (Cross - Reference to Related Applications) This application is related to the following applications filed simultaneously, the content of each of which is incorporated herein by reference. · U.S. Patent Application entitled "METHOD OF CONTROLLING AUTONOMOUS OPERATIONS IN A SURGICAL SYSTEM" filed together with this specification and having Attorney Docket No. END9430USNP1. · U.S. Patent Application entitled "ADAPTED AUTONOMY FUNCTIONS AND SYSTEM INTERCONNECTIONS" filed together with this specification and having Attorney Docket No. END9430USNP4.

Background Art

[0002] Surgical operations are typically performed in an operating room or room within a medical facility such as a hospital. Various surgical devices and systems are utilized in the performance of surgical operations. In the digital information age, medical systems and facilities are often slower to implement new and improved technologies for systems or procedures due to the general desire to maintain patient safety and conventional practices.

Summary of the Invention

Means for Solving the Problems

[0003] Systems, methods, and means for autonomous adaptation of surgical device control algorithms are described herein. A computing system for autonomous surgical device control algorithm adaptation may include a processor. The processor may be configured to perform one or more actions. The processor may be configured to receive first motion data associated with a first surgical procedure and second motion data associated with a second surgical procedure. In some examples, the first motion data is associated with a first aspect of a control algorithm of a first surgical device. In some examples, the second motion data is associated with a first aspect of a control algorithm of a second surgical device, and the first surgical device and the second surgical device are of a first surgical device type. The processor is configured to receive first result data associated with the first surgical procedure and second result data associated with the second surgical procedure. The processor is configured to determine that each of the control algorithms of the first surgical device and the second surgical device is the latest control algorithm associated with the first surgical device type. The processor is configured to generate first aggregated data based at least on the first motion data, the second motion data, the first result data, and the second result data. Based at least on the first aggregated data, the processor is configured to determine a correlation between the first aspect of the latest control algorithm and the result data. Based on the determined correlation, the processor is configured to generate an updated latest control algorithm.

[0004] A computing system is described for conforming to an autonomous surgical device control algorithm. The computing system includes a processor. The processor is configured to receive first motion data associated with a first surgical procedure and second motion data associated with a second surgical procedure. The first motion data is associated with a first aspect of a control algorithm for a first surgical device. The second motion data is associated with a first aspect of a control algorithm for a second surgical device. The first surgical device and the second surgical device are of a first surgical device type. The processor is configured to receive first result data associated with the first surgical procedure and second result data associated with the second surgical procedure. The processor is configured to determine that each of the control algorithm for the first surgical device and the control algorithm for the second surgical device is an up-to-date control algorithm associated with the first surgical device type. The processor is configured to generate first aggregated data based at least on the first motion data, the second motion data, the first result data, and the second result data. The processor is configured to determine a correlation between the first aspect of the up-to-date control algorithm and the result data based at least on the first aggregated data. The processor is configured to generate an updated up-to-date control algorithm based on the determined correlation. Advantageously, the system according to Embodiment 1 can improve surgical outcomes when a sub-optimal control algorithm is being used in one or more surgical devices, or when the control algorithm for one or more surgical devices is not well-suited to the surgical environment and context in which it is employed (e.g., surgical type, surgeon preference, mode of operation, etc.).

[0005] As a non-limiting example, the control algorithm can control one or both of the closing force (FTC) and the firing force (FTF). The first operation data / second operation data can include one or more of the FTC over time, the waiting time before firing is initiated, the FTF over time, tissue characteristics (e.g., impedance, thickness, rigidity, etc.), tissue gap (i.e., tissue thickness), tissue type, tissue state, the number of firings on the device, etc. By comparing the first operation data and the second operation data with the first result and the second result, it may be possible to determine the correlation between the data and the result. For example, a longer waiting time before firing and / or a higher peak FTF may be associated with a more positive result for a specific tissue type / thickness for a particular treatment. Thus, generating an updated and up-to-date control algorithm can include including a longer waiting time and / or a higher peak FTF associated with a more positive result.

[0006] The operation data may include some variables about the way the device was used that enable a more accurate comparison of the operation data, such as the firing waiting time, FTC, FTF, tissue characteristics, etc. For example, a positive result may be associated with the difference between the operation data for one or more variables.

[0007] By aggregating the operation and result data for multiple surgical procedures performed with different settings, the system can generate result-improving control algorithm updates in a way that would not be possible with a system that depends on data for a single surgical device or a single procedure alone.

[0008] As an example of improving the result, the result of tissue resection can correspond to the integrity of the sealing line, and a seal with less leakage is equivalent to a more positive or improved result than a seal with more leakage. An improved result can also correspond to a reduction in the risk of complications, e.g., a reduction in the risks of device malfunction, sealing line leakage, staple misfiring, etc.

[0009] The first aspect of the control algorithm for each device can include at least one coefficient, at least one operating parameter, at least one limit, and / or at least one setting of the control algorithm, and / or at least one function, subroutine, process, and / or algorithm. Receiving and using data for control algorithm coefficients, parameters, limits, or settings (e.g., sensed, detected, or recorded force values, time values, voltage / current values, displacement values, etc.) enables the system to computationally efficiently and quickly determine candidate updates to the control algorithm based on the correlation between this data and the resulting data. Such simple inputs can be sufficient to generate control algorithm updates because a statistical link between these inputs and the variations that occur as a result in the surgical outcome can be found in a non-resource-intensive manner.

[0010] Alternatively, instead of or in addition to the first aspect of the control algorithm for each device that includes at least one coefficient, at least one operating parameter, at least one limit, and / or at least one setting of the control algorithm, the first aspect of the control algorithm for each device can include at least one function, subroutine, process, and / or algorithm. For example, the first aspect of the control algorithm can include a power algorithm used by a surgical device / each surgical device. A "power algorithm" can be the change in power of a device (e.g., an electrosurgical device or an ultrasonic device) over time in response to the internal state of the device / each device and / or in response to the tissue state. For example, a computing system can determine the correlation between positive outcomes and a specific power algorithm that involves treating small tissue sizes with low power and large tissue sizes with high power, and then generate a new control algorithm that includes this same power algorithm or a similar power algorithm (e.g., a power algorithm that shares at least some characteristics / behaviors with this power algorithm) for use in other surgical devices, causing them to treat small tissue sizes with low power and large tissue sizes with high power.

[0011] Generating an updated and latest control algorithm may include replacing at least one coefficient, operating parameter, limit and / or setting, and / or power algorithm associated with the control algorithms for the first surgical device and the second surgical device with at least one updated coefficient, operating parameter, limit and / or setting, and / or power algorithm associated with the updated and latest control algorithm. Replacing / updating the coefficient / parameter / limit / setting / power algorithm of the control algorithm is a particularly advantageous means of generating a control algorithm update. This is because the new control algorithm can be generated based on the old control algorithm, using it as a "template", without the need to make large-scale modifications to the algorithm or build it "from scratch".

[0012] The first operating data and the second operating data may be received together with additional operating data associated with a plurality of further surgical procedures, the additional operating data being associated with a first aspect of the control algorithm of each surgical device, and each surgical device being of the first surgical device type. The first result data and the second result data may be received together with additional result data associated with a plurality of further surgical procedures. Generating the first aggregated data may also be based at least on the additional operating data and the additional result data. The greater the amount of surgical procedures and the diversity of surgical procedures for which data is supplied to the computing system, the smaller the impact of outliers in the data on the determined correlations, and the greater the degree to which variables that may potentially affect the surgical outcome (e.g., surgeon skill and experience, time, location, patient age and health status, etc.) are reduced and controlled in the analysis. Thus, it may be advantageous to use data for a plurality of further surgical procedures in addition to the data from the first surgical procedure and the second surgical procedure.

[0013] The surgical procedure may be a past surgical procedure.

[0014] Each of the surgical devices may be mechanically identical and / or the surgical devices may differ functionally only in their respective control algorithms. Using data from surgical devices that are mechanically the same (e.g., same manufacturer, model, and / or brand) and / or whose differences in function can be entirely attributed to the specific control algorithms used in each device can be advantageous because doing so can substantially simplify data aggregation and analysis (and the generation of control algorithm updates). For example, if two instruments are mechanically identical, the various (e.g.) force / voltage / current / speed values output by the devices can be analyzed without the need to account for differences between the specific structural differences between one device and another.

[0015] Each surgical procedure can be of the same surgical procedure type. Advantageously, using data from a single common surgical procedure type enables the generation of more detailed and specific control algorithm updates beyond providing general improvements, generating control algorithms well-suited to those specific surgical tasks.

[0016] Determining the correlation between a first aspect of a current control algorithm and result data can include determining that the first result data represents a more positive result of a surgical procedure or step thereof than the second result data, determining the difference in the first aspect of the control algorithms used in the first surgical procedure and the second surgical procedure, and associating the difference in the first aspect of the control algorithm with the more positive result. Advantageously, the above embodiments enable improvements in surgical outcomes to be attributed to specific device settings or parameters that cause them, such that success can be reproduced across other instruments by including the settings / parameters in the control algorithm update.

[0017] As an example, the results of the result data can correspond to the integrity of the sealing line, and a leaking seal is not equivalent to a more positive or improved result than a seal that leaks more. As another example, a more positive result can be one without surgical complications, while a negative result can be one with one or more complications, such as instrument malfunction, sealing line leakage, staple misfiring, etc. A more positive result can also be associated with more positive results than negative results, such as, for example, increasing the percentage of positive results.

[0018] Generating an updated and latest control algorithm may include replacing at least one coefficient, operating parameter, limit, and / or setting with a coefficient, operating parameter, limit, and / or setting associated with a control algorithm used in at least one surgery that resulted in a positive result.

[0019] Generating an updated and latest control algorithm may include considering the difference in result data between at least a first surgery and a second surgery as being due to the difference in a first aspect of the control algorithm used in the procedure.

[0020] The computing system can be one of an edge computing system or a cloud computing system.

[0021] The first operation data may be received from a first surgical device, and the second operation data may be received from a second surgical device. Receiving operation data directly from the device can improve the processing speed of the entire system and reduce the delay in generating (and optionally distributing) control algorithm updates. Further, the need for additional storage devices and / or the need for any compression / truncation / rounding of the operation data, which could otherwise result in a loss of fidelity, can be eliminated.

[0022] The first operation data and the second operation data may be received from a surgical hub or two different surgical hubs. Receiving all operation data from the same hub results in a more consistent system with less latency and reduced network requirements. By receiving data from two different hubs, it becomes possible to use data from geographically different surgical environments for analysis to draw new conclusions that are not apparent from data of a single surgical environment only.

[0023] The first result data associated with the first surgical procedure may be received from a surgical hub or a surgical visualization device.

[0024] The processor may be further configured to publish an updated and latest control algorithm for the surgical device or surgical hub to download via the interface. Making the updated and latest control algorithm available for download enables the benefits associated with the improved control algorithm to be more readily obtained across a wide range of devices in potentially geographically different surgical environments, where otherwise new control algorithms may have to be obtained through slower alternative means.

[0025] The processor may be further configured to send the updated and latest control algorithm to a plurality of surgical hubs and / or a plurality of surgical devices. Sending the updated control algorithm to the hub and / or device enables the benefits associated with the improved control algorithm to be more readily obtained in these other environments, where otherwise new control algorithms may have to be obtained through slower alternative means, as it enables it to be deployed across a wide range of devices in potentially geographically different surgical environments.

[0026] The determined correlation may include a correlation between a first aspect of the latest control algorithm and a negative surgical outcome, and a correlation between the first aspect of the latest control algorithm and a positive surgical outcome. As an example, the surgical outcome can correspond to the integrity of the seal line, and a seal with less leakage is equivalent to a positive or improved outcome, while a seal with more leakage is a negative outcome or a reduction in outcome (relative to others). A threshold can be set to classify positive or negative outcomes, and in this example, it can be a threshold leakage. As another example, a more positive outcome can be a result without surgical complications, while a negative outcome can be a result with one or more complications, such as instrument malfunction, seal line leakage, staple misfiring, etc.

[0027] The result data may include first result data and second result data.

[0028] A smart surgical device is described. The smart surgical device comprises a processor. The processor is configured to receive from a surgical hub an updated control algorithm associated with the smart surgical device. The processor is configured to determine that the updated control algorithm is more up-to-date than the control algorithm installed on the smart surgical device. The processor is configured to replace the control algorithm with the updated control algorithm. The processor is configured to operate using the updated control algorithm.

[0029] Replacing the control algorithm with the updated control algorithm may include replacing a coefficient associated with the control algorithm with an updated coefficient associated with the updated control algorithm.

[0030] A method is described for the adaptation of an autonomous surgical device control algorithm. The method includes receiving first motion data associated with a first surgical procedure and second motion data associated with a second surgical procedure. The first motion data is associated with a first aspect of a control algorithm for a first surgical device. The second motion data is associated with a first aspect of a control algorithm for a second surgical device. The first surgical device and the second surgical device are of a first surgical device type. The method further includes receiving first result data associated with the first surgical procedure and second result data associated with the second surgical procedure. The method further includes determining that each of the control algorithm for the first surgical device and the control algorithm for the second surgical device is an up-to-date control algorithm associated with the first surgical device type. The method further includes generating first aggregated data based on at least the first motion data, the second motion data, the first result data, and the second result data. The method further includes determining a correlation between the first aspect of the up-to-date control algorithm and the result data based on at least the first aggregated data. The method further includes generating an updated up-to-date control algorithm based on the determined correlation. Advantageously, the method according to Embodiment 21 can improve surgical outcomes when a sub-optimal control algorithm is being used in one or more surgical devices, or when the control algorithm for one or more surgical devices is not well-suited to the surgical environment and context in which it is employed (e.g., surgical type, surgeon preference, mode of operation, etc.).

[0031] As a non-limiting example, the control algorithm can control one or both of the closing force (FTC) and the firing force (FTF). The first operation data / second operation data can include one or more of the FTC over time, the waiting time before firing is initiated, the FTF over time, tissue characteristics (e.g., impedance, thickness, stiffness, etc.), tissue gap (i.e., tissue thickness), tissue type, tissue state, the number of firings on the device, etc. By comparing the first operation data and the second operation data with the first result and the second result, it may be possible to determine the correlation between the data and the result. For example, a longer waiting time and / or a higher peak FTF before firing may be associated with a more positive result for a specific tissue type / thickness for a particular treatment. Thus, generating an updated and up-to-date control algorithm can include including a longer waiting time and / or a higher peak FTF associated with a more positive result.

[0032] The operation data may include some variables about the way the device was used that enable a more accurate comparison of the operation data, such as the firing waiting time, FTC, FTF, tissue characteristics, etc. For example, a positive result may be associated with the difference between the operation data for one or more variables.

[0033] By aggregating the operation and result data for multiple surgical procedures performed with different settings, the method can generate result-improving control algorithm updates in a way that would not be possible with a method that relies on data for a single surgical device or treatment alone.

[0034] As an example of improving the result, the result of tissue resection can correspond to the integrity of the sealing line, and a seal with less leakage is equivalent to a more positive or improved result than a seal with more leakage. An improved result can also correspond to a reduction in the risk of complications, e.g., a reduction in the risks of device malfunction, seal line leakage, staple misfiring, etc.

[0035] The first aspect of the control algorithm for each device can include at least one coefficient, at least one operating parameter, at least one limit, and / or at least one setting of the control algorithm, and / or at least one function, subroutine, process, and / or algorithm. Receiving and using data for control algorithm coefficients, parameters, limits, or settings (e.g., sensed, detected, or recorded force values, time values, voltage / current values, displacement values, etc.) enables the system to computationally efficiently and quickly determine candidate updates to the control algorithm based on the correlation between this data and the resulting data. Such simple inputs can be sufficient to generate control algorithm updates because statistical links between these inputs and the variations resulting in surgical outcomes can be found in a non-resource-intensive manner.

[0036] Alternatively, instead of or in addition to the first aspect of the control algorithm for each device that includes at least one coefficient, at least one operating parameter, at least one limit, and / or at least one setting of the control algorithm, the first aspect of the control algorithm for each device can include at least one function, subroutine, process, and / or algorithm. For example, the first aspect of the control algorithm can include a power algorithm used by a surgical device / each surgical device. A "power algorithm" can be the change in power over time of a device (e.g., an electrosurgical device or an ultrasonic device) in response to the internal state of the device / each device and / or in response to the tissue state. For example, a computing system can determine the correlation between positive outcomes and a specific power algorithm that involves treating small tissue sizes with low power and large tissue sizes with high power, and then generate a new control algorithm that includes this same power algorithm or a similar power algorithm (e.g., a power algorithm that shares at least some characteristics / behaviors with this power algorithm) for use in other surgical devices, causing them to treat small tissue sizes with low power and large tissue sizes with high power.

[0037] Generating an updated and latest control algorithm may include replacing at least one coefficient, operating parameter, limit and / or setting, and / or power algorithm associated with the control algorithms for the first surgical device and the second surgical device with at least one updated coefficient, operating parameter, limit and / or setting, and / or power algorithm associated with the updated and latest control algorithm. Replacing / updating the coefficient / parameter / limit / setting / power algorithm of the control algorithm is a particularly advantageous means of generating a control algorithm update. This is because the new control algorithm can be generated based on the old control algorithm, using it as a "template", without the need to make large-scale modifications to the algorithm or build it "from scratch".

[0038] The first operating data and the second operating data may be received together with additional operating data associated with a plurality of further surgical procedures, the additional operating data being associated with a first aspect of the control algorithm of each surgical device, and each surgical device being of the first surgical device type. The first result data and the second result data may be received together with additional result data associated with a plurality of further surgical procedures. Generating the first aggregated data may also be based at least on the additional operating data and the additional result data. The greater the amount of surgical procedures and the diversity of the surgical procedures for which data is supplied, the smaller the impact on the determined correlations of outliers in the data, and the greater the degree to which variables that can potentially affect the surgical outcome (e.g., surgeon skill and experience, time, location, patient age and health status, etc.) are reduced and controlled in the analysis. Therefore, it may be advantageous to use data for a plurality of further surgical procedures in combination with the data from the first surgical procedure and the second surgical procedure.

[0039] The surgical procedure may be a past surgical procedure.

[0040] Each of the surgical devices may be mechanically identical and / or the surgical devices may be functionally different only in their respective control algorithms. Using data from surgical devices that are mechanically the same (e.g., same manufacturer, model, and / or brand) and / or whose differences in function can be entirely attributed to the specific control algorithms used in each device can be advantageous because doing so can substantially simplify data aggregation and analysis (and the generation of control algorithm updates). For example, if two instruments are mechanically identical, the various (e.g.) force / voltage / current / speed values output by the devices can be analyzed without having to account for differences between the specific structural differences between one device and another.

[0041] Each surgical procedure can be of the same surgical procedure type. Advantageously, using data from a single common surgical procedure type enables the generation of more detailed and specific control algorithm updates beyond providing general improvements, generating control algorithms well-suited to those specific surgical tasks.

[0042] Determining the correlation between a first aspect of a current control algorithm and the result data may include determining that the first result data represents a more positive result of a surgical procedure or a step thereof than the second result data, determining the difference in the first aspect of the control algorithms used in the first surgical procedure and the second surgical procedure, and associating the difference in the first aspect of the control algorithm with the more positive result. Advantageously, the above embodiments enable the improvement of surgical outcomes to be attributed to the specific device settings or parameters that caused them, such that success can be reproduced across other instruments by including the settings / parameters in the control algorithm update.

[0043] As an example, the results of the result data can correspond to the integrity of the seal line, and a leaking seal is not equivalent to a more positive or improved result than a more leaking seal. As another example, a more positive result can be one without surgical complications, while a negative result can be one with one or more complications, such as instrument malfunction, seal line leakage, staple misfiring, etc. A more positive result can also be associated with more positive results than negative results, for example, by increasing the percentage of positive results.

[0044] Generating an updated and latest control algorithm may include replacing at least one coefficient, operating parameter, limit, and / or setting with a coefficient, operating parameter, limit, and / or setting associated with a control algorithm used in at least one surgery that resulted in a positive result.

[0045] Generating an updated and latest control algorithm may include considering the difference in result data between at least a first surgery and a second surgery as being due to the difference in a first aspect of the control algorithm used in the procedure.

[0046] The computing system can be one of an edge computing system or a cloud computing system.

[0047] The first operation data may be received from a first surgical device, and the second operation data may be received from a second surgical device. Receiving the operation data directly from the device can improve the processing speed of the entire system and reduce the delay in generating (and optionally distributing) control algorithm updates. Further, the need for additional storage devices and / or the need for any compression / truncation / rounding of the operation data, which could otherwise result in a loss of fidelity, can be eliminated.

[0048] The first operation data and the second operation data may be received from a surgical hub or two different surgical hubs. Receiving all operation data from the same hub results in more connections, shorter latency, and reduced network requirements. By receiving data from two different hubs, it becomes possible to use data from geographically different surgical environments in the analysis to draw new conclusions that are not apparent from data from a single surgical environment only.

[0049] The first result data associated with the first surgical procedure may be received from a surgical hub or a surgical visualization device.

