Autonomous adaptation of surgical device control algorithms

Abstract

A computing system may receive first operational data associated with a first surgical procedure and second operational data associated with a second surgical procedure. The computing system receives first result data associated with a first surgical procedure and second result data associated with a second surgical procedure. The computing system determines that each of a control algorithm of the first surgical device and a control algorithm of the second surgical device is a latest control algorithm associated with the first surgical device type. The computing system generates first aggregated data based on at least the first operational data, the second operational 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

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application is related to the following concurrently filed patent applications, the contents of each of which are incorporated herein by reference:

[0003] U.S. Patent Application Titled “METHOD OF CONTROLLING AUTONOMOUSOPERATIONS IN A SURGICAL SYSTEM,” Attorney Docket No. END9430USNP1.

[0004] U.S. Patent Application Titled “ADAPTED AUTONOMY FUNCTIONS AND SYSTEM INTERCONNECTIONS,” Attorney Docket No. END9430USNP4. Background Art

[0005] Surgical procedures are typically performed in a surgical suite or operating room at a medical facility, such as, for example, a hospital. Various surgical devices and systems are utilized in performing surgical procedures. In the digital and information age, medical systems and facilities are often slow to implement systems or procedures that utilize newer and improved technologies due to patient safety and a general desire to maintain traditional practices. Summary of the invention

[0006] Systems, methods, and tools 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 be capable of receiving first operation data associated with a first surgical procedure and second operation data associated with a second surgical procedure. In some examples, the first operation data is associated with a first aspect of a control algorithm of a first surgical device. In some examples, 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. The processor is configured to be capable of receiving first result data associated with the first surgical procedure and second result data associated with the second surgical procedure. The processor is configured to be capable of determining that each of the control algorithms of the first surgical device and the control algorithms of the second surgical device is a latest control algorithm associated with the first surgical device type. The processor is configured to be capable of generating 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 at least on the first aggregated data, the processor is configured to be capable of determining 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 be capable of generating an updated latest control algorithm.

[0007] A computing system for autonomous surgical device control algorithm adaptation is described. The computing system includes a processor. The processor is configured to receive first operation data associated with a first surgical procedure and second operation data associated with a second surgical procedure. The first operation data is associated with a first aspect of a control algorithm of the first surgical device. The second operation data is associated with a first aspect of a control algorithm of the 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 of the first surgical device and the control algorithm of the second surgical device is a latest control algorithm associated with the first surgical device type. The processor is configured to generate first aggregate data based on at least the first operation data, the second operation data, the first result data, and the second result data. The processor is configured to determine a correlation between the first aspect of the latest control algorithm and the result data based on at least the first aggregate data. The processor is configured to generate an updated latest control algorithm based on the determined correlation. Advantageously, the system according to Embodiment 1 can improve surgical outcomes when a suboptimal control algorithm is used in one or more surgical devices, or when the control algorithm of one or more surgical devices is not suitable for the surgical environment and context (e.g., type of surgery, surgeon preference, operating method, etc.) in which the control algorithm is used.

[0008] As a non-limiting example, the control algorithm may control one or both of the closing force (FTC) and the firing force (FTF). The first / second operating data may include one or more of the following: FTC over time, waiting time before initiating firing, FTF over time, tissue characteristics (e.g., impedance, thickness, stiffness, etc.), tissue gaps (i.e., thickness of tissue), tissue type, tissue condition, number of firings of the instrument, etc. By comparing the first operating data and the second operating data and the first result and the second result, the correlation between the data and the result can be determined, for example, a longer waiting time and / or a higher peak FTF before firing can be associated with a more positive result for a specific tissue type / thickness for a specific surgery. Therefore, the latest control algorithm generated for the update may include a longer waiting time and / or a higher peak FTF associated with a more positive result.

[0009] The operational data may include multiple variables of the manner in which the device is used, e.g., wait time before firing, FTC, FTF, tissue characteristics, etc., which enables a more accurate comparison of the operational data, e.g., a positive result may be associated with a difference between the operational data involving one or more variables.

[0010] By aggregating operational and outcome data from multiple surgical procedures performed in different settings, the system can generate control algorithm updates that improve outcomes in a manner that is not possible with systems that rely on data from a single surgical device or procedure.

[0011] As an example of an improved outcome, the outcome of tissue resection may correspond to the integrity of the seal line, wherein less seal leakage is equivalent to a more positive or improved outcome than more seal leakage. The improved outcome may also correspond to a reduced risk of complications, such as a reduced risk of instrument failure, seal line leakage, staple misfiring, etc.