[0050] The processor may be further configured to publish an updated and latest control algorithm for the surgical device or surgical hub to download via the interface. Making the updated and latest control algorithm available for download enables the benefits associated with the improved control algorithm to be more readily obtained across a wide range of devices in potentially geographically different surgical environments, where otherwise new control algorithms may have to be obtained through slower alternative means.

[0051] The processor may be further configured to send the updated and latest control algorithm to a plurality of surgical hubs and / or a plurality of surgical devices. Sending the updated control algorithm to the hubs and / or devices enables the benefits associated with the improved control algorithm to be more readily obtained in these other environments, where otherwise new control algorithms may have to be obtained through slower alternative means, by enabling it to be deployed across a wide range of devices in potentially geographically different surgical environments.

[0052] The determined correlations may include a correlation between the first aspect of the updated control algorithm and a negative surgical outcome, and a correlation between the first aspect of the updated control algorithm and a positive surgical outcome. As an example, a surgical outcome may correspond to the integrity of the seal line, with a seal with less leakage equating to a positive outcome or improved outcome, and a seal with more leakage being a negative outcome or reduced outcome (versus the other). A threshold may be set to classify positive or negative outcomes, which in this example may be a threshold leak. As another example, a more positive outcome may be an outcome without a surgical complication, whereas a negative outcome may be an outcome with one or more complications, such as instrument malfunction, seal line leakage, staple misfire, etc.

[0053] The outcome data may include first outcome data and second outcome data.

[0054] The first and second surgical devices may both be surgical endocutter or stapling devices. The first and second operational data may relate to one or more of control of an energy source, cutting, stapling, knob orientation, body orientation, body position, anvil jaw force, and reload alignment slot management.

[0055] A computer program product is described for causing a computer to carry out any of the above methods.

[0056] A non-transitory computer readable storage medium is described that contains computer readable instructions that, when executed by a computer, cause the computer to perform any of the aforementioned methods. [Brief description of the drawings]

[0057]

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Modes for Carrying Out the Invention

[0058] FIG. 1 is a block diagram of a computer-implemented surgical system 20000. An exemplary surgical system such as surgical system 20000 can include one or more surgical systems (e.g., surgical subsystems) 20002, 20003, and 20004. For example, each surgical system 20002 can include a computer-implemented bidirectional surgical system. For example, surgical system 20002 may include a surgical hub 20006 and / or a computing device 20016 that communicates with a cloud computing system 20008, as described, for example, in FIG. 2. The cloud computing system 20008 can include at least one remote cloud server 20009 and at least one remote cloud storage unit 20010. Exemplary surgical systems 20002, 20003, or 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 HCP sensing systems and / or one or more patient sensing systems. The environmental sensing system 20015 may include, for example, one or more devices used to measure one or more environmental attributes, as further described, for example, in FIG. 2. The robotic system 20013 may include, for example, a plurality of devices used to perform a surgical procedure, as further described, for example, in FIG. 2.

[0059] The surgical system 20002 may communicate with a remote server 20009 that may be part of a cloud computing system 20008. In one example, the surgical system 20002 may communicate with the remote server 20009 via a cable / FIOS networking node of an Internet service provider. In one example, the patient sensing system may communicate directly with the remote server 20009. The surgical system 20002 and / or its components may use one or more of the cellular protocols of GSM / GPRS / EDGE (2G), UMTS / HSPA (3G), long term evolution (LTE) or 4G, LTE-Advanced (LTE-A), new radio (NR) or 5G to communicate with the remote server 20009 via a cellular transmission / reception point (TRP) or base station.

[0060] The surgical hub 20006 may have a collaborative interaction with one of more means for displaying images from the laparoscope scope 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 transmit 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 and from the human interface system 20012. The human interface system 20012 may include one or more human interface devices (HIDs). The surgical hub 20006 may transmit and / or receive acoustic devices, notification or control information to the display, and / or control information to various devices that communicate with the surgical hub.

[0061] For example, as discussed in FIG. 1, the sensing system 20001 may include a wearable sensing system 20011 (which may include one or more HCP sensing systems and one or more patient sensing systems) and an environmental sensing system 20015. One or more sensing systems 20001 may measure data regarding various biomarkers. One or more sensing systems 20001 may use 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., to measure biomarkers. One or more sensors may use one of more of sensing techniques such as photoplethysmography, electrocardiogram examination, electroencephalogram examination, colorimetric analysis, impedancemetry, potential difference measurement, current measurement, etc., to measure biomarkers as described herein.

[0062] The biomarkers measured by one or more sensing systems 20001 may include, but are not limited to, sleep, core body temperature, maximal oxygen consumption, physical activity, alcohol intake, respiratory rate, oxygen saturation, blood pressure, blood glucose, heart rate variability, blood potential of hydrogen, hydration status, heart rate, skin conductance, peripheral temperature, tissue perfusion pressure, cough and sneeze, gastrointestinal motility, gastrointestinal imaging, airway bacteria, edema, mental state, sweat, circulating tumor cells, autonomic nervous tension, circadian rhythm, and / or menstrual cycle.

[0063] Biomarkers may be related to physiological systems including, but not limited to, the behavioral and psychological, cardiovascular, renal, skin, nervous, gastrointestinal, respiratory, endocrine, immune, tumor, musculoskeletal, and / or reproductive systems. Information from biomarkers may be determined and / or used, for example, by a computer-implemented patient and surgical system 20000. Information from biomarkers may be determined and / or used by a computer-implemented patient and surgical system 20000 to, for example, improve the above system and / or improve patient outcomes. One or more sensing systems 20001, biomarkers 20005, and physiological systems are described in more detail by U.S. Patent Application No. 17 / 156,287, filed January 22, 2021, entitled "METHOD OF ADJUSTING A SURGICAL PARAMETER BASED ON BIOMARKER MEASUREMENTS" (Attorney Docket No. END9290USNP1), the disclosure of which is incorporated herein by reference in its entirety.

[0064] Figure 2 shows an example of a surgical system 20002 in an operating room. As illustrated in Figure 2, the patient is operated on by one or more healthcare professionals (HCPs). The HCPs are monitored by one or more HCP sensing systems 20020 worn by the HCPs. The HCPs, and the environment surrounding the HCPs, 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 deployed in the operating room. The HCP sensing systems 20020 and the environmental sensing systems communicate with a surgical hub 20006 and may further communicate with one or more cloud servers 20009 of a cloud computing system 20008 as shown in Figure 1. The environmental sensing system may be used to measure one or more environmental attributes, such as the position of the HCPs in the operating room, HCP movement, ambient noise in the operating room, temperature / humidity in the operating room, and the like.

[0065] As illustrated in FIG. 2, the main display 20023 and one or more audio output devices (e.g., speaker 20019) are placed within the sterile field so as to be visible to the operator on the operating table 20024. Additionally, the visualization / notification tower 20026 is placed outside the sterile field. The visualization / notification tower 20026 may include a first non-sterile human interface device (HID) 20027 and a second non-sterile HID 20029 facing opposite each other. The HID may be a display, or may be a display having a touch screen that enables a human to directly interface with the HID. The human interface system guided by the surgical hub 20006 may be configured to utilize the HIDs 20027, 20029, and 20023 to regulate the information flow to the operators inside and outside the sterile field. In one example, the surgical hub 20006 may cause the HID (e.g., the main HID 20023) to display notifications and / or information regarding the patient and / or surgical steps. In one example, the surgical hub 20006 may prompt and / or receive input from a person within the sterile field or non-sterile area. In one example, the surgical hub 20006 may cause the HID to display a snapshot of the surgical site recorded by the imaging device 20030 on the non-sterile HID 20027 or 20029 while maintaining a live video of the surgical site on the main HID 20023. The snapshot on the non-sterile display 20027 or 20029 may, for example, permit a non-sterile operator to perform diagnostic steps related to the surgery.

[0066] In one aspect, the surgical hub 20006 may be configured to send diagnostic input or feedback entered by a non-sterile operator at the visualization tower 20026 to the main display 20023 within the sterile field, such that it can be viewed by the sterile operator on the operating table. In one example, the input may be in the form of a modification to a snapshot displayed on the non-sterile display 20027 or 20029, which may be sent by the surgical hub 20006 to the main display 20023.

[0067] Referring to FIG. 2, the surgical instrument 20031 is used as part of a surgical system 20002 in a surgical operation. The hub 20006 can also be configured to regulate the information flow to the display of the surgical instrument 20031. For example, in U.S. Patent Application Publication No. 2019 / 0200844 (A1) (U.S. Patent Application No. 16 / 209,385) entitled "METHOD OF HUB COMMUNICATION, PROCESSING, STORAGE AND DISPLAY", filed on December 4, 2018, the disclosure of which is hereby incorporated by reference in its entirety. Diagnostic inputs or feedback entered by a non-sterile operator at the visualization tower 20026 are sent by the hub 20006 to the surgical instrument display within the sterile field, where they can be viewed by the operator of the surgical instrument 20031. Exemplary surgical instruments suitable for use with the 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) entitled "METHOD OF HUB COMMUNICATION, PROCESSING, STORAGE AND DISPLAY", filed on December 4, 2018, the disclosure of which is hereby incorporated by reference in its entirety.

[0068] FIG. 2 illustrates an example of a surgical system 20002 used to perform surgery on a patient lying on an operating table 20024 within an operating room 20035. A robotic system 20034 can be used as part of the surgical system 20002 in a surgical procedure. The robotic system 20034 can include a surgeon's console 20036, a patient-side cart 20032 (surgical robot), and a surgical robot hub 20033. While the surgeon views the surgical site through the surgeon's console 20036, the patient-side cart 20032 can manipulate at least one removably coupled surgical tool 20037 through a minimally invasive incision in the patient's body. An image of the surgical site can be obtained by a medical imaging device 20030 that can be manipulated by the patient-side cart 20032 to change the orientation of the imaging device 20030. The robotic hub 20033 can be used to process an image of the surgical site and then display it to the surgeon through the surgeon's console 20036.

[0069] Other types of robotic systems can be readily adapted to be used with the surgical system 20002. Various examples of robotic systems and surgical tools suitable for use with the present disclosure are described in U.S. Patent Application Publication No. 2019 / 0201137 (A1) (U.S. Patent Application No. 16 / 209,407), entitled "METHOD OF ROBOTIC HUB COMMUNICATION, DETECTION, AND CONTROL," filed on Dec. 4, 2018, the disclosure of which is hereby incorporated by reference in its entirety.

[0070] Various examples of cloud-based analysis methods implemented by a cloud computing system 20008 and suitable for use with the present disclosure are described in U.S. Patent Application Publication No. 2019-0206569 (A1) (U.S. Patent Application No. 16 / 209,403), entitled "METHOD OF CLOUD BASED DATA ANALYTICS FOR USE WITH THE HUB," filed on Dec. 4, 2018, the disclosure of which is hereby incorporated by reference in its entirety.

[0071] In various aspects, the imaging device 20030 may include at least one image sensor and one or more optical components. Suitable image sensors may include, but are not limited to, Charge-Coupled Device (CCD) sensors and Complementary Metal-Oxide Semiconductor (CMOS) sensors.

[0072] The optical components of the imaging device 20030 may include one or more illumination sources and / or one or more lenses. The one or more illumination sources may be directed to illuminate a portion of the surgical field. The one or more image sensors may be capable of receiving light reflected or refracted from the surgical field, including light reflected or refracted from tissue and / or surgical instruments.

[0073] The one or more illumination sources may be configured to irradiate electromagnetic energy within the visible spectrum as well as the invisible spectrum. The visible spectrum is sometimes also referred to as the optical spectrum or emission spectrum and is a portion of the electromagnetic spectrum that is visible to the human eye (i.e., detectable by the human eye) and may be referred to as visible light or simply light. Typically, the human eye responds to wavelengths of approximately 380 nm to approximately 750 nm in air.

[0074] The invisible spectrum (e.g., non-emission spectrum) is a portion of the electromagnetic spectrum that is located below and above the visible spectrum (i.e., wavelengths less than approximately 380 nm and greater than approximately 750 nm). The invisible spectrum is not detectable by the human eye. Wavelengths greater than approximately 750 nm are longer than the red visible spectrum and become invisible infrared (IR), microwaves, and radio electromagnetic radiation. Wavelengths less than approximately 380 nm are shorter than the violet spectrum and become invisible ultraviolet light, x-rays, and gamma ray electromagnetic radiation.

[0075] In various aspects, the imaging device 20030 is configured for use in minimally invasive procedures. Examples of imaging devices suitable for use with the present disclosure include, but are not limited to, arthroscopes, angioscopes, bronchoscopes, choledochoscopes, colonoscopes, cytoscopes, duodenoscopes, enteroscopes, esophagogastroduodenoscopes (gastroscopes), endoscopes, laryngoscopes, nasopharyngo-neproscopes, sigmoidoscopes, thoracoscopes, and ureteroscopes.

[0076] The imaging device may employ multispectral monitoring to distinguish topography from the underlying structure. A multispectral image captures image data within a specific wavelength range from across the electromagnetic spectrum. The wavelengths can be separated by filters or by using instruments with sensitivity to specific wavelengths including frequencies beyond the visible light range, e.g., IR, and light from ultraviolet. Spectral imaging enables extraction of additional information that cannot be captured by the red, green, and blue receptors of the human eye. The use of multispectral imaging is detailed 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 Dec. 4, 2018, the disclosure of which is incorporated herein by reference in its entirety. Multispectral monitoring can be a useful tool for repositioning the surgical field after a surgical task for performing one or more of the above-described tests on the treated tissue has been completed. It is understood that strict sterilization of the operating room and surgical instruments is required during any surgical procedure. The strict hygiene and sterilization conditions required in the “operating room,” i.e., the operating or treatment room, require the highest possible sterility of all medical devices and instruments. Part of the sterilization process is the need to sterilize anything that comes into contact with the patient or enters the sterile field, including the imaging device 20030 and its accessories and components. It will be understood that the sterile field can be considered a specific area considered to be free of microorganisms, such as within a tray or on a sterile towel, or the sterile field can be considered the area immediately surrounding a patient prepared for surgery. The sterile field can include properly attired and scrubbed team members, as well as all equipment and fixtures within that area.

[0077] The wearable sensing system 20011 shown in FIG. 1 may include one or more sensing systems, such as the HCP sensing system 20020 as shown in FIG. 2. The HCP sensing system 20020 may include a sensing system for monitoring and detecting a set of physical and / or physiological states of a healthcare provider (HCP). The HCP may generally be one or more healthcare providers assisting a surgeon or other healthcare service provider. In one example, the sensing system 20020 may measure a set of biomarkers to monitor the heart rate of the HCP. In one example, the sensing system 20020 (e.g., a watch or a wristband) worn on the wrist of a surgeon may use an accelerometer to detect hand movement and / or shake, and determine the magnitude and frequency of tremors. The sensing system 20020 may transmit measurement data associated with the set of biomarkers and data associated with the physical state of the surgeon to the surgical hub 20006 for further processing. One or more environmental sensing devices may transmit environmental information to the surgical hub 20006. For example, the environmental sensing device may include a camera 20021 for detecting the hand / body position of the HCP. The environmental sensing device may include a microphone 20022 for measuring ambient noise in the operating room. Other environmental sensing devices may include devices such as a thermometer for measuring temperature and a hygrometer for measuring ambient humidity in the operating room. The surgical hub 20006 may, alone or in communication with a cloud computing system, use the surgeon biomarker measurement data and / or environmental sensing information to, for example, modify the control algorithm of a handheld instrument or the average latency of a robotic interface to minimize tremors. In one example, the HCP 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 HCP sensing system 20020 may use one or more of the RF protocols of Bluetooth (registered trademark), Bluetooth Low-Energy (BLE), Bluetooth Smart, Zigbee, Z-wave, IPv6 Low-Power Wireless Personal Area Network (6LoWPAN), Wi-Fi to communicate with the surgical hub 20006. The surgeon biomarkers may include one or more of stress, heart rate, etc. The environmental measurements from the operating room may include the ambient noise level related to the movement of the surgeon or patient, surgeon and / or staff, the attention level of the surgeon and / or staff, etc.

[0078] The surgical hub 20006 may adaptively control one or more surgical instruments 20031 using the surgeon biomarker measurement data associated with the HCP. For example, the surgical hub 20006 may send a control program to the surgical instrument 20031 to control its actuator to limit or compensate for fatigue and the use of fine motor skills. The surgical hub 20006 may send the control program based on situation recognition and / or circumstances related to the importance or criticality of the task. The control program may instruct the instrument to change its operation to provide more control when control is needed.

[0079] Figure 3 shows an exemplary surgical system 20002 having a surgical hub 20006. The surgical hub 20006 can be 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 via a modular control unit. 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 aspects, as illustrated in FIG. 3, the hub 20006 further includes a smoke evacuation module 20054 and / or a suction / irrigation module 20055. The various modules and systems can be connected directly or via the communication module 20056 to the modular control unit via a router. Operating room devices can be coupled to cloud computing resources and data storage via the modular control unit. The human interface system 20012 can include a display subsystem and a notification subsystem.

[0080] The modular control unit may be connected to a non-contact sensor module. The non-contact sensor module may use ultrasonic, laser type, and / or similar non-contact measurement devices to measure the dimensions of the operating room and generate a map of the operating room. Other distance sensors can be used to determine the boundaries of the operating room. In U.S. Provisional Patent Application No. 62 / 611,341, filed December 28, 2017, entitled "INTERACTIVE SURGICAL PLATFORM," which is hereby incorporated by reference in its entirety, as described under the heading "Surgical Hub Spatial Awareness Within an Operating Room" in that document, an ultrasonic-based non-contact sensor module can scan the operating room by transmitting an ultrasonic burst and receiving the echo when the ultrasonic burst is reflected from the outer wall of the operating room. The sensor module may be configured to determine the size of the operating room and adjust the Bluetooth pairing distance limit. A laser-based non-contact sensor module can scan the operating room, for example, by transmitting a laser light pulse, receiving the laser light pulse reflected from the outer wall of the operating room, comparing the phase of the transmitted pulse with the received pulse to determine the size of the operating room, and adjusting the Bluetooth pairing distance limit.

[0081] During a surgical operation, applying energy to tissue for sealing and / or cutting is generally associated with smoke evacuation, aspiration of excess fluid, and / or perfusion of tissue. Fluid lines, power lines, and / or data lines from different sources often become entangled during a surgical operation. Valuable time may be lost in dealing with this problem during a surgical operation. To untangle the lines, it may be necessary to unplug the lines from their corresponding modules, and for this, it may be necessary to reset the modules. The hub module type enclosure 20060 provides an integrated environment for managing power lines, data lines, and fluid lines, reducing the frequency of such entanglements between the lines. Aspects of the present disclosure present a surgical hub 20006 for use in a surgical operation involving applying energy to tissue at a surgical site. The surgical hub 20006 includes a hub enclosure 20060 and a combined generator module slidably receivable within a docking station of the hub enclosure 20060. The docking station includes data contacts and power contacts. The combined generator module includes two or more of an ultrasonic energy generator component, a bipolar RF energy generator component, and a monopolar RF energy generator component housed within a single unit. In one aspect, the combined generator module also includes a smoke evacuation component, at least one energy supply cable for connecting the combined generator module to a surgical instrument, at least one smoke evacuation component configured to discharge smoke, fluid, and / or particulates generated by applying therapeutic energy to tissue, and a fluid line extending from a remote surgical site to the smoke evacuation component. In one aspect, the fluid line may be a first fluid line, and a second fluid line may extend from the remote surgical site to a suction and perfusion module 20055 slidably receivable within the hub enclosure 20060. In one aspect, the hub enclosure 20060 may include a fluid interface. A particular surgical operation may require applying two or more energy types to tissue.One type of energy may be more beneficial for cutting tissue, while a different type of energy 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 the sealed tissue. Aspects of the present disclosure present a solution where a hub module enclosure 20060 is configured to house different generators and facilitate two-way communication between them. One advantage of the hub module enclosure 20060 is that it allows for the quick 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 a first energy for application to tissue, and a first docking station including a first docking port that includes 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 also 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 different from the first energy for application to tissue, and a second docking station including a second docking port that includes second data contacts and second power contacts, wherein the second energy generator module is slidably movable to electrically engage with the power and data contacts, and the second energy generator module is also slidably movable to disengage from the electrical engagement with the second power and data contacts. Additionally, 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 FIG. 3, aspects of the present disclosure are presented regarding a generator module 20050, a smoke exhaust module 20054, and a hub module enclosure 20060 that enables modular integration of the aspiration / irrigation module 20055. The hub module enclosure 20060 further facilitates two-way communication between the module 20059, the module 20054, and the module 20055. The generator module 20050 may include integrated monopolar components, bipolar components, and ultrasonic components supported within a single housing unit slidably insertable into the hub's modular enclosure 20060. The generator module 20050 may be configured to connect to a monopolar device 20051, a bipolar device 20052, and an ultrasonic device 20053. Alternatively, the generator module 20050 may include a series of monopolar generator modules, bipolar generator modules, and / or ultrasonic generator modules that interact via the hub module enclosure 20060. The hub module enclosure 20060 can be configured to facilitate the insertion of multiple generators and two-way communication between the generators docked to the hub module enclosure 20060 such that the multiple generators function as a single generator.