[0012] A first aspect of the control algorithm for each device may include at least one coefficient, at least one operating parameter, at least one limitation, 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 (e.g., sensed, detected, or recorded force values, time values, voltage / current values, displacement values, etc.) for the control algorithm coefficients, parameters, limitations, or settings allows the system to efficiently and quickly determine candidate updates to the control algorithm in a computationally efficient manner based on correlations between the data and the outcome data. Such direct inputs can be well-involved in the generation of control algorithm updates because statistical connections between these inputs and resulting changes in surgical outcomes can be found in a non-resource-intensive manner.

[0013] Alternatively or in addition to the first aspect of the control algorithm for 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 for each device may include at least one function, subroutine, process and / or algorithm. For example, the first aspect of the control algorithm may include a power algorithm used by one / each surgical device. A "power algorithm" may be a power response of a device (e.g., an electrosurgical or ultrasonic device) to an internal state of one / each device and / or to changes in tissue conditions over time. For example, a computing system may determine a correlation between positive results and a particular power algorithm involving treating small tissue sizes with low power and treating large tissue sizes with high power, and then generate a new control algorithm that includes the same power algorithm or a similar power algorithm (e.g., a power algorithm that shares at least some characteristics / behavior with the power algorithm) for use in other surgical devices to enable these other surgical devices to treat small tissue sizes with low power and treat large tissue sizes with high power.

[0014] Generating an updated state-of-the-art control algorithm may include 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 state-of-the-art control algorithm. Replacing / updating the coefficients / parameters / limits / settings / power algorithm of a control algorithm is a particularly advantageous means of generating a control algorithm update because a new control algorithm can be generated based on an old control algorithm, i.e., using the old algorithm as a "template," rather than having to extensively modify the algorithm or build the algorithm "from scratch."

[0015] The first operation data and the second operation data may be received together with additional operation data associated with a plurality of additional surgical operations, the additional operation data being associated with a first aspect of a control algorithm of a corresponding surgical device, each of the surgical devices being a first surgical device type. The first result data and the second result data may be received together with additional result data associated with the plurality of additional surgical operations. Generating the first aggregated data may also be based at least on the additional operation data and the additional result data. The greater the number of surgical operations and the diversity of surgical operations for which data is provided to the computing system, the less the impact of outliers in the data on the determined correlations, and the higher the degree to which variables that may potentially affect surgical results (e.g., surgeon skill and experience, time of day, location, patient age and health status, etc.) are mitigated and controlled in the analysis. Therefore, it is advantageous to use data for a plurality of additional surgical operations in combination with data from the first surgical operation and the second surgical operation.

[0016] The surgical procedure may be a historical surgical procedure.

[0017] Each surgical device may be mechanically identical, and / or wherein the surgical devices may functionally differ only in their respective control algorithms. Using data from surgical devices that are mechanically identical (e.g., same make, model, and / or brand) and / or whose functional differences are entirely attributable to the specific control algorithms used in each device may be advantageous because doing so allows for significant simplification of data aggregation and analysis (and generation of control algorithm updates). For example, if two instruments are mechanically identical, then the values ​​of various (e.g.) forces / voltages / currents / velocities to be output by the devices may be analyzed without having to control for differences between specific construction differences of one device and another.

[0018] Each surgical procedure may be of the same surgical procedure type. Advantageously, using data from a single general surgical procedure type allows for the generation of more detailed and specific control algorithm updates that go beyond providing general improvements and produce control algorithms that are well suited to their specific surgical tasks.

[0019] Determining a correlation between the first aspect of the most recent control algorithm and the outcome data may include: determining that the first outcome data represents a more positive outcome of the surgical procedure or step thereof as compared to the second outcome data; and determining a difference in the first aspect of the control algorithm used in the first surgical procedure and the second surgical procedure; and correlating the difference in the first aspect of the control algorithm with the more positive outcome. Advantageously, the above-described embodiments allow improvements in surgical outcomes to be attributed to specific device settings or parameters responsible for those outcomes, such that success may be replicated in other instruments by including that setting / parameter in a control algorithm update.

[0020] For example, the outcome of the outcome data may correspond to the integrity of the seal line, wherein less seal leakage is equivalent to a more positive or improved outcome than more seal leakage. For another example, a more positive outcome may be an outcome without surgical complications, while a negative outcome is an outcome with one or more complications (e.g., instrument failure, seal line leakage, staple misfire, etc.). A more positive outcome may also be associated with more positive outcomes than negative outcomes, for example, increasing the ratio of positive outcomes.

[0021] Generating an updated state-of-the-art 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 surgical procedure that produced a positive outcome.