[0082] FIG. 4 illustrates a surgical data network having a set of communication hubs configured to connect to a cloud, a set of sensing systems, an environmental sensing system, and a set of other modular devices disposed in one or more operating rooms, patient recovery rooms, or rooms within a medical facility specially equipped for surgery within a medical facility, according to at least one aspect of the present disclosure.

[0083] As illustrated in FIG. 4, the surgical hub system 20060 may include a modular communication hub 20065 configured to connect modular devices disposed within a medical facility to a cloud-based system (e.g., a cloud computing system 20064 that may include a remote server 20067 connected to a remote storage 20068). The modular communication hub 20065 and the devices may be connected in a room within a medical facility specially equipped for surgical procedures. In one aspect, the modular communication hub 20065 may include a network hub 20061 and / or a network switch 20062 that communicate with a network router 20066. The modular communication hub 20065 may also be connected to a local computer system 20063 and provide local computer processing and data manipulation.

[0084] The computer system 20063 may include a processor and a network interface 20100. The processor may be coupled via a system bus to a communication module, storage, memory, non-volatile memory, and an input / output (I / O) interface. 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 using any of a variety of available bus architectures, examples of which include a 9-bit bus, 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 bus (PCMCIA), Small Computer Systems Interface (SCSI), or any other proprietary bus, but is not limited thereto.

[0085] The processor may be any single-core or multi-core processor, such as those known by the product name ARM Cortex by Texas Instruments. In one aspect, the processor may be, for example, the LM4F230H5QR ARM Cortex-M4F processor core available from Texas Instruments. This processor core has on-chip memory of 256KB of single-cycle flash memory or other non-volatile memory with a maximum of 40MHz, a prefetch buffer for improving performance beyond 40MHz, 32KB of single-cycle serial random access memory (SRAM), an internal read-only memory (ROM) with StellarisWare® software, 2KB of 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.

[0086] In one example, the processor may comprise a safety controller with two controller-based families such as TMS570 and RM4x, also known by the product name Hercules ARM Cortex R4 from Texas Instruments. The safety controller may be configured specifically for safety-critical applications of IEC61508 and ISO26262, among others, to provide a highly integrated safety mechanism while offering scalable performance, connectivity, and memory options.

[0087] It should be understood that computer system 20063 may include software that functions as a medium between the described user and basic computer resources in a suitable operating environment. Such software may include an operating system. An operating system that may be stored on disk storage 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 system memory or on disk storage. It should be understood that the various components described herein may be implemented with various operating systems or combinations of operating systems.

[0088] A user can input commands or information into the computer system 20063 via an input device connected to the I / O interface. Examples of input devices include, but are not limited to, pointing devices such as a mouse, trackball, stylus, touchpad, keyboard, microphone, joystick, gamepad, satellite broadcast receiving antenna, scanner, TV tuner card, digital camera, digital video camera, web camera, etc. These and other input devices are connected to the processor 20102 through the system bus via an interface port. Examples of interface ports include, for example, serial ports, parallel ports, game ports, and USB. The output device uses some of the same type of ports as the input device. Thus, for example, a USB port may be used to provide input to the computer system 20063 and output information from the computer system 20063 to the output device. An output adapter may be provided to illustrate that among the output devices that may require a special adapter, there can be several output devices such as a monitor, display, speaker, and printer. Examples of output adapters include, but are not limited to, video and sound cards that provide connection means between the output device and the system bus. Note that other devices and / or systems of devices, such as remote computers, can provide both input and output functions.

[0089] The computer system 20063 can operate in a networked environment using logical connections to one or more remote computers, such as a cloud computer, or a local computer. The remote cloud computer can be, for example, a personal computer, a server, a router, a network PC, a workstation, a microprocessor-based device, a peer device, or other common network nodes, but typically includes many or all of the elements described with respect to the computer system. For the sake of brevity, only a memory storage device is illustrated together with the remote computer. The remote computer can be logically connected to the computer system via a network interface and subsequently physically connected via a communication connection. The network interface can include communication networks such as a local area network (LAN) and a wide area network (WAN). Examples of LAN technologies include Fiber Distributed Data Interface (FDDI), Copper Distributed Data Interface (CDDI), Ethernet / IEEE802.3, Token Ring / IEEE802.5, etc. Examples of WAN technologies include circuit-switched networks such as point-to-point links, Integrated Services Digital Network (ISDN) and its variations, packet-switched networks, and Digital Subscriber Line (DSL), but are not limited thereto.

[0090] In various examples, computer system 20063 may include an image processor, an image processing engine, a media processor, or any special digital signal processor (DSP) used for processing digital images. The image processor can enhance speed and efficiency using parallel computing with single instruction, multiple data (SIMD), or multiple instruction, multiple data (MIMD) techniques. The digital image processing engine can perform various tasks. The image processor may be a system on a chip with a multi-core processor architecture.

[0091] The communication connection part may refer to the hardware / software used to connect a network interface to a bus. For exemplary clarity, the communication connection part is shown inside computer system 20063, but the communication connection part may also be outside computer system 20063. For illustrative purposes only, the hardware / software required for connection to a network interface can include modems such as ordinary telephone grade modems, cable modems, fiber optic modems, and DSL modems, ISDN adapters, and internal and external technologies such as Ethernet cards. In some examples, the network interface may also be provided using an RF interface.

[0092] The surgical data network associated with the surgical hub system 20060 may be configured as passive, intelligent, or switching. A passive surgical data network functions as a conduit for data, enabling data to go from one device (or segment) to another device (or segment) and to cloud computing resources. An intelligent surgical data network enables traffic to pass through the surgical data network under surveillance and includes additional features that configure each port within the network hub 20061 or the network switch 20062. An intelligent surgical data network may be referred to as a manageable hub or switch. A switching hub reads the destination address of each packet and then forwards the packet to the correct port.

[0093] The modular devices 1a - 1n arranged in the operating room can be connected to the modular communication hub 20065. The network hub 20061 and / or the network switch 20062 can be connected to the network router 20066 to connect the devices 1a - 1n to the cloud computing system 20064 or the local computer system 20063. The data associated with the devices 1a - 1n may be transferred via the router to a cloud - based computer for remote data processing and operation. The data associated with the devices 1a - 1n can also be transferred to the local computer system 20063 for local data processing and operation. The modular devices 2a - 2m arranged in the same operating room may also be connected to the network switch 20062. The network switch 20062 can be connected to the network hub 20061 and / or the network router 20066 to connect the devices 2a - 2m to the cloud 20064. The data associated with the devices 2a - 2m can be transferred via the network router 20066 to the cloud computing system 20064 for data processing and operation. The data associated with the devices 2a - 2m may also be transferred to the local computer system 20063 for local data processing and operation.

[0094] The wearable sensing system 20011 may include one or more sensing systems 20069. The sensing system 20069 may include an HCP sensing system and / or a patient sensing system. One or more sensing systems 20069 may communicate with the computer system 20063 of the surgical hub system 20060 or the cloud server 20067 directly through one of the network routers 20066 or via a network hub 20061 or network switching 20062 that communicates with the network router 20066.

[0095] The sensing system 20069 can be connected 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 can be transferred via the network router 20066 to the cloud computing system 20064 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 manipulation.

[0096] As illustrated in FIG. 4, the surgical hub system 20060 can be extended by interconnecting a plurality of network hubs 20061 and / or a plurality of network switches 20062 with a plurality of network routers 20066. The modular communication hub 20065 can be housed within a modular control tower configured to receive a plurality of devices 1a - 1n / 2a - 2m. The local computer system 20063 may also be housed within the modular control tower. The modular communication hub 20065 can be connected to a display 20068 to display images obtained by some of the devices 1a - 1n / 2a - 2m, for example, during a surgical procedure. In various aspects, the devices 1a - 1n / 2a - 2m can include various modules such as an imaging module coupled to an endoscope, a generator module coupled to an energy-based surgical device, a smoke evacuation module, a suction / irrigation module, a communication module, a processor module, a storage array, a surgical device coupled to a display, and / or a non-contact sensor module, among other modular devices that can be connected to the modular communication hub 20065 of a surgical data network.

[0097] In one aspect, the surgical hub system 20060 illustrated in FIG. 4 may comprise a combination of network hub(s), network switch, and network router(s) that connect devices 1a-1n / 2a-2m, or sensing system 20069, to the cloud-based system 20064. One or more of devices 1a-1n / 2a-2m or sensing system 20069 coupled to 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 the "Internet," but this term is not so limited. Thus, the term "cloud computing" can be used herein to refer to "one type of Internet-based computing," in which case various services such as servers, storage, and applications are delivered via the Internet to modular communication hub 20065 and / or computer system 20063 located in an operating room (e.g., a fixed, mobile, temporary, or on-site operating room or space), and to devices connected to modular communication hub 20065 and / or computer system 20063. The cloud infrastructure may be maintained by a cloud service provider. In this context, a cloud service provider may be an entity that coordinates the use and control of devices 1a-1n / 2a-2m located in one or more operating rooms. Cloud computing services may perform a number of calculations based on data collected by smart surgical instruments, robots, sensing systems, and other computerized devices located in 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.

[0098] By applying cloud computing data processing technology to the data collected by devices 1a - 1n / 2a - 2m, the surgical data network can provide improvements in surgical outcomes, cost reduction, and patient satisfaction. After tissue sealing and cutting procedures, at least some of devices 1a - 1n / 2a - 2m can be used to observe the tissue state to evaluate leakage or perfusion of the sealed tissue. At least some of devices 1a - 1n / 2a - 2m can be used to examine data including images of samples of body tissues for diagnostic purposes using cloud - based computing to identify pathologies such as the effects of diseases. This can include tissue localization, margin confirmation, and phenotype. At least some of devices 1a - 1n / 2a - 2m can be used to identify the anatomical structures of the body using various sensors integrated with the imaging device and techniques such as overlaying images captured by multiple imaging devices. The data collected by devices 1a - 1n / 2a - 2m, including image data, can be transferred to a cloud computing system 20064 or a local computer system 20063 or both for data processing and operations including image processing and manipulation. The data may be analyzed to improve the results of surgery by determining whether further treatments such as endoscopic interventions, emerging technologies, targeted radiation, targeted interventions, and precision robotics for tissue - specific sites and conditions can be performed. Such data analysis may further employ prognostic analysis processing, and using standardized methods can provide useful feedback either to confirm surgical treatment and surgeon behavior or to propose modifications to surgical treatment and surgeon behavior.

[0099] When cloud computer data processing technology is applied to the measurement data collected by the sensing system 20069, the surgical data network can bring about improved surgical outcomes, improved recovery outcomes, reduced costs, and improved patient satisfaction. At least some of the sensing system 20069 may be used to evaluate the physiological state of a surgeon performing a surgery, a patient being prepared for surgery, or a patient recovering after surgery. The cloud-based computing system 20064 can monitor biomarkers associated with a surgeon or patient in real time, generate a surgical plan based at least on the measurement data collected before surgery, supply a control signal to a surgical instrument during surgery, and be used to notify a patient of complications during the postoperative period.

[0100] The operating room devices 1a to 1n can be connected to the modular communication hub 20065 via a wired channel or a wireless channel according to the configuration of the devices 1a to 1n with respect to the network hub 20061. In one aspect, the network hub 20061 may be implemented as a local network broadcast device that functions on the physical layer of the Open System Interconnection (OSI) model. The network hub can provide connectivity to the devices 1a to 1n located within the same operating room network. The network hub 20061 can collect data in the form of packets and transmit them to the router in half-duplex mode. The network hub 20061 cannot store any media access control / Internet Protocol (MAC / IP) for transferring device data. Only one of the devices 1a to 1n can transmit data at a time via the network hub 20061. The network hub 20061 cannot have a routing table or intelligence regarding the destination of information and broadcasts all network data across each connection and to the remote server 20067 of the cloud computing system 20064. The network hub 20061 can detect basic network errors such as collisions, but broadcasting all information to multiple ports can pose a security risk and cause bottlenecks.

[0101] The operating room devices 2a - 2m can be connected to the network switch 20062 via a wired channel or a wireless channel. The network switch 20062 functions within the data link layer of the OSI model. The network switch 20062 may be a multicast device for connecting the devices 2a - 2m arranged within the same operating room to the network. The network switch 20062 transmits data in the form of frames to the network router 20066 and can function in full - duplex mode. Multiple devices 2a - 2m can transmit data simultaneously via the network switch 20062. The network switch 20062 stores and uses the MAC addresses of the devices 2a - 2m for data transfer.

[0102] The network hub 20061 and / or the network switch 20062 can be connected to the network router 20066 to connect to the cloud computing system 20064. The network router 20066 functions within the network layer of the OSI model. The network router 20066 creates a route for transmitting the data packets received from the network hub 20061 and / or the network switch 20062 to cloud - based computer resources for further processing and operation of the data collected by any one or all of the devices 1a - 1n / 2a - 2m and the wearable sensing system 20011. The network router 20066 may be used to connect two or more different networks located at different positions, such as different operating rooms in the same medical facility or different networks in different operating rooms of different medical facilities. The network router 20066 transmits data in the form of packets to the cloud computing system 20064 and can function in full - duplex mode. Multiple devices can transmit data simultaneously. The network router 20066 can use IP addresses for data transfer.

[0103] In one example, the network hub 20061 may be implemented as a USB hub that enables a plurality of USB devices to be connected to a host computer. The USB hub can expand a single USB port into several tiers so that there are more available ports for connecting devices to the host system computer. The network hub 20061 may include a wired function or a wireless function for receiving information via a wired channel or a wireless channel. In one aspect, a wireless USB short-range high-bandwidth wireless communication protocol may be used for communication between devices 1a to 1n and devices 2a to 2m located within an operating room.

[0104] In an example, the operating room devices 1a - 1n / 2a - 2m and / or the sensing system 20069 may communicate with the modular communication hub 20065 via the Bluetooth wireless technology standard to exchange data over a short distance (using short - wavelength UHF radio waves in the 2.4 - 2.485 GHz ISM band) and to construct a personal area network (PAN). The operating room devices 1a - 1n / 2a - 2m and / or the sensing system 20069 may communicate with the modular communication hub 20065 via some wireless communication standards or wired communication standards or protocols, such as Bluetooth, Low - Energy Bluetooth, near - field communication (NFC), Wi - Fi (IEEE802.11 family), WiMAX (IEEE802.16 family), IEEE802.20, New Radio (NR), Long Term Evolution (LTE), as well as Ev - DO, HSPA+, HSDPA+, HSUPA+, EDGE, GSM, GPRS, CDMA, TDMA, DECT, and Ethernet derivatives of these, and not limited to these, any other wireless protocols and wired protocols designated for 3G, 4G, 5G, and later. The computing module may include a plurality of communication modules. For example, the first communication module may be dedicated to short - range wireless communication such as Wi - Fi and Bluetooth, Low - Energy Bluetooth, Bluetooth Smart, etc., and 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, TDMA, etc.

[0105] The modular communication hub 20065 functions as a central connection for one or more of the operating room devices 1a - 1n / 2a - 2m and / or the sensing system 20069 and can handle data types known as frames. The frame can carry data generated by the devices 1a - 1n / 2a - 2m and / or the sensing system 20069. When the frame is received by the modular communication hub 20065, the frame is amplified and sent to the network router 20066, which can transfer this data to the cloud computing system 20064 or the local computer system 20063 by using a number of wireless communication standards or wired communication standards or protocols as described herein.

[0106] The modular communication hub 20065 may be used as a stand - alone device or may be connected to compatible network hubs 20061 and network switches 20062 to form a larger network. Since the modular communication hub 20065 is generally easy to install, configure, and maintain, it can be a good option for networking the operating room devices 1a - 1n / 2a - 2m.

[0107] FIG. 5 illustrates a logic diagram of a control system 20220 for a surgical instrument or surgical tool according to one or more aspects of the present disclosure. The surgical instrument or surgical tool may be configurable. The surgical instrument may be a hand-held instrument such as an imaging device, a surgical stapler, an energy device, an end cutter device, and may include surgical supplies specific to the procedure. For example, the surgical instrument may include any of an electric stapler, an electric stapler generator, an energy device, a high energy device, a high energy jo device, an end cutter clamp, an energy device generator, an in-operative imaging system, a smoke evacuation device, a suction irrigation device, a pneumoperitoneum system, etc. The system 20220 may comprise a control circuit. The control circuit may include a microcontroller 20221 comprising a processor 20222 and a memory 20223. For example, one or more of sensors 20225, 20226, 20227 provide real-time feedback to the processor 20222. A motor 20230 driven by a motor driver 20229 is operably coupled to displaceable members movable in the longitudinal direction to drive an I-beam knife element. A tracking system 20228 may be configured to determine the position of the displaceable members movable in the longitudinal direction. The position information may be provided to a processor 20222 programmed or configured to determine the position of the longitudinally movable drive member as well as the position of the firing member, the firing bar, and the I-beam knife element. Additional motors may be provided to a tool driver interface to control the firing of the I-beam, the movement of the closure tube, the rotation of the shaft, and the articulation movement. A display 20224 may display various operating states of the instrument and may include a touch screen function for data input. The information displayed on the display 20224 may be overlaid with an image acquired via an endoscope imaging module.

[0108] The microcontroller 20221 may be any single-core or multi-core processor, such as those known by the product names of ARM Cortex from Texas Instruments. In one aspect, the main microcontroller 20221 may be, for example, an on-chip memory of 256KB single-cycle flash memory or other non-volatile memory with a maximum of 40MHz, the details of which are available in the product datasheet, a prefetch buffer for improving performance beyond 40MHz, 32KB single-cycle SRAM, an internal ROM with StellarisWare (registered trademark) software, 2KB EEPROM, one or more PWM modules, one or more QEI analogs, and / or one or more 12-bit ADCs with 12 analog input channels, which may be the LM4F230H5QR ARM Cortex-M4F processor core available from Texas Instruments.

[0109] The microcontroller 20221 may also include a safety controller with two controller-based families such as TMS570 and RM4x known by the product names of Hercules ARM Cortex R4 from Texas Instruments. The safety controller may be configured specifically for safety-critical applications of IEC61508 and ISO26262, among others, to provide a scalable performance, connectivity, and memory options while providing an advanced integrated safety mechanism.

[0110] The microcontroller 20221 may be programmed to perform various functions such as precise control over the speed and position of the knife and the articulation movement system. In one aspect, the microcontroller 20221 may include a processor 20222 and a memory 20223. The electric motor 20230 may be a brushed direct current (DC) motor with a gearbox and a mechanical coupling to the articulation movement section or the knife system. In one aspect, the motor driver 20229 may be an A3941 available from Allegro Microsystems, Inc. Other motor drivers may be readily substituted for use in the tracking system 20228 with an absolute positioning system. A detailed description of the absolute positioning system is described in U.S. Patent Application Publication No. 2017 / 0296213, published on October 19, 2017, titled "SYSTEMS AND METHODS FOR CONTROLLING A SURGICAL STAPLING AND CUTTING INSTRUMENT", which is hereby incorporated by reference in its entirety.

[0111] The microcontroller 20221 may be programmed to provide accurate control over the speed and position of the displacement member and the articulation movement system. The microcontroller 20221 may be configured to calculate a response within the software of the microcontroller 20221. The calculated response may be compared with the measured response of the actual system to obtain an "observed" response, which is used for actual feedback decision-making. The observed response may be a suitably adjusted value that balances the smooth and continuous nature of the simulated response with the measured response, which can detect external influences on the system.

[0112] The motor 20230 may be controlled by a motor driver 20229 and can also be used by a surgical instrument or a tool firing system. In various forms, the motor 20230 may be a brushed DC drive motor having a maximum rotational speed of about 25,000 RPM. In some examples, the motor 20230 may include 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 with field effect transistors (FETs). The motor 20230 may be powered by a power supply assembly removably attached to a handle assembly or a tool housing to supply control power to the surgical instrument or tool. The power supply assembly may include a battery that may include a number of battery cells connected in series that can be used as a power source for powering the surgical instrument or tool. In certain situations, the battery cells of the power supply assembly may be replaceable and / or rechargeable. In at least one example, the battery cell may be a lithium-ion battery that can be connectable to and separable from the power supply assembly.

[0113] 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 include a proprietary charge pump regulator, which supplies a full (>10V) gate drive to battery voltages up to 7V, enabling the A3941 to operate with a reduced gate drive up to 5.5V. A bootstrap capacitor may be used to supply the above-mentioned battery supply voltage required for the N-channel MOSFET. The internal charge pump for high-side drive enables DC (100% duty cycle) operation. The full bridge can be driven in fast or slow decay mode using diodes or synchronous rectification. In slow decay mode, current recirculation is possible through either the high-side FET or the low-side FET. The power FETs can be protected from shoot-through by a resistor-adjustable dead time. The integrated diagnostics indicate low voltage, overtemperature, and power bridge abnormalities and can be configured to protect the power MOSFETs under most short-circuit conditions. Other motor drivers may be easily substituted for use in the tracking system 20228 with an absolute positioning system.

[0114] The tracking system 20228 can include a controlled motor drive circuit configuration with a position sensor 20225 according to one aspect of the present disclosure. The position sensor 20225 for an absolute positioning system can supply a unique position signal corresponding to the position of the displacement member. In some examples, the displacement member can represent a longitudinally movable drive member having a rack of drive teeth for meshing engagement with a corresponding drive gear of a gear reduction assembly. In some examples, the displacement member can represent a firing member adapted and configured to include a rack of drive teeth. In some examples, the displacement member can represent a firing bar or an I-beam, each of which can be adapted and configured to include a rack of drive teeth. Thus, as used herein, the term displacement member can generally be used to refer to any movable member of a surgical instrument or tool, such as a drive member, a firing member, a firing bar, an I-beam, or any element that can be displaced. In one aspect, the longitudinally movable drive member can be coupled to the firing member, the firing bar, and the I-beam. Thus, the absolute positioning system can actually track the linear displacement of the I-beam by tracking the linear displacement of the longitudinally movable drive member. In various aspects, the displacement member may be coupled to any position sensor 20225 suitable for measuring linear displacement. Thus, the longitudinally movable drive member, the firing member, the firing bar, or the I-beam, or combinations thereof, may be coupled to any suitable linear displacement sensor. The linear displacement sensor may include a contact displacement sensor or a non-contact displacement sensor.The linear displacement sensor may include a linear variable differential transformer (LVDT), a differential variable reluctance transducer (DVRT), a slide potentiometer, a magnetic sensing system including a movable magnet and a series of linearly arranged Hall effect sensors, a magnetic sensing system including a fixed magnet and a series of linearly arranged movable Hall effect sensors, an optical detection system including a movable light source and a series of linearly arranged photodiodes or photodetectors, an optical sensing system including a fixed light source and a series of linearly arranged movable photodiodes or photodetectors, or any combination thereof.

[0115] The electric motor 20230 may include a rotatable shaft that operably interfaces with a gear assembly attached to engage a set of drive teeth or a rack on the displacement member. The 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 configuration of the gear ring and sensor may be connected to a linear actuator by a rack and pinion configuration or to a rotary actuator by spur gears or other connections. A power supply may supply 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 engaging a corresponding drive gear of a gear reduction assembly. The displacement member may represent a longitudinally movable firing member, firing bar, I-beam, or a combination thereof.

[0116] One rotation of the sensor element associated with the position sensor 20225 can correspond to a longitudinal linear displacement d1 of the displacement member, where d1 is the longitudinal linear distance that the displacement member moves from point "a" to point "b" after one rotation of the sensor element connected to the displacement member. The sensor device can be connected via a gear reduction device in which the position sensor 20225 completes one or more rotations with respect to the full stroke of the displacement member. The position sensor 20225 may complete multiple rotations with respect to the full stroke of the displacement member.

[0117] To provide a unique position signal for two or more rotations of the position sensor 20225, a series of switches (where n is an integer greater than 1) may be used alone or in combination with a gear reduction device. The state of the switch can 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 supplied to the microcontroller 20221. The position sensor 20225 of the sensor device can comprise a magnetic sensor, an analog rotational sensor such as a potentiometer, or an array of analog Hall effect elements that output a unique combination of position signals or values.

[0118] The position sensor 20225 can comprise any number of magnetic sensing elements such as, for example, magnetic sensors classified by whether they measure the total magnetic field or a vector component of the magnetic field. The technologies used to produce both types of magnetic sensors can encompass numerous aspects of physics and electronics. Technologies used for sensing magnetic fields include, among others, search coils, flux gates, optical pumping, nuclear precession, SQUIDs, Hall effect, anisotropic magnetoresistance, giant magnetoresistance, magnetic tunnel junctions, giant magnetic impedance, magnetostrictive / piezoelectric composites, magnetic diodes, magnetic transistors, optical fibers, magneto-optics, and microelectromechanical systems-based magnetic sensors.

[0119] The position sensor 20225 of the tracking system 20228 with an absolute positioning system may include a magnetic rotary absolute positioning system. The position sensor 20225 may be implemented as an AS5055EQFT single-chip magnetic rotary position sensor available from Austria Microsystems, AG. The position sensor 20225 is connected to the microcontroller 20221 to implement an absolute positioning system. The position sensor 20225 is a low-voltage and low-power component and may include four Hall effect elements in the area of the position sensor 20225 that can be arranged above the magnet. Also, a high-resolution ADC and a smart power management controller may be provided on the chip. A coordinate rotation digital computer (CORDIC) processor, also known as the digit-by-digit method and the border algorithm, may be provided to implement a concise and efficient algorithm for calculating hyperbolic and trigonometric functions that only require addition, subtraction, bit shifting, and table reference operations. The angular position, alarm bit, and magnetic field information may be transmitted to the microcontroller 20221 via a standard serial communication interface such as a serial peripheral interface (SPI) interface. The position sensor 20225 may provide a resolution of 12 bits or 14 bits. The position sensor 20225 may be an AS5055 chip provided in a small QFN16-pin 4×4×0.85 mm package.

[0120] Tracking system 20228 with an absolute positioning system may include and / or be programmed to implement a feedback controller such as a PID, state feedback, and adaptive controller. The power supply converts a signal from the feedback controller into a physical input to the system, in this case a voltage. Other examples include PWM of voltage, current, and force. In addition to the position measured by position sensor 20225, other sensors (sensors) may be provided to measure physical parameters of the physical system. In some embodiments, other sensors (sensors) may include those described in U.S. Patent No. 9,345,481, issued May 24, 2016, titled "STAPLE CARTRIDGE TISSUE THICKNESS SENSOR SYSTEM," which is hereby incorporated by reference in its entirety, U.S. Patent Application Publication No. 2014 / 0263552, published September 18, 2014, titled "STAPLE CARTRIDGE TISSUE THICKNESS SENSOR SYSTEM," which is hereby incorporated by reference in its entirety, and U.S. Patent Application No. 15 / 628,175, filed June 20, 2017, titled "TECHNIQUES FOR ADAPTIVE CONTROL OF MOTOR VELOCITY OF A SURGICAL STAPLING AND CUTTING INSTRUMENT," which is hereby incorporated by reference in its entirety, such as sensor arrangements. 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 a comparison and combination circuit to combine the calculated response with the measured response using algorithms such as weighted averages and theoretical control loops that drive the calculated response towards the measured response. The calculated response of the physical system may take into account characteristics such as mass, inertia, viscous friction, inductive resistance, etc. to predict how the state and output of the physical system will behave based on the input.

[0121] An absolute positioning system can provide the absolute position of a displacement member upon power-up of the instrument without retracting or advancing the displacement member to a reset (zero or home) position, which may require a conventional rotary encoder that simply counts the number of forward or backward steps taken by the motor 20230 to estimate the position of a device actuator, drive bar, knife, etc.

[0122] For example, a sensor 20226, such as a strain gauge or a micro strain gauge, may be configured to measure one or more parameters of the end effector, such as the amplitude of the strain exerted on the anvil during the clamping operation, which can indicate, for example, the closing force applied to the anvil. The measured strain can be converted into a digital signal and provided to the processor 20222. Instead of, or in addition to, the 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 tool. The I-beam is configured to engage a wedge thread, which is configured to cam the staple driver upward to eject a staple and deformably contact the anvil. The I-beam may also include a sharp cutting edge that can be used to cut tissue when the I-beam is advanced distally by a firing bar. Alternatively, a current sensor 20231 may be used to measure the current consumed by the motor 20230. The force required to advance the firing member can correspond to, for example, the current drawn by the motor 20230. The measured force can be converted into a digital signal and provided to the processor 20222.

[0123] For example, a strain gauge sensor 20226 may be used to measure the force applied to tissue by an end effector. To measure the force exerted by the end effector on the tissue being treated, the strain gauge may be coupled to the end effector. A system for measuring the force applied to tissue grasped by the end effector may include, for example, a strain gauge sensor 20226, such as a micro strain gauge, 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 the jaw members of the end effector during a clamping operation, which can indicate tissue compression. The measured strain can be converted to a digital signal and supplied to a processor 20222 of a microcontroller 20221. A load sensor 20227 may measure, for example, the force used to operate a knife element to cut tissue captured between an anvil and a staple cartridge. A magnetic field sensor may be used to measure the thickness of the captured tissue. The measurement of the magnetic field sensor may also be converted to a digital signal and provided to the processor 20222.

[0124] The measured values of tissue compression, tissue thickness, and / or the force required to close the end effector on the tissue, measured by sensors 20226, 20227, respectively, may be used by the microcontroller 20221 to characterize the selected position of the firing member and / or the corresponding value of the velocity of the firing member. In one embodiment, the memory 20223 may store techniques, equations, and / or look-up tables that may be used by the microcontroller 20221 during evaluation.

[0125] The control system 20220 of the surgical instrument or tool may also include a wired or wireless communication circuit for communicating with a surgical hub 20065 as shown in FIG. 4.

[0126] FIG. 6 shows an exemplary 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 through a local area network 20292 and / or a cloud network 20293 via a wired and / or wireless connection. 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 such that the adapter 20285 transmits force from the drive shaft to the loading unit 20287. The adapter 20285 or the loading unit 20287 may include a force gauge (not explicitly shown) disposed therein for measuring the force applied to 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 an in vivo loading unit, i.e., a multi-firing loading unit (MFLU), that allows a clinician to fire a plurality of fasteners multiple times without having to remove the loading unit 20287 from the surgical site to reload the loading unit 20287.

[0127] The first jaw 20291 and the second jaw 20290 may be configured to clamp tissue therebetween, fire a fastener through the clamped tissue, and cut the clamped tissue. The first jaw 20291 may be configured to fire at least one fastener multiple times, or may be configured to include a replaceable multi-firing fastener cartridge that can fire a plurality of fasteners (e.g., staples, clips, etc.) two or more times before being replaced. The second jaw 20290 may include an anvil that deforms or otherwise secures the fastener as the fastener is ejected from the multi-firing fastener cartridge.

[0128] The handle 20297 may include a motor coupled to the drive shaft so as to act on the rotation of the drive shaft. The handle 20297 may include a control interface for selectively activating the motor. The control interface may include buttons, switches, levers, sliders, touchscreens, and any other suitable input mechanism or user interface, which may be engaged by a clinician to activate the motor.

[0129] The control interface of the handle 20297 may communicate with a controller 20298 of the handle 20297 to selectively activate the motor and act on the rotation of the drive shaft. The controller 20298 may be disposed within the handle 20297 and may be configured to receive inputs from the control interface and adapter data from the adapter 20285 or loading unit data from the loading unit 20287. The controller 20298 may analyze inputs from the control interface and data received from the adapter 20285 and / or the loading unit 20287 to selectively activate the motor. The handle 20297 may also include a display viewable by a clinician during use of the handle 20297. The display may be configured to display portions of the adapter or loading unit data before, during, or after firing of the instrument 20282.

[0130] The adapter 20285 may include an adapter identification device 20284 disposed therein, and the loading unit 20287 may include a loading unit identification device 20288 disposed therein. The adapter identification device 20284 may communicate with the controller 20298, and the loading unit identification device 20288 may communicate with the controller 20298. It will be understood that the loading unit identification device 20288 may communicate with the adapter identification device 20284 that relays or passes communication from the loading unit identification device 20288 to the controller 20298.

[0131] Adapter 20285 may also include a plurality of sensors 20286 (one is shown) disposed therearound to detect various states of the adapter 20285 or the environment (e.g., whether the adapter 20285 is connected to the loading unit, whether the adapter 20285 is connected to the handle, whether the drive shaft is rotating, the torque of the drive shaft, the strain of the drive shaft, the temperature within the adapter 20285, the number of firings of the adapter 20285, the peak force of the adapter 20285 during firing, the total amount of force applied to the adapter 20285, the peak recoil force of the adapter 20285, the number of pauses of the adapter 20285 during firing, etc.). The plurality of sensors 20286 may provide an input to the adapter identification device 20284 in the form of a data signal. The data signals of the plurality of sensors 20286 may be stored within the adapter identification device 20284 or may be used to update the adapter data stored within the adapter identification device 20284. The data signals of the plurality of sensors 20286 may be analog or digital. The plurality of sensors 20286 may include a force gauge for measuring the force exerted on the loading unit 20287 during firing.

[0132] The handle 20297 and the 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 each other to transmit energy and signals therebetween). Additionally, or alternatively, the electrical interface may be a non-contact electrical interface for wirelessly transmitting (e.g., inductively transmitting) energy and signals therebetween. It is also contemplated that the adapter identification device 20284 and the controller 20298 may wirelessly communicate with each other via a wireless connection separate from the electrical interface.

[0133] The handle 20297 may include a transceiver 20283 configured to transmit instrument data from the controller 20298 to other components of the system 20280 (e.g., the LAN 20292, the cloud 20293, the console 20294, or the portable device 20296). The controller 20298 may also transmit instrument data and / or measurement data associated with one or more sensors 20286 to the surgical hub. The transceiver 20283 may receive data (e.g., cartridge data, loading unit data, adapter data, or other notifications) from the surgical hub 20270. The transceiver 20283 may receive data (e.g., cartridge data, loading unit data, or adapter data) from other components of the system 20280. For example, the controller 20298 may transmit instrument data including the serial number of a mounting adapter (e.g., the adapter 20285) attached to the handle 20297, the serial number of a loading unit (e.g., the loading unit 20287) attached to the adapter 20285, and the serial numbers of a plurality of fired fastener cartridges loaded into the loading unit to the console 20294. The console 20294 may then reply to the controller 20298 with data (e.g., cartridge data, loading unit data, or adapter data) associated with the attached cartridge, loading unit, and adapter, respectively. The controller 20298 may display a message on the local instrument display or, alternatively, transmit a message to the console 20294 or the portable device 20296 via the transceiver 20283 to display the message on the display 20295 or the portable device screen, respectively.

[0134] FIG. 7 illustrates a diagram of a situational awareness surgical system 5100 according to at least one aspect of the present disclosure. The data source 5126 can include, for example, a modular device 5102 (which can include sensors configured to detect parameters associated with a patient, an HCP, and the environment, and / or the modular device itself), a database 5122 (e.g., an EMR database including patient records), a patient monitoring device 5124 (e.g., a blood pressure (BP) monitor and an electrocardiography (EKG) monitor), an HCP monitoring device 35510, and / or an environmental monitoring device 35512. The surgical hub 5104 can be configured to derive context information regarding a surgical procedure from the data, for example, based on a particular combination of the received data or the particular order in which data is received from the data source 5126. The context information inferred from the received data can include, for example, the type of surgical procedure being performed, a particular step of the surgical procedure the surgeon is performing, the type of tissue being operated on, or the body cavity that is the subject of the procedure. This function, according to some aspects of the surgical hub 5104 for deriving or inferring information regarding a surgical procedure from the received data, can be referred to as "situational awareness." For example, the surgical hub 5104 can incorporate a situational awareness system, which is the hardware and / or programming associated with the surgical hub 5104 that derives context information regarding a surgical procedure from received data and / or surgical planning information received from an edge computing system 35514 or a corporate cloud server 35516.

[0135] The situation recognition system of the surgical hub 5104 can be configured to derive context information from data received from various different data sources 5126. For example, the situation recognition system can include a pattern recognition system, or a machine learning system (such as an artificial neural network), trained with training data, to correlate various inputs (such as data from the database 5122, the patient monitoring device 5124, the modular device 5102, the HCP monitoring device 35510, and / or the environmental monitoring device 35512) with corresponding context information regarding the surgical procedure. The machine learning system can be trained to accurately derive context information regarding the surgical procedure from the provided inputs. In an example, the situation recognition system can include a look-up table that stores pre-characterized context information regarding the surgical procedure in association with one or more inputs (or a range of inputs) corresponding to that context information. In response to a query with one or more inputs, the look-up table can return the corresponding context information of the situation recognition system to control the modular device 5102. In an example, the context information received by the situation recognition system of the surgical hub 5104 can be associated with a specific control adjustment, or a series of control adjustments, of one or more modular devices 5102. In an example, the situation recognition system can include a further machine learning system, a look-up table, or other such system that generates or reads one or more control adjustments of one or more modular devices 5102 when context information is provided as an input.

[0136] The surgical hub 5104 incorporating the situation awareness system can provide many advantages to the surgical system 5100. One advantage can include providing improved interpretation of sensed and collected data, which can improve the processing accuracy during the surgical procedure and / or the use of the data. Returning to the previous example, the situation awareness surgical hub 5104 can determine which type of tissue is being operated on, and thus, if an unexpectedly high force is detected to close the end effector of the surgical instrument, the situation awareness surgical hub 5104 can correctly accelerate or decelerate the motor of the surgical instrument according to the tissue type.

[0137] The type of tissue being operated on can affect the adjustments made to the compression speed and load threshold of a surgical stapling and cutting instrument for a specific tissue gap measurement. The situation awareness surgical hub 5104 can infer whether the surgical procedure being performed is a thoracic procedure or an abdominal procedure, whereby the surgical hub 5104 can determine whether the tissue clamped by the end effector of the surgical stapling and cutting instrument is lung tissue (in the case of a thoracic procedure) or stomach tissue (in the case of an abdominal procedure). The surgical hub 5104 can then appropriately adjust the compression speed and load threshold of the surgical stapling and cutting instrument according to the type of tissue.

[0138] The type of body cavity being operated on during a insufflation procedure can affect the function of the smoke evacuation device. The situation awareness surgical hub 5104 can determine whether the surgical site is under pressure (by determining that the surgical procedure is using insufflation) and can determine the type of procedure. Generally, since a certain type of procedure can be performed within a specific body cavity, the surgical hub 5104 can appropriately control the motor speed of the smoke evacuation device according to the body cavity being operated on. Thus, the situation awareness surgical hub 5104 can provide a consistent amount of smoke evacuation for both thoracic and abdominal procedures.

[0139] The type of procedure being performed can affect the energy level optimal for the operation of an ultrasonic surgical instrument or a radio frequency (RF) electrosurgical instrument. For example, in arthroscopic procedures, the end effector of an ultrasonic surgical instrument or an RF electrosurgical instrument is immersed in fluid and may require a higher energy level. The Situational Awareness Surgical Hub 5104 can determine whether the surgical procedure is an arthroscopic procedure. The Surgical Hub 5104 can then adjust the RF power level of the generator or the ultrasonic amplitude (e.g., “energy level”) to compensate for the fluid-filled environment. In connection therewith, the type of tissue being operated on can affect the energy level optimal for the operation of an ultrasonic surgical instrument or an RF electrosurgical instrument. The Situational Awareness Surgical Hub 5104 can determine which type of surgical procedure is being performed and then customize the energy level of the ultrasonic surgical instrument or the RF electrosurgical instrument, respectively, according to the tissue profile expected for the surgical procedure. Further, the Situational Awareness Surgical Hub 5104 can be configured to adjust the energy level of the ultrasonic surgical instrument or the RF electrosurgical instrument not only for each procedure but also over the course of the surgical procedure. The Situational Awareness Surgical Hub 5104 can determine which step of the surgical procedure is being performed or will be performed next and then update the control algorithm of the generator and / or the ultrasonic surgical instrument or the RF electrosurgical instrument to set the energy level to an appropriate value for the type of tissue expected according to the step of the surgical procedure.

[0140] In an example, the surgical hub 5104 can derive data from additional data sources 5126 in order to improve conclusions drawn from one data source 5126. The situation awareness surgical hub 5104 can augment data received from the modular device 5102 with context information constructed from other data sources 5126 regarding the surgical procedure. For example, the situation awareness surgical hub 5104 can be configured to determine whether hemostasis has occurred (e.g., whether bleeding has stopped at the surgical site) according to video or image data received from a medical imaging device. The surgical hub 5104 can be further configured to make a determination regarding the integrity of a staple line or tissue weld by comparing physiological measurements (e.g., blood pressure sensed by a BP monitor communicatively coupled to the surgical hub 5104) with visual or image data of hemostasis (e.g., from a medical imaging device communicatively coupled to the surgical hub 5104). The situation awareness system of the surgical hub 5104 can provide additional context when analyzing visualization data in consideration of physiological measurement data. The additional context can be useful when the visualization data may not be conclusive or may be incomplete by itself.

[0141] For example, if it is determined that the use of an instrument will be required in a subsequent step of a procedure, the situation awareness surgical hub 5104 can actively activate a generator to which an RF electrosurgical instrument is connected. Actively activating an energy source can enable the instrument to be ready for use as soon as a previous step of the procedure is completed.

[0142] The situation-aware surgical hub 5104 can determine whether the current or subsequent steps of a surgical procedure require different views or magnifications on the display according to the features of the surgical site that are expected to be seen by the surgeon. The surgical hub 5104 can actively change the displayed view (e.g., supplied from a medical imaging device for a visualization system) as appropriate, whereby the display automatically adjusts throughout the surgical procedure.

[0143] The situation-aware surgical hub 5104 can determine which step of a surgical procedure is being performed or will be performed next, and whether specific data or a comparison between data is required for that step of the surgical procedure. The surgical hub 5104 can be configured to automatically call up a data screen based on the step of the surgical procedure being performed, without waiting for the surgeon to request specific information.

[0144] During the setup of a surgical procedure or during the surgical procedure itself, errors can be checked. For example, the Situation Awareness Surgical Hub 5104 can determine whether the operating room is properly or optimally set up for the surgical procedure to be performed. The Surgical Hub 5104 can determine the type of surgical procedure being performed, read the corresponding checklist, product locations, or setup requirements (e.g., from memory), and then be configured to compare the current operating room layout to the standard layout for the type of surgical procedure that the Surgical Hub 5104 has determined is being performed. In some examples, the Surgical Hub 5104 can compare a list of items for the procedure and / or a list of devices paired with the Surgical Hub 5104 to the recommended or expected manifest of items and / or devices for a given surgical procedure. If there is any discrepancy between the lists, the Surgical Hub 5104 can provide an alert indicating that a particular modular device 5102, patient monitoring device 5124, HCP monitoring device 35510, environmental monitoring device 35512, and / or other surgical supplies are missing. In some examples, the Surgical Hub 5104 can determine, for example, the relative distance or relative position of the modular device 5102 and the patient monitoring device 5124 via proximity sensors. The Surgical Hub 5104 can compare the relative position of the devices to the layout recommended or expected for a particular surgical procedure. If there is any discrepancy between the layouts, the Surgical Hub 5104 can be configured to provide an alert indicating that the current layout of the surgical procedure deviates from the recommended layout.

[0145] The situation awareness surgical hub 5104 can determine whether a surgeon (or other HCP) is making a mistake or deviating from a series of actions expected during a surgical procedure. For example, the surgical hub 5104 can determine the type of surgical procedure being performed, read a corresponding list of steps or order of device use (e.g., from memory), and then compare the steps being taken or devices being used during the surgical procedure to the steps or devices expected for the type of surgical procedure that the surgical hub 5104 has determined is being performed. The surgical hub 5104 can provide an alert indicating that an unexpected action is being taken at a particular step in the surgical procedure or that an unexpected device is being utilized.

[0146] Surgical instruments (and other modular devices 5102) can be adjusted to suit the specific context of each surgical procedure (such as adjustment to different tissue types) and can verify actions during the surgical procedure. The next steps, data, and display adjustments can be provided to the surgical instruments (and other modular devices 5102) in the operating room according to the specific context of the procedure.

[0147] FIG. 8 shows an example of autonomous update of surgical device control algorithm(s) (49300). The control algorithm(s) may be pre-installed in the surgical device when the surgical device is manufactured. Each pre-installed control algorithm may be a baseline version and may be ready to be executed when the surgical device is operated as part of a surgical procedure. Each pre-installed control algorithm may be specific to the surgical device type associated with the surgical device. As shown, the surgical device 49305 (e.g., the modular device 9050 as described in FIG. 9) may be used in a surgical procedure 49302 within an operating room 49303. For example, the surgical device 49305 may be a surgical stapler pre-installed with a baseline control algorithm associated with controlling the closing force (FTC) and / or a baseline control algorithm associated with controlling the firing force (FTF), or a baseline control algorithm associated with controlling both FTC and FTF. The surgical device 49305 may be any of the modular devices as described herein.

[0148] When the surgical device 49305 is activated within the operating room 49303, the surgical device 49305 can communicate (e.g., pair or link) with the surgical hub 49306 (e.g., before the surgical device 49305 operates as part of a surgical procedure 49302). For example, in response to the surgical device 49305 communicating with the surgical hub 49306, it can be determined whether the pre-installed baseline control algorithm(s) of the activated surgical device 49305 is the latest version. In some examples, the surgical hub 49306 may push the latest version of the control algorithm (e.g., the latest version) to the surgical device 49305. In response thereto, the surgical device 49305 can determine whether the pre-installed baseline control algorithm is the same version as the last version of the control algorithm. If it is determined that the pre-installed baseline control algorithm is an old version, the surgical device 49305 can replace it with the latest version from the surgical hub 49306; otherwise, the surgical device 49305 discards the latest version from the surgical hub 49306. In some examples, the surgical device 49305 can communicate with the surgical hub 49306 to determine whether its pre-installed baseline control algorithm(s) is the latest version. The surgical device 49305 can request version information of the control algorithm available on the surgical hub 49306. If the version information indicates that the pre-installed baseline control algorithm is not the latest version, the surgical device 49305 can retrieve the latest version of the control algorithm from the surgical hub.

[0149] Surgical device 49305 can be operated (e.g., by a surgeon) in a surgical procedure 49302 that can be a partial lung resection. Perioperative data such as the motion data associated with surgical device 49305 can be sensed by surgical device 49305. For example, the motion data may include the waiting time before the firing is initiated. The motion data may include the FTC over time. The motion data includes the FTF over time. The motion data can be transmitted to surgical hub 49306. Perioperative data such as the result data of surgical procedure 49302 can be transmitted to surgical hub 49306. For example, the result data may include data indicating whether there was air or fluid leakage at the surgical site, whether the staples of a particular staple line were properly formed, and / or whether there was bleeding at the surgical site. The motion data and the result data can be paired as paired data 49304. The paired data 49304 may further include control algorithm information associated with the motion data. For example, the control algorithm information can include a unique identifier and a version number of the control algorithm that controls the FTC. The perioperative data is detailed in the detailed description of FIG. 194 in U.S. Patent Application Publication No. 20190206562 (A1) (U.S. Patent Application No. 16 / 209,416), filed on Dec. 4, 2018, entitled "Method of hub communication, processing, display, and cloud analytics", the disclosure of which is hereby incorporated by reference in its entirety.

[0150] Surgical hub 49306 can transmit paired data 49304 associated with surgical procedure 49302 to remote system 49312. The remote system can include a remote server 49314 (e.g., the analysis server 9070 described in FIG. 9) connected to a storage device 49310. The remote system can be a cloud computing system, such as 9100 described in FIGS. 15 and 17.

[0151] Surgical hub 49306 may transmit paired data 49308 associated with a plurality of surgical procedures 49302 to a remote system 49312 (e.g., after accumulating paired data 49304 from different surgical procedures over a period of time). The plurality of surgical procedures may be of one surgical type or two or more surgical types. Surgical hub 49306 may also transmit to remote system 49312 other perioperative data associated with a plurality of surgical procedures 49302, such as preoperative data including patient-specific information (e.g., age, employer, body mass index (BMI), or any data that can be used to verify the identity of the patient).

[0152] In one example, surgical hub 49306 may be placed within a data protection boundary 49322 associated with a healthcare facility (e.g., a hospital), such as a boundary for the Health Insurance Portability and Accountability Act (HIPAA). If paired data 49304 includes patient personal information such as age, employer, body mass index (BMI), or any data that can be used to verify the identity of the patient, surgical hub 49306 may edit paired data 49308 before transmitting it to remote system 49312. The editing process is detailed under the heading "Data Management and Collection" in U.S. Patent Application Publication No. 20190206562 (A1) (U.S. Patent Application No. 16 / 209,385), filed on December 4, 2018, with the title "Method of hub communication, processing, storage and display", the disclosure of which is hereby incorporated by reference in its entirety.

[0153] The remote system 49312 can receive paired data 49308. The remote system 49312 can (e.g., in response thereto) aggregate and / or analyze the received paired data 49308 to determine whether there is a correlation between the operation data and the result data. Based on the determination that there is a correlation between the operation data and the result data, the remote system 49312 may determine that an update to a control algorithm associated with a surgical device type (e.g., a surgical stapler) is necessary. For example, if the remote system 49312 determines that there is a correlation between an aspect of the control algorithm and a negative result, the remote system 49312 may determine that an updated control algorithm 49316 for the surgical device type is needed and may generate the updated control algorithm 49316.

[0154] The remote system 49312 may transmit an updated control algorithm 49316 to the surgical hub 49306 (e.g., in response to generating an updated control algorithm 49316). In response to receiving the updated control algorithm 49316, the surgical hub 49306 may push it to the paired surgical devices 49318 of the surgical device type associated with the updated control algorithm 49316 (e.g., if the corresponding control algorithm installed in the paired surgical device 49318 is an older version of the control algorithm 49316). For example, when a new surgical device communicates (e.g., pairs) with the surgical hub 49306, the surgical hub 49306 may push the updated control algorithm 49316 to the newly added surgical device. In one example, the surgical hub 49306 may push the updated control algorithm 49316 to the newly added surgical device and may not push it to the surgical devices that are communicating with the surgical hub and have already received the updated control algorithm 49316. In some examples, the surgical hub 49306 may push the updated control algorithm to the paired surgical devices that are communicating with the surgical hub 49306. The paired surgical devices may determine whether to update the corresponding algorithms installed in them, for example, as described herein.

[0155] FIG. 9 shows a block diagram of a computer-implemented adaptive surgical system 9060 configured to generate an adaptive control program update for a modular device 9050, according to at least one aspect of the present disclosure.

[0156] A modular device includes a module that can be received within a surgical hub (e.g., as described in connection with FIGS. 3 and 9), and a surgical device or instrument that can be connected to various modules. Examples of modular devices include, for example, intelligent surgical instruments, medical imaging devices, aspiration / irrigation devices, smoke evacuators, energy generators, ventilators, and inhalers. The various operations of the modular devices described herein can be controlled by one or more control algorithms. The control algorithms can be executed on the modular device itself, on the surgical hub to which a particular modular device is paired, or on both the modular device and the surgical hub (e.g., via a distributed computing architecture). In some examples, the control algorithm of a modular device can control the device based on data sensed by the modular device itself (i.e., by sensors within, on, or connected to the modular device). This data can be related to the patient during surgery (e.g., tissue characteristics or insufflation pressure), or can be related to the modular device itself (e.g., the speed of a advancing knife, motor current, or energy level). For example, the control algorithms for a surgical stapling instrument and a cutting instrument can control the speed at which the motor of the instrument drives the knife through tissue in accordance with the resistance generated by the knife as it advances.

[0157] "Intelligent" devices that include control algorithms that respond to sensed data can be an improvement over "dumb" devices that operate without considering the sensed data. However, if the control program of the device does not adapt or update over time in response to the collected data, the device may continue to repeat errors or otherwise continue to operate sub-optimally. In one example, the motion data collected by a modular device may be combined with the results of each surgery (or steps thereof). The combination may be sent to an analysis system. In one illustration, the surgical results can be inferred by a situational awareness system of a surgical hub to which the modular device is paired, as described in U.S. Patent Application No. 15 / 940654, entitled "SURGICAL HUB SITUATIONAL AWARENESS," which is hereby incorporated by reference in its entirety. The analysis system can analyze the data aggregated from a set of modular devices or a particular type of modular device to determine conditions under which the control program of the modular device being analyzed controls the modular device sub-optimally (e.g., if there are repeated failures or errors in the control program, or if an alternative algorithm performs better), or conditions under which healthcare providers are using the modular device sub-optimally. The analysis system can generate updates to modify or improve the control program of the modular device. Different types of modular devices can be controlled by different control programs, and thus the control program updates can be specific to the type of modular device that the analysis system determines is operating sub-optimally. The analysis system can then push the updates to the appropriate modular devices connected to the analysis system through the surgical hub.

[0158] In one example, a surgical system includes a surgical hub 9000, a plurality of modular devices 9050 communicatively coupled to the surgical hub 9000, and an analysis system 9100 communicatively coupled to the surgical hub 9000. Although a single surgical hub 9000 is shown, it should be noted that the surgical system 9060 can include any number of surgical hubs 9000 that may be connected to form a network of surgical hubs 9000 communicatively coupled to the analysis system 9010. In one example, the surgical hub 9000 includes a processor 9010 coupled to a memory 9020 for executing stored instructions, and a data relay interface 9030 through which data is transmitted to the analysis system 9100. In one example, the surgical hub 9000 further includes a user interface 9090 having an input device 9092 (e.g., a capacitive touch screen or a keyboard) for receiving input from a user and an output device 9094 (e.g., a display screen) for providing an output to the user. The output can include data from a query input by the user, a suggestion of a product or product configuration for use in a given surgery, and / or instructions for actions to be performed before, during, or after a surgical procedure. The surgical hub 9000 further includes an interface 9040 for communicatively coupling the modular device 9050 to the surgical hub 9000. In one aspect, the interface 9040 includes a transceiver that can be communicatively connected to the modular device 9050 via a wireless communication protocol. Examples of the modular device 9050 can include, for example, a surgical stapling and cutting instrument, an electrosurgical instrument, an ultrasonic instrument, a respirator, an insufflator, and a display screen. In one example, the surgical hub 9000 can be further communicatively coupled to one or more patient monitoring devices 9052, such as an EKG monitor or a BP monitor. In another example, the surgical hub 9000 can be further communicatively coupled to one or more databases 9054 or external computer systems, such as an EMR database of the medical facility in which the surgical hub 9000 is located.

[0159] When the modular device 9050 is connected to the surgical hub 9000, the surgical hub 9000 can sense or receive perioperative data from the modular device 9050 and then associate the received perioperative data with surgical outcome data. The perioperative data indicates how the modular device 9050 was controlled during the surgical procedure. The surgical outcome data includes data associated with the outcome from the surgical procedure (or a step thereof), which can include whether the surgical procedure (or a step thereof) had a positive or negative outcome. For example, the outcome data can include whether the patient developed postoperative complications from a particular surgery or whether there was a leak (e.g., bleeding or air leak) at a particular staple placement or incision line. The surgical hub 9000 can obtain the surgical outcome data by receiving data from an external source (e.g., from the EMR database 9054), by directly detecting the outcome (e.g., via one of the connected modular devices 9050), or by inferring the occurrence of the outcome through a situation recognition system. For example, data regarding postoperative complications can be retrieved from the EMR database 9054, and data regarding staple placement or incision line leaks can be directly detected or inferred by the situation recognition system. The surgical outcome data can be inferred by the situation recognition system from data received from various data sources, including the modular device 9050 itself, the patient monitoring device 9052, and the database 9054 to which the surgical hub 9000 is connected.

[0160] The Surgical Hub 9000 can send the data of the associated modular device 9050 and the result data to the analysis system 9100 for processing there. By sending both the perioperative data indicating how the modular device 9050 is controlled and the surgical result data, the analysis system 9100 can correlate the different modes of controlling the modular device 9050 with the surgical results for a specific treatment type. In one exemplary case, the analysis system 9100 includes a network of analysis servers 9070 configured to receive data from the Surgical Hub 9000. Each of the analysis servers 9070 can include a memory and a processor coupled to the memory that executes instructions stored for analyzing the received data. In some exemplary cases, the analysis servers 9070 are connected in a distributed computing architecture and / or utilize a cloud computing architecture. Based on this paired data, the analysis system 9100 can then learn the optimal or preferred operating parameters for various types of modular devices 9050, generate adjustments to the control programs of the in-field modular devices 9050, and then send (or "push") updates to the control programs of the modular devices 9050.

[0161] Further details regarding the computer-implemented bi-directional surgical system 9060, including the Surgical Hub 9000 and the various modular devices 9050 connectable thereto, are described herein.

[0162] To assist in the understanding of process 9100 shown in FIG. 9 and the other concepts described above, FIG. 10 shows a diagram of an exemplary analysis system 9100 for updating a surgical instrument control program, according to at least one aspect of the present disclosure. In one example, the surgical hub 9000 or a network of surgical hubs 9000 is communicatively coupled to the analysis system 9100, as shown above in FIG. 9. The analysis system 9100 is configured to filter and analyze data of the modular device 9050 associated with surgical outcome data to determine whether adjustments need to be made to the control program of the modular device 9050. The analysis system 9100 can then push updates to the modular device 9050 through the surgical hub 9000, if necessary. In the illustrated example, the analysis system 9100 includes a cloud computing architecture. The perioperative data of the modular device 9050 received by the surgical hub 9000 from their paired modular devices 9050 can include, for example, firing force (i.e., the force required to advance the cutting member of the surgical stapling instrument through tissue), closing force (i.e., the force required to clamp the jaws of the surgical stapling instrument on tissue), power algorithm (i.e., the change in power over time of an electrosurgical instrument or ultrasonic instrument in response to the internal state of the instrument and / or the tissue state), tissue characteristics (e.g., impedance, thickness, stiffness, etc.), tissue gap (i.e., the thickness of the tissue), and closing speed (i.e., the speed at which the jaws of the instrument are clamped and closed). It should be noted that the data of the modular device 9050 transmitted to the analysis system 9100 is not limited to a single type of data and can include multiple different data types paired with surgical outcome data. The surgical outcome data for a surgical procedure (or a step thereof) can include, for example, whether there was bleeding at the surgical site, whether there was air leakage or fluid leakage at the surgical site, and whether the staples of a particular staple line were properly formed.Surgical outcome data can further include, or be associated with, positive or negative outcomes, such as those determined by, for example, the Surgical Hub 9000 or the Analytics System 9100. Surgical outcome data corresponding to the data of the modular device 9050 and the perioperative data of the modular device 9050 can be paired with each other or otherwise associated with each other when they are uploaded to the analytics system 9100, such that the analytics system 9100 can recognize trends in surgical outcomes based on the underlying data of the modular device 9050 that produced each particular outcome. In other words, the analytics system 9100 can aggregate the data of the modular device 9050 and the surgical outcome data to search for trends or patterns within the underlying device modular data 9050 that can indicate adjustments that can be made to the control program of the modular device 9050.

[0163] In the illustrated example, an analytics system 9100 that executes a process 9200 described in connection with FIG. 9 receives the data of the modular device 9050 and the surgical outcome data at 9202. When sent to the analytics system 9100, the surgical outcome data can be associated with or paired with the modular device 9050 data corresponding to the operation of the modular device 9050 that caused a particular surgical outcome. The perioperative data of the modular device 9050 and the corresponding surgical outcome data can be referred to as a data pair. The data is shown as including a first group 9212 of data associated with successful surgical outcomes and a second group 9214 of data associated with negative surgical outcomes. For this particular example, a subset of the data 9212, 9214 received at 9202 by the analytics system 9100 is highlighted to further illustrate the concepts discussed herein.

[0164] For the first data pair 9212a, the data of the modular device 9050 includes the closing force over time (FTC), the firing force over time (FTF), the tissue type (parenchymal tissue), the tissue state (the tissue is from a patient suffering from emphysema and who has received radiation), how many times this has been fired at the instrument (the third time), an anonymized timestamp (to protect the patient's confidentiality while still allowing the analysis system to calculate elapsed time between firings and other such metrics), and an anonymized patient identifier (002). The surgical outcome data includes data indicating that there was no bleeding, which corresponds to a successful outcome (i.e., a successful firing of the surgical stapling instrument). For the second data pair 9212b, the data of the modular device 9050 includes the waiting time before the instrument is fired (corresponding to the first firing of the instrument), the FTC over time, the FTF over time (indicating that there was a force spike near the end of the firing stroke), the tissue type (1.1 mm blood vessel), the tissue state (the tissue has received radiation), how many times this has been fired at the instrument (once), an anonymized timestamp, and an anonymized patient identifier (002). The surgical outcome data includes data indicating that there was a leak, which corresponds to a negative outcome (i.e., a failed firing of the surgical stapling instrument). For the third data pair 9212c, the data of the modular device 9050 includes the waiting time before the instrument is fired (corresponding to the first firing of the instrument), the FTC over time, the FTF over time, the tissue type (1.8 mm blood vessel), the tissue state (no notable state), how many times this has been fired at the instrument (once), an anonymized timestamp, and an anonymized patient identifier (012). The surgical outcome data includes data indicating that there was a leak, which corresponds to a negative outcome (i.e., a failed firing of the surgical stapling instrument). It should be noted again that this data is intended for illustrative purposes only to aid in the understanding of the concepts discussed herein and should not be construed as limiting the data received and / or analyzed by the analysis system 9100 to generate control program updates.

[0165] When the analysis system 9100 receives perioperative data from the surgical hub 9000 to which it is communicatively connected at 9202, the analysis system 9100 proceeds to aggregate and / or store the data according to the type of procedure (or step thereof) associated with the data, the type of modular device 9050 that generated the data, and other such classifications. By collating the data accordingly, the analysis system 9100 can analyze the data set to identify a correlation between a particular method of controlling each particular type of modular device 9050 and a positive surgical outcome or a negative surgical outcome. Based on whether a particular method of controlling the modular device 9050 correlates with a positive surgical outcome or a negative surgical outcome, the analysis system 9100 can determine at 9204 whether the control program for the type of modular device 9050 should be updated.

[0166] In this particular example, the analysis system 9100 performs a first analysis 9216 of the dataset by analyzing the peak FTF 9213 (i.e., the maximum FTF for a particular firing of the surgical stapling instrument) against the number of firings 9211 for each peak FTF value. In this exemplary case, the analysis system 9100 can determine that there is no specific correlation between the peak FTF 9213 and the occurrence of positive or negative results for a particular dataset. In other words, there is no distinct distribution of the peak FTF 9213 for positive and negative results. Since there is no specific correlation between the peak FTF 9213 and positive or negative results, the analysis system 9100 thus determines that no control program update is required to address this variable. Further, the analysis system 9100 performs a second analysis 9216b of the dataset by analyzing the standby time 9215 before the instrument is fired against the number of firings 9211. For this particular analysis 9216b, the analysis system 9100 can determine that there are distinct negative result distributions 9217 and positive result distributions 9219. In this exemplary case, the negative result distribution 9217 has an average of 4 seconds and the positive result distribution has an average of 11 seconds. Thus, the analysis system 9100 can determine that there is a correlation between the standby time 9215 and the type of result of this surgical step. That is, the negative result distribution 9217 indicates that there is a relatively large proportion of negative results for standby times of 4 seconds or less. Based on this analysis 9216b demonstrating a large divergence between the negative result distribution 9217 and the positive result distribution 9219, the analysis system 9100 can then determine at 9204 that a control program update should be generated at 9208.

[0167] When the analysis system 9100 analyzes a data set and determines at 9204 that an adjustment to the control program for a particular modular device 9050 targeted by the data set improves the performance of the modular device 9050, the analysis system 9100 generates a control program update at 9208 accordingly. In this exemplary case, based on the analysis 9216b of the data set, the analysis system 9100 can determine that a control program update 9218 that recommends a waiting time exceeding 5 seconds prevents 90% of the distribution of negative results within a 95% confidence interval. Alternatively, based on the analysis 9216b of the data set, the analysis system 9100 can determine that a control program update 9218 that recommends a waiting time exceeding 5 seconds results in a greater proportion of positive results than negative results. Thus, the analysis system 9100 can determine that a particular type of surgical instrument should wait for more than 5 seconds before being fired under a particular tissue condition so that negative results are less than positive results. Based on either or both of these constraints for generating the control program update determined by the analysis system 9100 being satisfied by the analysis 9216b, the analysis system 9100 can generate at 9208 a control program update 9218 for the surgical instrument, whereby the surgical instrument imposes a waiting time of more than 5 seconds before a particular surgical instrument can be fired under a given situation, or the surgical instrument displays to the user a warning or recommendation that the user should wait at least 5 seconds before firing the instrument. The analysis system 9100 can utilize various other constraints when determining whether to generate at 9208 a control program update, such as whether the control program update reduces the proportion of negative results by a particular amount or whether the control program update maximizes the proportion of positive results.

[0168] After control program update 9218 is generated at 9208, the analysis system 9100 then sends the control program update 9218 of the appropriate type of modular device 9050 to the surgical hub 9000 at 9210. In one example, when the modular device 9050 corresponding to the control program update 9218 is next connected to the surgical hub 9000 that downloaded the control program update 9218, the modular device 9050 then automatically downloads the update 9218. In another example, the surgical hub 9000 controls the modular device 9050 according to the control program update 9218, rather than the control program update 9218 being sent directly to the modular device 9050 itself.

[0169] Figure 11 shows an exemplary process of autonomous update of a surgical device control algorithm. As shown, there may be a plurality of operating rooms 49303 within a medical facility (e.g., a hospital). The plurality of operating rooms 49303 may be within a data protection boundary 49322 associated with the medical facility (e.g., as described herein). The plurality of operating rooms 49303 may send paired data 49308 to a remote system 49401 (e.g., via a surgical hub 49306) (e.g., after editing patient personal information within the paired data 49308 and then crossing the data protection boundary 49322) as described herein.

[0170] The remote system 49401 (e.g., the remote system 49312) may include an autonomous update subsystem 49410. The autonomous update subsystem 49410 may include an analysis server 49414 (e.g., 49314 described in FIG. 8) and a data store 49410 (e.g., 49310 described in FIG. 8). The analysis server 49414 may include an aggregation process 49402 and an analysis process 49404.

[0171] The data store 49410 can store a control algorithm collection 49406. The control algorithm collection 49406 can include the latest versions of control algorithms. For example, each surgical device type may have one or more control algorithms associated with its operation. Such control algorithms for each surgical device type can be stored in the data store 49410. The unique identifier and version number of each control algorithm can be stored in the data store 49410.

[0172] After the remote system 49401 receives the paired data 49308, the autonomous update subsystem 49410 can determine whether the paired data 49308 should be discarded or used for the generation of control algorithm updates. In an example, the autonomous update subsystem 49410 can determine whether the control algorithms included in the paired data 49308 (e.g., the control algorithm information associated with the operation data portion of the paired data 49308) are up-to-date. If the version numbers associated with the control algorithms indicate that they are not the latest versions, the autonomous update subsystem 49410 can discard them (e.g., not perform further processing on them). If the version numbers associated with the control algorithms indicate that they are the latest version numbers, the autonomous update subsystem 49410 can perform further processing on the associated paired data / operation data.

[0173] The aggregation process 49402 can aggregate the paired data 49308 based on the surgical device type. The aggregation process 49402 can aggregate the paired data 49308 based on the surgical procedure type. The aggregation process 49402 can aggregate the paired data 49308 based on the surgical device type and the surgical procedure type. The aggregation process 49402 can aggregate the paired data 49308 based on any other such classification(s).

[0174] Based on the aggregated paired data, the analysis process 49404 can identify correlations (if any) between aspects of the motion data and result data (if any) associated with the surgical device. For example, the analysis process 49404 can perform an analysis of the data set (e.g., analysis 9216b in FIG. 10) by analyzing the waiting time 9215 before the surgical stapler is fired for the number of firings 9211. The analysis process 49404 can determine that there are distinct negative result distributions (e.g., 9217 in analysis 9216b as shown in FIG. 10) and positive result distributions (e.g., 9219 in analysis 9216b as shown in FIG. 10). In one example, the negative result distribution can have an average of 4 seconds, and the positive result distribution can have an average of 11 seconds. The analysis process 49404 may determine that there is a correlation between the waiting time (e.g., 9215 within analysis 9216b as shown in FIG. 10) and the type of result of this surgical step. That is, the negative result distribution can indicate that there is a relatively large proportion of negative results for waiting times of 4 seconds or less.

[0175] Based on this analysis, the analysis process 49404 can determine that an update to the control algorithm (e.g., an FTC control algorithm such as 49408) associated with controlling the waiting time should be generated for the surgical stapler. An updated control algorithm 49316 may be generated, and the updated control algorithm 49316 may include a constraint that the waiting time before firing must be at least 5 seconds. The updated control algorithm 49316 can be sent to the data store 49406 to update or replace the existing control algorithm 49408. The updated control algorithm 49316 can be sent to multiple operating rooms 49303 (e.g., surgical hubs 49306 within the operating room 49303).

[0176] The self-regulating update subsystem 49410 can autonomously update control algorithms as described herein when the remote system 49401 continues to receive paired data 49308 from multiple operating rooms 49303. Such paired data 49308 can be received from operating rooms 49303 within a medical facility, medical facilities within a geographical location, medical facilities within a geographical region, or medical facilities within various geographical areas.

[0177] In one example, the paired data 49308 can be received from a geographical region that has not previously transmitted the paired data 49308 to the remote system 49401. In such a case, the paired data 49308 from the geographical region can present different data patterns, for example, because surgeons / medical specialists can be trained to operate surgical devices differently. For example, based on the aggregated paired data (including the paired data 49308 from the new geographical region), the analysis process 49404 (e.g., analysis 9216b as shown in FIG. 10) can identify a new correlation between the standby time and the result data in the case of a surgical stapler. The negative result distribution can appear to have an average of 16 seconds. Based on this analysis, the analysis process 49404 can determine that another update to the control algorithm (e.g., the FTC control algorithm such as 49408) associated with controlling the standby time should be generated for the surgical stapler (9204). An updated control algorithm 49316 can be generated, and the updated control algorithm 49316 can include the constraint that the pre-firing standby time must be at least 5 seconds and less than 16 seconds. The updated control algorithm 49316 can be sent to the data store 49406 to update or replace the existing control algorithm 49408. The updated control algorithm 49316 can be sent to multiple operating rooms 49303 (e.g., the surgical hub 49306 within the operating room 49303).

[0178] FIG. 12 shows an exemplary process for autonomous update of a surgical device control algorithm. As shown, there may be a plurality of data protection boundaries 49322 associated with a corresponding medical facility (e.g., as described herein). A surgical hub 49306 within each 49322 may transmit paired data 49308 to a remote system 49312 (e.g., as described in FIGS. 14 and 17). In response, the surgical hub 49306 may receive an updated control algorithm 49316 from the remote system 49312 (e.g., as described in FIGS. 14 and 17).

[0179] Alternatively, a surgical hub 49306 within each 49322 may transmit paired data 49308 to an edge computing device 49502, which may, in response, aggregate and / or analyze the paired data 49308 to determine whether there is a correlation between the operational data and the result data. The edge computing device / system is described in detail in U.S. Patent Application No. 17 / 384,151, filed July 23, 2021, entitled "MULTI-LEVEL SURGICAL DATA ANALYSIS SYSTEM", the disclosure of which is hereby incorporated by reference in its entirety.

[0180] Similar to the remote system 49312 (or remote system 49401), the edge computing device 49502 can include an autonomous update subsystem 49410, as described in FIG. 11. The edge computing device 49502 can determine that an update is needed for a control algorithm associated with a surgical device type (e.g., a surgical stapler) based on a determination that there is a correlation between the operational data and the result data. For example, if the edge computing device 49502 determines that there is a correlation between an aspect of the control algorithm and a negative result, the remote system 49312 can determine that an updated control algorithm 49316 for the surgical device type is needed and can generate the updated control algorithm 49316. In response to generating the updated control algorithm 49316, the edge computing device 49502 can transmit the updated control algorithm 49316 to the surgical hub 49306.

[0181] Unlike the remote system 49312, the edge computing device 49502 is located within the data protection boundary 49322, and thus, the surgical hub 49306 can send the paired data 49308 to the edge computing device 49502 in its unedited form. In such a case, the aggregation process 49402 of the edge computing device 49502 can further aggregate the unedited paired data 49308 based on the patient's personal information, and the analysis process 49404 can identify the correlation(s) between the mode(s) of the operation data associated with the surgical device and the result data based on the patient's personal information. For example, the aggregation process 49402 of the edge computing device 49502 can aggregate the unedited paired data 49308 for each surgical device type and for each patient age group. In one example, the analysis process 49404 can perform an analysis (e.g., analysis 9216b of FIG. 10) to identify and determine the negative result distribution and the positive result distribution for each patient age group. In this way, an updated control algorithm for the surgical stapler (e.g., the updated FTC control algorithm) can include different restrictions for different age groups, such as the waiting time must be at least 5 seconds for patients under 20 years old, at least 7 seconds for patients between 20 and 50 years old, and at least 10 seconds for patients over 50 years old.

[0182] FIG. 13 is a flowchart of an exemplary process for autonomous update of a surgical device control algorithm (49500). The process can be executed by the remote system 49312 or the edge computing device 49502 described in FIG. 12.

[0183] At 49510, operation data related to a surgical operation is received. For example, first operation data associated with a first surgical operation and second operation data associated with a second surgical operation may be received. The first operation data may be associated with a first aspect of the control algorithm of the first surgical device. The second operation data may be associated with a first aspect of the control algorithm of the second surgical device. The first surgical device and the second surgical device may be of a first surgical device type. For example, the first operation data may be received from the first surgical device, and the second operation data may be received from the second surgical device. For example, the first operation data and the second operation data may be received from a surgical hub or two different surgical hubs.

[0184] At 49512, result data associated with a surgical operation can be received. For example, first result data associated with a first surgical operation can be received. Second result data associated with a second surgical operation may be received. For example, the first result data associated with the first surgical operation may be received from a surgical hub or a surgical visualization device.

[0185] At 49514, it is determined that the control algorithm is the latest control algorithm. For example, each of the control algorithm of the first surgical device and the control algorithm of the second surgical device may be determined to be the latest control algorithm associated with the first surgical device type.

[0186] At 49516, aggregated data can be generated based on the operation data and the result data. For example, the aggregated data may be generated based on at least the first operation data, the second operation data, the first result data, and the second result data. For example, the result data may include the first result data and the second result data.

[0187] In 49518, a correlation between a control algorithm and result data can be determined. For example, based on at least first aggregated data, a correlation between a first aspect of the latest control algorithm and the result data can be determined.

[0188] For example, the determined correlation may include a correlation between a first aspect of the latest control algorithm and a negative surgical result, and a correlation between a first aspect of the latest control algorithm and a positive surgical result.

[0189] In 49520, an updated control algorithm can be generated. For example, based on the determined correlation, an updated latest control algorithm may be generated. For example, the updated latest control algorithm can be published for a surgical device or a surgical hub to download via an interface. For example, the updated latest control algorithm can be sent to a plurality of surgical hubs.

[0190] Self-regulation for controlling the control algorithm and / or approach of a scope, a viewing instrument associated with the scope, and / or a display system associated with the scope can be used to configure device capture, device operation, and / or device display modes, for example, to maximize user control of communication data and displayed data.

[0191] The display data associated with the instrument can be autonomously adapted based on the recorded usage. Autonomous adjustment of the video stream and / or interpretation of a part of the video stream can be used to adapt system notifications and warnings. Based on the detected movement of the tool (e.g., via a camera or a scope), system parameters can be automatically adjusted. In an example, when the device moves closer and into a fixed position to start a usage sequence (such as cutting by an energy tool), the system may start to obtain a device that is ready to execute the associated usage tasks (such as heating the blade, performing safety and / or functionality checks). In an example, when the device moves away from the intended usage site (such as near an important structure) and / or when the device is out of the field of view, the device may be placed in a safe mode or continuous usage may be prevented.

[0192] Adjustment to the approach to the tissue(s) can be made based on the detected device position and / or the detected device usage. The way the device approaches the tissue being used can be video-captured. Based on this approach, the user can be notified of different approaches. The system can automatically attempt to compensate for an inadequate approach angle (e.g., associated with the device).

[0193] The calibration of control algorithms can be performed. Autonomous control algorithm calibration can be implemented so that different devices can operate on the same actual result. In one example, linear staplers can measure their corresponding speeds (e.g., cutting member forward speed or staple firing speed) as part of factory data collection. Such speed data can be aggregated against speed data associated with other linear staplers. For example, the aggregation can be performed in a remote system such as remote system 49312, or in an edge computing system such as edge computing device 49502. An updated speed algorithm(s) can be generated and pushed to the linear stapler for use in future surgeries. When used in a surgical procedure, the linear stapler may be able to execute a calibrated approach for optimized results (e.g., using the calibrated speed algorithm). In the example, different linear staplers may operate at different firing speeds within a certain manufacturing limit. This manufacturing difference can be used as an input for data aggregation / data analysis (e.g., 49402 and 49404 of FIG. 11) to determine an optimized algorithm output for the firing speed, for example, via calibration of the firing algorithm(s) using the firing speed data. Other calibrations (e.g., calibrations required based on factory settings) may be skipped or performed (e.g., after an optimized algorithm has been determined and other calibrations are no longer required).

[0194] Calibration(s) of control algorithm(s) associated with surgical device(s) can be performed during surgery. The surgical device can initiate firing. The surgical device may adjust (e.g., be able to adjust) a control algorithm associated with during and / or between firings, based at least on results during firing. In an example, the surgical device can detect that it has stopped (e.g., it may be possible to detect). The stopped surgical device may be able to automatically adjust its force gain (e.g., in an area where it should not have stopped), based at least on the detection that it has stopped. In an example, the articulation mechanism may degrade over time and may result in non-reproducible results. The articulation mechanism may include an articulation mechanism of an end effector, which may include articulation force, articulation angle, articulation direction, and adjustment thereof in the articulation process. The articulation mechanism is described in more detail by PCT Patent Application Publication No. 2018142277 (A1) entitled "Robotic Surgical System and Methods for Articulation Calibration" filed on January 30, 2018, the disclosure of which is incorporated herein by reference in its entirety. Such degradation of the articulation mechanism can shift the zero position. The surgical device may automatically adjust accordingly (e.g., in response thereto). For example, the surgical device may automatically adjust based on a visual indication of an angle or force for biasing the articulation system during trocar removal.

[0195] Calibration can be performed at regular time intervals. The frequency of calibration can be determined based on the system's last known calibration (e.g., the time associated with the system's last known calibration). In one example, the system can introduce (e.g., intentionally) some uncertainty into the automated optimization. In an example, the system can be outside its optimal calibration time window and still be within an acceptable limit for use. As a result, the uncertainty offset may continue to increment over time. The system can make recommendations and / or optimizations based on the associated uncertainty limits. For example, when the system makes recommendations and / or optimizations, it may not be permitted to optimize parameters or settings beyond the associated uncertainty limit(s). The system can perform checks, e.g., periodic checks. In one example, the system can perform a calibration check when the system is in an idle state or not operating. In one example, the system may perform one or more calibration checks that can be automatically performed at the start of a surgery and / or when a surgical device is connected to a surgical system (e.g., a robotic surgical system), the surgical device is initialized, or the surgical device is reset.

[0196] The system can perform a calibration error check. For example, the system may perform one or more checks on different regions to confirm whether the calibration was performed correctly. By evaluating data from different regions, the system can determine whether the calibration was actually effective or whether there was another problem within the system that could lead to an invalid calibration. The different regions can refer to a set of subsystem components. For example, an illumination or scope system can execute a sequence using an expected output to determine whether a calibration error is active. In one example, different subsystems can be utilized to move a motor and check whether the calibration was performed correctly.

[0197] Intra-operative adjustments to a surgical procedure can be performed. Based on surgical decisions within the control of the surgeon, the system may make adjustments to control algorithms and / or surgical device operations. In an example, the surgeon may clamp a surgical device for a given amount of time. The amount of time may be part of a recommended clamping time. The amount of time may be selected to achieve optimal performance of the surgical device. Based on available datasets, the surgical device can adjust its firing speed and accordingly optimize the results of stapling and cutting operations.

[0198] The boundaries of adjustments to the algorithm or to the device operation may be created to limit the surgical procedure and may be changed dynamically based on the progress of the surgery. In an example, the surgeon may fire a linear stapler across a combination of healthy and diseased tissue. Based on tissue measurements, the linear stapler may be loaded with a specific profile. When the stapler knife and thread transition from healthy to diseased tissue, the limits and parameters of the surgical device can be automatically adjusted to a new profile. In an example, the surgeon may be performing a sleeve gastrectomy on a patient and may perform an initial firing using a blue cartridge. The stapler may adjust its firing algorithm based on the dataset of the blue cartridge in response to measurements of compression time or tissue thickness. For the next firing, the surgeon may switch to a different cartridge, and as a result, the system may automatically load a new dataset for the limits or other parameters within the algorithm or the surgical device.

[0199] The following is a numbered list of embodiments, which may or may not be claimed. 1. A computing system for autonomous surgical device control algorithm adaptation, the computing system comprising a processor, the processor comprising Receiving first operation data associated with a first surgical procedure and second operation data associated with a second surgical procedure, wherein the first operation data is associated with a first aspect of a control algorithm of a first surgical device, the second operation data is associated with a first aspect of a control algorithm of a second surgical device, and the first surgical device and the second surgical device are of a first surgical device type, and Receiving first result data associated with the first surgical procedure and second result data associated with the second surgical procedure, and Determining that each of the control algorithm of the first surgical device and the control algorithm of the second surgical device is the latest control algorithm associated with the first surgical device type, and Generating first aggregated data based on at least the first operation data, the second operation data, the first result data, and the second result data, and Determining a correlation between the first aspect of the latest control algorithm and the result data based on at least the first aggregated data, and Generating an updated latest control algorithm based on the determined correlation. A computing system configured to perform the above. Advantageously, the system according to Embodiment 1 can improve surgical outcomes when a sub-optimal control algorithm is being used in one or more surgical devices, or when the control algorithms of one or more surgical devices are not well-suited to the surgical environment and context in which they are employed (e.g., surgical type, surgeon preference, mode of operation, etc.).

[0200] As a non-limiting example, the control algorithm can control one or both of the closing force (FTC) and the firing force (FTF). The first operation data / second operation data can include one or more of the FTC over time, the waiting time before firing is initiated, the FTF over time, tissue characteristics (e.g., impedance, thickness, rigidity, etc.), tissue gap (i.e., tissue thickness), tissue type, tissue state, the number of firings on the device, and the like. By comparing the first operation data and the second operation data with the first result and the second result, it may be possible to determine the correlation between the data and the result. For example, a longer waiting time before firing and / or a higher peak FTF may be associated with a more positive result for a particular tissue type / thickness for a particular treatment. Thus, generating an updated and up-to-date control algorithm can include including a longer waiting time and / or a higher peak FTF associated with a more positive result.

[0201] The operation data may include some variables about the way the device was used that enable a more accurate comparison of the operation data, such as the firing waiting time, FTC, FTF, tissue characteristics, etc. For example, a positive result may be associated with the difference between the operation data for one or more variables.

[0202] By aggregating the operation and result data for multiple surgical procedures performed with different settings, the system can generate result-improving control algorithm updates in a way that would not be possible with a system that depends on data for a single surgical device or a single procedure alone.

[0203] As an example of improving the result, the result of tissue resection can correspond to the integrity of the sealing line, and a seal with less leakage is equivalent to a more positive or improved result than a seal with more leakage. An improved result can also correspond to a reduction in the risk of complications, e.g., a reduction in the risks of device malfunction, seal line leakage, staple misfiring, etc.

[0204] 2. The first aspect of the control algorithm for each device is the computing system described in Embodiment 1, including at least one coefficient of the control algorithm, at least one operating parameter, at least one limit, and / or at least one setting, and / or at least one function, subroutine, process, and / or algorithm.

[0205] Receiving and using data for control algorithm coefficients, parameters, limits, or settings (e.g., sensed, detected, or recorded force values, time values, voltage / current values, displacement values, etc.) enables the system to efficiently and quickly determine candidate updates to the control algorithm in a computationally efficient manner based on the correlation between this data and the resulting data. Such simple inputs can be sufficient to generate control algorithm updates because statistical links between these inputs and the variations that occur as a result in surgical outcomes can be found in a non-resource-intensive manner.

[0206] Alternatively, instead of, or in addition to, the first aspect of the control algorithm of each device including at least one coefficient, at least one operating parameter, at least one limit, and / or at least one setting of the control algorithm, the first aspect of the control algorithm of each device can include at least one function, subroutine, process, and / or algorithm. For example, the first aspect of the control algorithm can include a power algorithm used by the surgical device / each surgical device. A "power algorithm" can be a change in power of a device (e.g., an electrosurgical device or an ultrasonic device) over time in response to the internal state of the device / each device and / or in response to the tissue state. For example, a computing system can determine a correlation between a positive result and a specific power algorithm that involves treating a small tissue size with low power and a large tissue size with high power, and then generate a new control algorithm that includes this same power algorithm or a similar power algorithm (e.g., a power algorithm that shares at least some characteristics / behaviors with this power algorithm) for use in other surgical devices, such that they treat a small tissue size with low power and a large tissue size with high power.

[0207] 3. Generating an updated and latest control algorithm includes replacing at least one coefficient, operating parameter, limit, and / or setting, and / or power algorithm associated with the control algorithm for the first surgical device and the second surgical device with at least one updated coefficient, operating parameter, limit, and / or setting, and / or power algorithm associated with the updated and latest control algorithm, for the computing system according to Embodiment 1 or Embodiment 2.

[0208] Replacing / updating the coefficients / parameters / limits / settings / power algorithms of the control algorithm is a particularly advantageous means of generating a control algorithm update. This is because the new control algorithm can be generated based on the old control algorithm, using it as a "template", without the need to make large-scale modifications to the algorithm or build it "from scratch".

[0209] 4. The first operation data and the second operation data are received together with further operation data associated with a plurality of further surgical procedures, the further operation data being associated with a first aspect of the control algorithm of each surgical device, each surgical device being of a first surgical device type, The first result data and the second result data are received together with further result data associated with a plurality of further surgical procedures, Generating the first aggregated data is also a computing system according to any one of Embodiments 1 to 3, based at least on further operation data and further result data.

[0210] The greater the amount of surgical procedures and the diversity of surgical procedures for which data is supplied to the computing system, the smaller the impact on the determined correlation of outliers in the data, and the greater the degree to which variables that can potentially affect the surgical outcome (e.g., surgeon skill and experience, time, location, patient age and health status, etc.) are reduced and controlled in the analysis. Therefore, it may be advantageous to use data for a plurality of further surgical procedures in combination with data from the first surgical procedure and the second surgical procedure.

[0211] 5. A computing system according to any one of Embodiments 1 to 4, wherein the surgical procedure is a past surgical procedure.

[0212] 6. Each of the surgical devices is mechanically identical and / or the surgical devices differ functionally only in their respective control algorithms, the computing system according to any one of Embodiments 1 to 5.

[0213] Using data from surgical devices that are mechanically the same (e.g., same manufacturer, model, and / or brand) and / or whose functional differences can be entirely attributed to the specific control algorithms used in each device can be advantageous because doing so can substantially simplify data aggregation and analysis (and the generation of control algorithm updates). For example, if two instruments are mechanically identical, the various (e.g.) force / voltage / current / speed values output by the devices can be analyzed without having to account for differences between the specific structural differences between one device and another.

[0214] 7. Each surgical procedure is of the same surgical procedure type, the computing system according to any one of Embodiments 1 to 6.

[0215] Advantageously, using data from a single common surgical procedure type enables the generation of more detailed and specific control algorithm updates beyond providing general improvements, generating control algorithms well-suited to those specific surgical tasks.

[0216] 8. Determining the correlation between a first aspect of a current control algorithm and result data determining that the first result data represents a more positive result of a surgical procedure or a step thereof than the second result data determining the difference in the first aspect of the control algorithms used in the first surgical procedure and the second surgical procedure and associating the difference in the first aspect of the control algorithm with the more positive result, the computing system according to any one of Embodiments 1 to 7.

[0217] Advantageously, the above embodiments enable the improvement of surgical results to be attributed to specific device settings or parameters that cause them, so that success can be reproduced across other instruments by including the settings / parameters in the control algorithm update.

[0218] As an example, the results of the outcome data can correspond to the integrity of the seal line, and a leaking seal is not equivalent to a more positive or improved result than a more leaking seal. As another example, a more positive result can be one without surgical complications, while a negative result can be one with one or more complications, such as instrument malfunction, seal line leakage, staple misfiring, etc.

[0219] A more positive result can also be associated with more results that are more positive than negative, for example, increasing the percentage of positive results.

[0220] 9. Generating an updated and latest control algorithm includes replacing at least one coefficient, operating parameter, limit, and / or setting with a coefficient, operating parameter, limit, and / or setting associated with the control algorithm used in at least one surgery that resulted in a positive outcome, for the computing system of embodiment 8.

[0221] 10. Generating an updated and latest control algorithm includes considering the difference in outcome data between at least a first surgery and a second surgery to be due to the difference in a first aspect of the control algorithm used in the procedure, for the computing system according to any one of embodiments 1 to 9.

[0222] 11. The computing system is one of an edge computing system or a cloud computing system, for the computing system according to any one of embodiments 1 to 10.

[0223] 12. The computing system according to any one of Embodiments 1 to 11, wherein the first operation data is received from the first surgical device and the second operation data is received from the second surgical device.

[0224] Receiving the operation data directly from the device can improve the processing speed of the entire system and reduce the delay in generating (and optionally distributing) control algorithm updates. Further, the need for additional storage devices and / or the need for any compression / truncation / rounding of the operation data, which may otherwise result in loss of fidelity, can be eliminated.

[0225] 13. The computing system according to any one of Embodiments 1 to 12, wherein the first operation data and the second operation data are received from a surgical hub or two different surgical hubs.

[0226] Receiving all operation data from the same hub results in a more consistent system with less latency and reduced network requirements. By receiving data from two different hubs, it becomes possible to use data from geographically different surgical environments for analysis and draw new conclusions that are not apparent from data from a single surgical environment alone.

[0227] 14. The computing system according to any one of Embodiments 1 to 13, wherein first result data associated with the first surgery is received from a surgical hub or a surgical visualization device.

[0228] 15. The computing system according to any one of Embodiments 1 to 14, wherein the processor is further configured to publish an updated and latest control algorithm for the surgical device or the surgical hub to download via the interface.

[0229] Making the updated latest control algorithm available for download enables the benefits associated with the improved control algorithm to be more readily obtained across a wide range of devices in potentially geographically different surgical environments, such that these benefits would otherwise have to be obtained through slower alternative means for obtaining the new control algorithm in these other environments.

[0230] 16. The computing system according to any one of Embodiments 1 to 15, wherein the processor is further configured to send the updated latest control algorithm to a plurality of surgical hubs and / or a plurality of surgical devices.

[0231] Sending the updated control algorithm to the hub and / or device enables the benefits associated with the improved control algorithm to be more readily obtained across a wide range of devices in potentially geographically different surgical environments, such that these benefits would otherwise have to be obtained through slower alternative means for obtaining the new control algorithm in these other environments.

[0232] 17. The computing system according to any one of Embodiments 1 to 16, wherein the determined correlation includes a correlation between a first aspect of the latest control algorithm and a negative surgical outcome, and a correlation between the first aspect of the latest control algorithm and a positive surgical outcome.

[0233] As an example, surgical results can correspond to the integrity of the sealing line, and a seal with less leakage is equivalent to a positive result or an improved result, while a seal with more leakage is a negative result or a reduction in the result (compared to others). A threshold can be set to classify positive or negative results, and in this example, it can be a threshold leakage. As another example, a more positive result can be a result without surgical complications, while a negative result can be a result with one or more complications, such as instrument malfunction, sealing line leakage, staple misfiring, etc.

[0234] 18. The result data is the computing system according to any one of Embodiments 1 to 17, including first result data and second result data.

[0235] 19. A smart surgical device, wherein the smart surgical device comprises a processor, and the processor receives an updated control algorithm associated with the smart surgical device from a surgical hub, determines that the updated control algorithm is more up-to-date than the control algorithm installed on the smart surgical device, replaces the control algorithm with the updated control algorithm, and is configured to operate using the updated control algorithm.

[0236] 20. The smart surgical device according to Embodiment 19, wherein replacing the control algorithm with the updated control algorithm includes replacing the coefficients associated with the control algorithm with updated coefficients associated with the updated control algorithm.

[0237] 21. A method for autonomous surgical device control algorithm adaptation, the method comprising Receiving first operation data associated with a first surgical procedure and second operation data associated with a second surgical procedure, wherein the first operation data is associated with a first aspect of a control algorithm of a first surgical device, the second operation data is associated with a first aspect of a control algorithm of a second surgical device, and the first surgical device and the second surgical device are of a first surgical device type, and Receiving first result data associated with the first surgical procedure and second result data associated with the second surgical procedure, and Determining that each of the control algorithm of the first surgical device and the control algorithm of the second surgical device is the latest control algorithm associated with the first surgical device type, and Generating first aggregated data based on at least the first operation data, the second operation data, the first result data, and the second result data, and Determining a correlation between the first aspect of the latest control algorithm and the result data based on at least the first aggregated data, and Generating an updated latest control algorithm based on the determined correlation. A method comprising.

[0238] Advantageously, the method according to Embodiment 21 can improve surgical outcomes when a sub - optimal control algorithm is being used in one or more surgical devices, or when the control algorithms of one or more surgical devices are not well - suited to the surgical environment and context in which they are employed (e.g., surgical type, surgeon preference, mode of operation, etc.).

[0239] As a non-limiting example, the control algorithm can control one or both of the closing force (FTC) and the firing force (FTF). The first operation data / second operation data can include one or more of the FTC over time, the waiting time before firing is initiated, the FTF over time, tissue characteristics (e.g., impedance, thickness, rigidity, etc.), tissue gap (i.e., tissue thickness), tissue type, tissue state, the number of firings on the instrument, etc. By comparing the first operation data and the second operation data with the first result and the second result, it may be possible to determine the correlation between the data and the result. For example, a longer waiting time and / or a higher peak FTF before firing may be associated with a more positive result for a particular tissue type / thickness for a particular treatment. Thus, generating an updated and up-to-date control algorithm can include including a longer waiting time and / or a higher peak FTF associated with a more positive result.

[0240] The operation data may include some variables about the way the device was used that enable a more accurate comparison of the operation data, such as the firing waiting time, FTC, FTF, tissue characteristics, etc. For example, a positive result may be associated with a difference between the operation data for one or more variables.

[0241] By aggregating the operation and result data for multiple surgical procedures performed with different settings, the method can generate result-improving control algorithm updates in a way that would not be possible with a method that depends on data for a single surgical device or treatment alone.

[0242] As an example of improving the result, the result of tissue resection can correspond to the integrity of the sealing line, and a seal with less leakage is equivalent to a more positive or improved result than a seal with more leakage. An improved result can also correspond to a reduction in the risk of complications, e.g., a reduction in the risks of instrument malfunction, sealing line leakage, staple misfiring, etc.

[0243] 22. The first aspect of the control algorithm for each device is the method according to Embodiment 21, including at least one coefficient of the control algorithm, at least one operating parameter, at least one limit, and / or at least one setting, and / or at least one function, subroutine, process, and / or algorithm.

[0244] Receiving and using data for control algorithm coefficients, parameters, limits, or settings (e.g., sensed, detected, or recorded force values, time values, voltage / current values, displacement values, etc.) enables the system to computationally efficiently and quickly determine candidate updates to the control algorithm based on the correlation between this data and the result data. Such simple inputs can be sufficient to generate control algorithm updates because statistical links between these inputs and the variations that occur as a result in the surgical outcome can be found in a non-resource-intensive manner.

[0245] Alternatively, instead of, or in addition to, the first aspect of the control algorithm of each device that includes at least one coefficient, at least one operating parameter, at least one limit, and / or at least one setting of the control algorithm, the first aspect of the control algorithm of each device can include at least one function, subroutine, process, and / or algorithm. For example, the first aspect of the control algorithm can include a power algorithm used by a surgical device / each surgical device. A "power algorithm" can be a change in power over time of a device (e.g., an electrosurgical device or an ultrasonic device) in response to the internal state of the device / each device and / or in response to the tissue state. For example, a computing system can determine a correlation between a positive result and a specific power algorithm that involves treating a small tissue size with low power and a large tissue size with high power, and then generate a new control algorithm that includes this same power algorithm or a similar power algorithm (e.g., a power algorithm that shares at least some characteristics / behaviors with this power algorithm) for use in other surgical devices, causing them to treat a small tissue size with low power and a large tissue size with high power.

[0246] 23. Generating an updated and latest control algorithm includes replacing at least one coefficient, operating parameter, limit, and / or setting, and / or a power algorithm associated with the control algorithm for the first surgical device and the second surgical device with at least one updated coefficient, operating parameter, limit, and / or setting, and / or a power algorithm associated with the updated and latest control algorithm, the method according to Embodiment 21 or Embodiment 22.

[0247] Replacing / updating the coefficients / parameters / limits / settings / power algorithms of the control algorithm is a particularly advantageous means of generating a control algorithm update. This is because the new control algorithm can be generated based on the old control algorithm, using it as a "template", without the need to make large-scale modifications to the algorithm or build it "from scratch".

[0248] 24. The first operation data and the second operation data are received together with additional operation data associated with a plurality of further surgical procedures, the additional operation data being associated with a first aspect of the control algorithm of each surgical device, each surgical device being of a first surgical device type, The first result data and the second result data are received together with additional result data associated with a plurality of further surgical procedures, Generating the first aggregated data is also a method according to any one of embodiments 21 to 23, based at least on additional operation data and additional result data.

[0249] The greater the amount of surgery and the diversity of the surgeries for which data is supplied, the smaller the impact on the determined correlation of outliers in the data, and the greater the degree to which variables that can potentially affect the surgical outcome (e.g., surgeon skill and experience, time, location, patient age and health status, etc.) are reduced and controlled in the analysis. Thus, it can be advantageous to use data for a plurality of further surgical procedures in addition to the data from the first and second surgical procedures.

[0250] 25. A method according to any one of embodiments 21 to 24, wherein the surgical procedure is a past surgical procedure.

[0251] 26. A method according to any one of embodiments 21 to 25, wherein each of the surgical devices is mechanically identical and / or the surgical devices differ functionally only in their respective control algorithms.

[0252] Using data from surgical devices that are mechanically the same (e.g., from the same manufacturer, model, and / or brand) and / or whose differences in function can be entirely attributed to the specific control algorithms used in each device can be advantageous because doing so can substantially simplify data aggregation and analysis (and the generation of control algorithm updates). For example, if two instruments are mechanically identical, the various (e.g.) force / voltage / current / speed values output by the devices can be analyzed without the need to account for differences between the specific structural differences between one device and another.

[0253] 27. The method according to any one of embodiments 21 to 26, wherein each surgical procedure is of the same surgical procedure type.

[0254] Advantageously, using data from a single common surgical procedure type enables the generation of more detailed and specific control algorithm updates beyond providing general improvements, generating control algorithms well-suited to those specific surgical tasks.

[0255] 28. Determining the correlation between a first aspect of a current control algorithm and result data, determining that the first result data represents a more positive result of a surgical procedure or a step thereof than the second result data, determining the difference in the first aspect of the control algorithms used in the first surgical procedure and the second surgical procedure, and associating the difference in the first aspect of the control algorithm with the more positive result, the method according to any one of embodiments 21 to 27.

[0256] Advantageously, the above embodiments enable the improvement of surgical outcomes to be attributed to the specific device settings or parameters that cause them, so that success can be reproduced across other instruments by including the settings / parameters in control algorithm updates.

[0257] As an example, the results of the result data can correspond to the integrity of the sealing line, and a leaking seal is not equivalent to a more positive or improved result than a more leaking seal. As another example, a more positive result can be a result without surgical complications, while a negative result can be a result with one or more complications, such as instrument malfunction, sealing line leakage, staple misfiring, etc.

[0258] A more positive result can also be associated with more results that are more positive than negative, for example, increasing the percentage of positive results.

[0259] 29. Generating an updated and latest control algorithm includes replacing at least one coefficient, operating parameter, limit, and / or setting with a coefficient, operating parameter, limit, and / or setting associated with a control algorithm used in at least one surgery that resulted in a positive result, the method according to embodiment 28.

[0260] 30. Generating an updated and latest control algorithm includes considering the difference in result data between at least a first surgery and a second surgery as being due to the difference in a first aspect of the control algorithm used in the treatment, the method according to any one of embodiments 21 to 29.

[0261] 31. The computing system is one of an edge computing system or a cloud computing system, the method according to any one of embodiments 21 to 30.

[0262] 32. The first operation data is received from a first surgical device, and the second operation data is received from a second surgical device, the method according to any one of embodiments 21 to 31.

[0263] Receiving the operation data directly from the device can improve the processing speed of the entire system and reduce the delay in generating (and optionally distributing) control algorithm updates. Further, the need for additional storage devices and / or the need for any compression / truncation / rounding of the operation data, which could otherwise result in a loss of fidelity, can be eliminated.

[0264] 33. The first operation data and the second operation data are received from a surgical hub or two different surgical hubs, and are the method according to any one of Embodiments 21 to 32.

[0265] Receiving all operation data from the same hub results in more connections, shorter latency, and reduced network requirements. By receiving data from two different hubs, it becomes possible to use data from geographically different surgical environments for analysis and draw new conclusions that are not apparent from data from a single surgical environment alone.

[0266] 34. The first result data associated with the first surgical procedure is received from a surgical hub or a surgical visualization device, and is the method according to any one of Embodiments 21 to 33.

[0267] 35. The processor is further configured to publish the updated and latest control algorithm for the surgical device or the surgical hub to download via the interface, and is the method according to any one of Embodiments 21 to 34.

[0268] Making the updated and latest control algorithm available for download enables the benefits associated with the improved control algorithm to be more easily obtained across a wide range of devices in potentially geographically different surgical environments, such that these other environments (which might otherwise have to obtain the new control algorithm via slower alternatives) can be reached.

[0269] 36. The method according to any one of embodiments 21 to 35, wherein the processor is further configured to send an updated and latest control algorithm to a plurality of surgical hubs and / or a plurality of surgical devices.

[0270] Sending the updated control algorithm to the hub and / or device enables it to be potentially deployed across a wide range of devices in geographically different surgical environments so that the benefits associated with the improved control algorithm can be more readily obtained in these other environments (where otherwise new control algorithms may have to be obtained via slower alternative means).

[0271] 37. The method according to any one of embodiments 21 to 36, wherein the determined correlation includes a correlation between a first aspect of the latest control algorithm and a negative surgical outcome, and a correlation between the first aspect of the latest control algorithm and a positive surgical outcome.

[0272] As an example, the surgical outcome can correspond to the integrity of the seal line, a seal with less leakage is equivalent to a positive or improved outcome, and a seal with more leakage is a negative outcome or a reduction in outcome (compared to others). A threshold can be set to classify positive or negative outcomes, and in this example, it can be a threshold leakage. As another example, a more positive outcome can be an outcome with no surgical complications, while a negative outcome can be an outcome with one or more complications, such as instrument malfunction, seal line leakage, staple misfiring, etc.

[0273] 38. The method according to any one of embodiments 21 to 37, wherein the result data includes first result data and second result data.

[0274] 39. The first surgical device and the second surgical device are both surgical end cutters or staple fastening devices, and the first operation data and the second operation data are related to one or more of control of an energy source, cutting, staple fastening, knob orientation, body orientation, body position, anvil jaw force, and reload alignment slot management, and are from any one of Embodiments 1 to 18 of the computing system described or from any one of Embodiments 21 to 38 of the method described.

[0275] 40. A computer program product for causing a computer to execute the method according to any one of Embodiments 21 to 39.

[0276] 41. A non-transitory computer-readable storage medium including computer-readable instructions that, when executed by a computer, cause the computer to execute the method according to any one of Embodiments 21 to 39.

[0277] The following are numbered aspects of the present disclosure, which may or may not be claimed. 1. A computing system for self-adaptive surgical device control algorithm adaptation, the computing system comprising: a processor, the processor receiving first operation data associated with a first surgical procedure and second operation data associated with a second surgical procedure, the first operation data being associated with a first aspect of a control algorithm of a first surgical device, the second operation data being associated with a first aspect of a control algorithm of a second surgical device, the first surgical device and the second surgical device being of a first surgical device type; receiving first result data associated with the first surgical procedure and second result data associated with the second surgical procedure; Determining that each of the control algorithm of the first surgical device and the control algorithm of the second surgical device is the latest control algorithm associated with the first surgical device type; Generating first aggregated data based on at least the first operation data, the second operation data, the first result data, and the second result data; Determining a correlation between the first aspect of the latest control algorithm and the result data based on at least the first aggregated data; A computing system configured to generate an updated latest control algorithm based on the determined correlation. 2. The computing system according to aspect 1, wherein the computing system is one of an edge computing system or a cloud computing system. 3. The computing system according to aspect 1, wherein the first operation data is received from the first surgical device and the second operation data is received from the second surgical device. 4. The computing system according to aspect 1, wherein the first operation data and the second operation data are received from a surgical hub or two different surgical hubs. 5. The computing system according to aspect 1, wherein the first result data associated with the first surgical procedure is received from a surgical hub or a surgical visualization device. 6. The computing system according to aspect 1, wherein the processor is further configured to publish the updated latest control algorithm for download by the surgical device or the surgical hub via an interface. 7. The computing system according to aspect 1, wherein the processor is further configured to send the updated latest control algorithm to a plurality of surgical hubs. 8. The computing system according to aspect 1, wherein the determined correlation includes a correlation between the first aspect of the latest control algorithm and a negative surgical result and a correlation between the first aspect of the latest control algorithm and a positive surgical result. 9. A computing system according to aspect 1, wherein the result data includes first result data and second result data. 10. A smart surgical device, wherein the smart surgical device comprises a processor, and the processor receives an updated control algorithm associated with the smart surgical device from a surgical hub, determines that the updated control algorithm is more up-to-date than the control algorithm installed on the smart surgical device, replaces the control algorithm with the updated control algorithm, and is configured to operate using the updated control algorithm. A smart surgical device. 11. The smart surgical device according to aspect 10, wherein replacing the control algorithm with the updated control algorithm includes replacing coefficients associated with the control algorithm with updated coefficients associated with the updated control algorithm. 12. A method for autonomous surgical device control algorithm adaptation, the method comprising: receiving first operation data associated with a first surgical procedure and second operation data associated with a second surgical procedure, wherein the first operation data is associated with a first aspect of a control algorithm of a first surgical device, the second operation data is associated with a first aspect of a control algorithm of a second surgical device, and the first surgical device and the second surgical device are of a first surgical device type; receiving first result data associated with the first surgical procedure and second result data associated with the second surgical procedure; determining that each of the control algorithms of the first surgical device and the second surgical device is the most up-to-date control algorithm associated with the first surgical device type; generating first aggregated data based on at least the first operation data, the second operation data, the first result data, and the second result data; Based on at least first aggregated data, determining a correlation between a first aspect of a latest control algorithm and result data; and generating an updated latest control algorithm based on the determined correlation. A method comprising these steps. 13. The method according to aspect 12, wherein the computing system is one of an edge computing system or a cloud computing system. 14. The method according to aspect 12, wherein the first operation data is received from a first surgical device and the second operation data is received from a second surgical device. 15. The method according to aspect 12, wherein the first operation data and the second operation data are received from one surgical hub or two different surgical hubs. 16. The method according to aspect 12, wherein the first result data associated with the first surgical procedure is received from a surgical hub or a surgical visualization device. 17. The method according to aspect 12, wherein the processor is further configured to publish the updated latest control algorithm for download by the surgical device or the surgical hub via an interface. 18. The method according to aspect 12, wherein the processor is further configured to send the updated latest control algorithm to a plurality of surgical hubs. 19. The method according to aspect 12, wherein the determined correlation includes a correlation between a first aspect of the latest control algorithm and a negative surgical result, and a correlation between a first aspect of the latest control algorithm and a positive surgical result. 20. The method according to aspect 12, wherein the result data includes first result data and second result data.

[0278] 〔Embodiment〕 (1) A computing system for autonomous surgical device control algorithm adaptation, the computing system comprising: a processor, the processor being: Receiving first operation data associated with a first surgical procedure and second operation data associated with a second surgical procedure, wherein the first operation data is associated with a first aspect of a control algorithm of a first surgical device, the second operation data is associated with a first aspect of a control algorithm of a second surgical device, and the first surgical device and the second surgical device are of a first surgical device type; Receiving first result data associated with the first surgical procedure and second result data associated with the second surgical procedure; Determining that each of the control algorithm of the first surgical device and the control algorithm of the second surgical device is the latest control algorithm associated with the first surgical device type; Generating first aggregated data based on at least the first operation data, the second operation data, the first result data, and the second result data; Determining a correlation between the first aspect of the latest control algorithm and the result data based on at least the first aggregated data; Generating an updated latest control algorithm based on the determined correlation. A computing system configured to perform the above. (2) The computing system according to Embodiment 1, wherein the first aspect of the control algorithm of each device includes at least one coefficient of the control algorithm, at least one operation parameter, at least one limit, and / or at least one setting, and / or at least one function, subroutine, process, and / or algorithm. (3) Generating the updated latest control algorithm includes replacing at least one coefficient, operating parameter, limit and / or setting, and / or power algorithm associated with the control algorithm for the first surgical device and the second surgical device with at least one updated coefficient, operating parameter, limit and / or setting, and / or power algorithm associated with the updated latest control algorithm, the computing system according to Embodiment 1 or Embodiment 2. (4) The first operation data and the second operation data are received together with further operation data associated with a plurality of further surgical procedures, the further operation data being associated with a first aspect of the control algorithm of each surgical device, each surgical device being of the first surgical device type, The first result data and the second result data are received together with further result data associated with the plurality of further surgical procedures, Generating the first aggregated data also includes the computing system according to any one of Embodiments 1 to 3, based at least on the further operation data and the further result data. (5) The surgical procedure is a past surgical procedure, the computing system according to any one of Embodiments 1 to 4.

[0279] (6) Each of the surgical devices is mechanically identical and / or the surgical devices are functionally different only in their respective control algorithms, the computing system according to any one of Embodiments 1 to 5. (7) Each surgical procedure is of the same surgical procedure type, the computing system according to any one of Embodiments 1 to 6. (8) Determining the correlation between the first aspect of the latest control algorithm and the result data includes determining that the first result data represents a more positive result of the surgical procedure or a step thereof than the second result data, Determining the difference in the first aspect of the control algorithm used in the first surgery and the second surgery; Associating the difference in the first aspect of the control algorithm with the more positive result, a computing system according to any one of embodiments 1 to 7. (9) Generating the updated latest control algorithm includes replacing at least one coefficient, operating parameter, limit, and / or setting with the coefficient, operating parameter, limit, and / or setting associated with the control algorithm used in the at least one surgery that produced the positive result, a computing system according to embodiment 8. (10) Generating the updated latest control algorithm includes considering the difference in the result data between at least the first surgery and the second surgery to be due to the difference in the first aspect of the control algorithm used in the procedure, a computing system according to any one of embodiments 1 to 9.

[0280] (11) The computing system is one of an edge computing system or a cloud computing system, a computing system according to any one of embodiments 1 to 10. (12) The first operation data is received from the first surgical device, and the second operation data is received from the second surgical device, a computing system according to any one of embodiments 1 to 11. (13) The first operation data and the second operation data are received from a surgical hub or two different surgical hubs, a computing system according to any one of embodiments 1 to 12. (14) The first result data associated with the first surgery is received from a surgical hub or a surgical visualization device, a computing system according to any one of embodiments 1 to 13. (15) The computing system according to any one of Embodiments 1 to 14, wherein the processor is further configured to publish the updated latest control algorithm for download by a surgical device or a surgical hub via an interface.

[0281] (16) The computing system according to any one of Embodiments 1 to 15, wherein the processor is further configured to transmit the updated latest control algorithm to a plurality of surgical hubs and / or a plurality of surgical devices. (17) The computing system according to any one of Embodiments 1 to 16, wherein the determined correlation includes a correlation between the first aspect of the latest control algorithm and a negative surgical outcome, and a correlation between the first aspect of the latest control algorithm and a positive surgical outcome. (18) The computing system according to any one of Embodiments 1 to 17, wherein the result data includes the first result data and the second result data. (19) A smart surgical device, wherein the smart surgical device comprises a processor, and the processor receives an updated control algorithm associated with the smart surgical device from a surgical hub, determines that the updated control algorithm is newer than the control algorithm installed on the smart surgical device, replaces the control algorithm with the updated control algorithm, and is configured to operate using the updated control algorithm. (20) The smart surgical device according to Embodiment 19, wherein replacing the control algorithm with the updated control algorithm includes replacing a coefficient associated with the control algorithm with an updated coefficient associated with the updated control algorithm.

[0282] (21) A method for adapting a self-regulating surgical device control algorithm, the method comprising: receiving first operation data associated with a first surgical procedure and second operation data associated with a second surgical procedure, wherein the first operation data is associated with a first aspect of a control algorithm of a first surgical device, the second operation data is associated with a first aspect of a control algorithm of a second surgical device, and the first surgical device and the second surgical device are of a first surgical device type; receiving first result data associated with the first surgical procedure and second result data associated with the second surgical procedure; determining that each of the control algorithm of the first surgical device and the control algorithm of the second surgical device is the latest control algorithm associated with the first surgical device type; generating first aggregated data based on at least the first operation data, the second operation data, the first result data, and the second result data; determining a correlation between the first aspect of the latest control algorithm and the result data based on at least the first aggregated data; generating an updated latest control algorithm based on the determined correlation. (22) The method according to embodiment 21, wherein the first aspect of the control algorithm of each device comprises at least one coefficient, at least one operation parameter, at least one limit, and / or at least one setting of the control algorithm, and / or at least one function, subroutine, process, and / or algorithm. (23) Generating the updated latest control algorithm includes replacing at least one coefficient, operating parameter, limit and / or setting, and / or power algorithm associated with the control algorithm for the first surgical device and the second surgical device with at least one updated coefficient, operating parameter, limit and / or setting, and / or power algorithm associated with the updated latest control algorithm, according to the method described in Embodiment 21 or Embodiment 22. (24) The first operating data and the second operating data are received together with further operating data associated with a plurality of further surgical procedures, the further operating data being associated with a first aspect of the control algorithm of each surgical device, and each surgical device being of the first surgical device type, The first result data and the second result data are received together with further result data associated with the plurality of further surgical procedures, Generating the first aggregated data also includes the method described in any of Embodiments 21 to 23, based at least on the further operating data and the further result data. (25) The method according to any of Embodiments 21 to 24, wherein the surgical procedure is a past surgical procedure.

[0283] (26) Each of the surgical devices is mechanically identical and / or the surgical devices are functionally different only in their respective control algorithms, according to the method described in any of Embodiments 21 to 25. (27) The method according to any of Embodiments 21 to 26, wherein each surgical procedure is of the same surgical procedure type. (28) Determining the correlation between the first aspect of the latest control algorithm and the result data includes determining that the first result data represents a more positive result of the surgical procedure or a step thereof than the second result data, Determining the difference in the first aspect of the control algorithm used in the first surgery and the second surgery; Associating the difference in the first aspect of the control algorithm with the more positive result, a method according to any one of embodiments 21 to 27. (29) Generating the updated latest control algorithm includes replacing at least one coefficient, operating parameter, limit, and / or setting with the coefficient, operating parameter, limit, and / or setting associated with the control algorithm used in the at least one surgery that produced the positive result, a method according to embodiment 28. (30) Generating the updated latest control algorithm includes considering at least the difference in the result data between the first surgery and the second surgery as being due to the difference in the first aspect of the control algorithm used in the procedure, a method according to any one of embodiments 21 to 29.

[0284] (31) The computing system is one of an edge computing system or a cloud computing system, a method according to any one of embodiments 21 to 30. (32) The first operation data is received from the first surgical device and the second operation data is received from the second surgical device, a method according to any one of embodiments 21 to 31. (33) The first operation data and the second operation data are received from a surgical hub or two different surgical hubs, a method according to any one of embodiments 21 to 32. (34) The first result data associated with the first surgery is received from a surgical hub or a surgical visualization device, a method according to any one of embodiments 21 to 33. (35) The method according to any one of embodiments 21 to 34, wherein the processor is further configured to publish the updated latest control algorithm for download by a surgical device or a surgical hub via an interface.

[0285] (36) The method according to any one of embodiments 21 to 35, wherein the processor is further configured to transmit the updated latest control algorithm to a plurality of surgical hubs and / or a plurality of surgical devices. (37) The method according to any one of embodiments 21 to 36, wherein the determined correlation includes a correlation between the first aspect of the latest control algorithm and a negative surgical outcome, and a correlation between the first aspect of the latest control algorithm and a positive surgical outcome. (38) The method according to any one of embodiments 21 to 37, wherein the result data includes the first result data and the second result data. (39) Both the first surgical device and the second surgical device are surgical end cutters or staple fastening devices, and the first operation data and the second operation data are related to one or more of control of an energy source, cutting, staple fastening, knob orientation, body orientation, body position, anvil jaw force, and reload alignment slot management. The computing system according to any one of embodiments 1 to 18 or the method according to any one of embodiments 21 to 38. (40) A computer program product for causing a computer to execute the method according to any one of embodiments 21 to 39.

[0286] (41) A non-transitory computer-readable storage medium including computer-readable instructions that, when executed by a computer, cause the computer to execute the method according to any one of embodiments 21 to 39.

Claims

Claim 1 A computing system for adapting an autonomous surgical device control algorithm, the computing system comprising: a processor, the processor being configured to: receive first motion data associated with a first surgical procedure and second motion data associated with a second surgical procedure, the first motion data being associated with a first aspect of a control algorithm of a first surgical device, the second motion data being associated with a first aspect of a control algorithm of a second surgical device, the first surgical device and the second surgical device being of a first surgical device type; receive first result data associated with the first surgical procedure and second result data associated with the second surgical procedure; determine that each of the control algorithm of the first surgical device and the control algorithm of the second surgical device is an up-to-date control algorithm associated with the first surgical device type; generate first aggregated data based at least on the first motion data, the second motion data, the first result data, and the second result data; determine a correlation between the first aspect of the up-to-date control algorithm and the result data based at least on the first aggregated data; generate an updated up-to-date control algorithm based on the determined correlation. A computing system configured to perform the above steps. Claim 2 The first aspect of the control algorithm of each device includes at least one coefficient, at least one operating parameter, at least one limit, and / or at least one setting of the control algorithm, and / or at least one function, subroutine, process, and / or algorithm. The computing system according to claim 1. Claim 3 Generating the updated latest control algorithm includes replacing at least one coefficient, operating parameter, limit and / or setting, and / or power algorithm associated with the control algorithm for the first surgical device and the second surgical device with at least one updated coefficient, operating parameter, limit and / or setting, and / or power algorithm associated with the updated latest control algorithm. The computing system according to claim 1 or claim 2.

4. The first operation data and the second operation data are received together with additional operation data associated with a plurality of additional surgical procedures, the additional operation data being associated with a first aspect of the control algorithm of each surgical device, and each surgical device being of the first surgical device type. The first result data and the second result data are received together with additional result data associated with the plurality of additional surgical procedures. Generating the first aggregated data also includes the computing system according to claim 1, based at least on the additional operation data and the additional result data.

5. The computing system according to claim 1, wherein the surgical procedure is a past surgical procedure.

6. Each of the surgical devices is mechanically identical and / or the surgical devices are functionally different only in their respective control algorithms. The computing system according to claim 1.

7. The computing system according to claim 1, wherein each surgical procedure is of the same surgical procedure type.

8. Determining the correlation between the first aspect of the latest control algorithm and the result data includes determining that the first result data represents a more positive result of the surgical procedure or a step thereof than the second result data; determining a difference in the first aspect of the control algorithm used in the first surgical procedure and the second surgical procedure; associating the difference in the first aspect of the control algorithm with the more positive result. The computing system according to claim 1.

9. Generating the updated latest control algorithm includes replacing at least one coefficient, operating parameter, limit, and / or setting with the coefficient, operating parameter, limit, and / or setting associated with the control algorithm used in the at least one surgery that produced the positive result. The computing system according to claim 8.

10. Generating the updated latest control algorithm includes considering the difference in the result data between at least the first surgery and the second surgery as being due to the difference in the first aspect of the control algorithm used in the procedure. The computing system according to claim 1.

11. The computing system is one of an edge computing system or a cloud computing system. The computing system according to claim 1.

12. The first operation data is received from the first surgical device, and the second operation data is received from the second surgical device. The computing system according to claim 1.

13. The first operation data and the second operation data are received from a surgical hub or two different surgical hubs. The computing system according to claim 1.

14. The first result data associated with the first surgery is received from a surgical hub or a surgical visualization device. The computing system according to claim 1.

15. The processor is further configured to publish the updated latest control algorithm for download by a surgical device or a surgical hub via an interface. The computing system according to claim 1.

16. The processor is further configured to transmit the updated latest control algorithm to a plurality of surgical hubs and / or a plurality of surgical devices. The computing system according to claim 1.

17. The determined correlation includes the correlation between the first aspect of the latest control algorithm and a negative surgical result, and the correlation between the first aspect of the latest control algorithm and a positive surgical result. The computing system according to claim 1.

18. The computing system according to claim 1, wherein the result data includes the first result data and the second result data.

19. A smart surgical device, wherein the smart surgical device comprises a processor, and the processor receives an updated control algorithm associated with the smart surgical device from a surgical hub, determines that the updated control algorithm is more up-to-date than the control algorithm installed on the smart surgical device, replaces the control algorithm with the updated control algorithm, and is configured to operate using the updated control algorithm.

20. The smart surgical device according to claim 19, wherein replacing the control algorithm with the updated control algorithm includes replacing a coefficient associated with the control algorithm with an updated coefficient associated with the updated control algorithm.

21. A method for autonomous surgical device control algorithm adaptation, the method comprising: receiving first operation data associated with a first surgical procedure and second operation data associated with a second surgical procedure, wherein the first operation data is associated with a first aspect of a control algorithm of a first surgical device, the second operation data is associated with a first aspect of a control algorithm of a second surgical device, and the first surgical device and the second surgical device are of a first surgical device type; receiving first result data associated with the first surgical procedure and second result data associated with the second surgical procedure; determining that each of the control algorithm of the first surgical device and the control algorithm of the second surgical device is the most up-to-date control algorithm associated with the first surgical device type; generating first aggregated data based on at least the first operation data, the second operation data, the first result data, and the second result data; determining a correlation between the first aspect of the most up-to-date control algorithm and the result data based on at least the first aggregated data; A method comprising generating an updated and latest control algorithm based on the determined correlation.

22. The first aspect of the control algorithm for each device includes at least one coefficient of the control algorithm, at least one operating parameter, at least one limit, and / or at least one setting of the control algorithm, and / or at least one function, subroutine, process, and / or algorithm, The method according to claim 21.

23. Generating the updated and latest control algorithm includes replacing at least one coefficient, operating parameter, limit, and / or setting associated with the control algorithm for the first surgical device and the second surgical device, and / or a power algorithm, with at least one updated coefficient, operating parameter, limit, and / or setting associated with the updated and latest control algorithm, and / or a power algorithm. The method according to claim 21 or claim 22.

24. The first operation data and the second operation data are received together with additional operation data associated with a plurality of further surgical procedures, and the additional operation data is associated with a first aspect of the control algorithm of each surgical device, and each surgical device is of the first surgical device type. The first result data and the second result data are received together with additional result data associated with the plurality of further surgical procedures. Generating the first aggregated data also includes the method according to claim 21, based at least on the additional operation data and the additional result data.

25. The method according to claim 21, wherein the surgical procedure is a past surgical procedure.

26. Each of the surgical devices is mechanically identical and / or the surgical devices are functionally different only in their respective control algorithms. The method according to claim 21.

27. The method according to claim 21, wherein each surgical procedure is of the same surgical procedure type.

28. Determining the correlation between the first aspect of the latest control algorithm and the result data includes Determining that the first result data represents a more positive result of the surgical procedure or a step thereof than the second result data. Determining a difference in the first aspect of the control algorithm used in the first surgery and the second surgery; Associating the difference in the first aspect of the control algorithm with the more positive result, the method of claim 21 comprising.

29. Generating the updated latest control algorithm includes replacing at least one coefficient, operating parameter, limit, and / or setting with the coefficient, operating parameter, limit, and / or setting associated with the control algorithm used in the at least one surgery that produced the positive result, the method of claim 28.

30. Generating the updated latest control algorithm includes considering at least the difference in the result data between the first surgery and the second surgery to be due to the difference in the first aspect of the control algorithm used in the procedure, the method of claim 21.

31. The computing system is one of an edge computing system or a cloud computing system, the method of claim 21.

32. The first operation data is received from the first surgical device, and the second operation data is received from the second surgical device, the method of claim 21.

33. The first operation data and the second operation data are received from a surgical hub or two different surgical hubs, the method of claim 21.

34. The first result data associated with the first surgery is received from a surgical hub or a surgical visualization device, the method of claim 21.

35. The processor is further configured to publish the updated latest control algorithm for download by a surgical device or a surgical hub via an interface, the method of claim 21.

36. The processor is further configured to transmit the updated latest control algorithm to a plurality of surgical hubs and / or a plurality of surgical devices, the method of claim 21.

37. The method of claim 21, wherein the determined correlation includes a correlation between the first aspect of the latest control algorithm and a negative surgical outcome, and a correlation between the first aspect of the latest control algorithm and a positive surgical outcome.

38. The method of claim 21, wherein the result data includes the first result data and the second result data.

39. The first surgical device and the second surgical device are both a surgical end cutter or a stapling device, and the first operation data and the second operation data are related to one or more of control of an energy source, cutting, stapling, knob orientation, body orientation, body position, anvil jaw force, and reload alignment slot management. The computing system according to claim 1 or the method according to claim 21.

40. A computer program product for causing a computer to execute the method according to claim 21.

41. A non-transitory computer-readable storage medium including computer-readable instructions that, when executed by a computer, cause the computer to execute the method according to claim 21.

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