[0022] Generating an updated state-of-the-art control algorithm may include attributing differences in outcome data between at least the first surgical procedure and the second surgical procedure to differences in a first aspect of the control algorithm used in the procedures.

[0023] The computing system may be one of an edge computing system or a cloud computing system.

[0024] The first operational data may be received from the first surgical device, and the second operational data may be received from the second surgical device. Receiving the operational data directly from the device may increase the processing speed of the system as a whole, and reduce the latency of generating (and optionally distributing) control algorithm updates. Furthermore, this may eliminate the need for additional storage devices and / or any (e.g.) compression / truncation / rounding of the operational data, which may otherwise result in a loss of fidelity.

[0025] The first operational data and the second operational data may be received from a surgical hub or two different surgical hubs. Receiving all operational data from the same hub results in a more cohesive system with lower latency and reduced network requirements. Receiving data from two different hubs allows data from geographically different surgical environments to be used in the analysis to draw new conclusions that are not apparent from the data of a single surgical environment alone.

[0026] First outcome data associated with a first surgical procedure may be received from a surgical hub or a surgical visualization device.

[0027] The processor may be further configured to enable publishing of updated state-of-the-art control algorithms for download by surgical devices or surgical hubs via the interface. Making the updated state-of-the-art control algorithm available for download allows it to be deployed on a wide range of devices in potentially geographically diverse surgical environments so that the benefits associated with the improved control algorithm may be more readily obtained in these other environments (which otherwise may have to obtain the new control algorithm via slower alternative means).

[0028] The processor may be further configured to enable sending updated state-of-the-art control algorithms to multiple surgical hubs and / or multiple surgical devices. Sending the updated control algorithm to the hub and / or devices allows it to be deployed on a wide range of devices in potentially geographically diverse surgical environments so that the benefits associated with the improved control algorithm can be more easily obtained in these other environments (which might otherwise have to obtain the new control algorithm via slower alternative means).

[0029] The determined correlations may include a correlation between the first aspect of the most recent control algorithm and a negative surgical outcome, and a correlation between the first aspect of the most recent control algorithm and a positive surgical outcome. For example, the surgical outcome may correspond to the integrity of the seal line, with a seal with less leakage equating to a positive or improved outcome, and a seal with more leakage equating to a negative or reduced outcome (relative to the other). A threshold may be set to classify a positive or negative outcome, which for this example may be a threshold leakage. As another example, a positive outcome may be an outcome with no surgical complications, while a negative outcome may be an outcome with one or more complications (e.g., instrument failure, seal line leakage, staple misfiring, etc.).

[0030] The result data may include first result data and second result data.

[0031] An intelligent surgical device is described. The intelligent surgical device includes a processor. The processor is configured to receive an updated control algorithm associated with the intelligent surgical device from a surgical hub. The processor is configured to determine that the updated control algorithm is more recent than a control algorithm installed on the intelligent 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.

[0032] Replacing the control algorithm with an updated control algorithm may include replacing coefficients associated with the control algorithm with updated coefficients associated with the updated control algorithm.

[0033] A method for autonomous surgical device control algorithm adaptation is described. The method includes receiving first operation data associated with a first surgical procedure and second operation data associated with a second surgical procedure. The first operation data is associated with a first aspect of the control algorithm of the first surgical device. The second operation data is associated with a first aspect of the control algorithm of the second surgical device. The first surgical device and the second surgical device are of a first surgical device type. The method also includes receiving first result data associated with the first surgical procedure and second result data associated with the second surgical procedure. The method also includes determining 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 method also includes generating first aggregated data based at least on the first operation data, the second operation data, the first result data, and the second result data. The method also includes determining, based at least on the first aggregated data, a correlation between the first aspect of the latest control algorithm and the result data. The method also includes generating an updated latest control algorithm based on the determined correlation. Advantageously, the method according to embodiment 21 can improve surgical outcomes in the following situations: a suboptimal control algorithm is used in one or more surgical devices, or the control algorithm of one or more surgical devices is not suitable for the surgical environment and context (e.g., type of surgery, surgeon preference, operating method, etc.) in which the control algorithm is employed.

[0034] As a non-limiting example, the control algorithm may control one or both of the closing force (FTC) and the firing force (FTF). The first / second operating data may include one or more of the following: FTC over time, waiting time before initiating firing, FTF over time, tissue characteristics (e.g., impedance, thickness, stiffness, etc.), tissue gaps (i.e., thickness of tissue), tissue type, tissue condition, number of firings of the instrument, etc. By comparing the first operating data and the second operating data and the first result and the second result, the correlation between the data and the result can be determined, for example, a longer waiting time and / or a higher peak FTF before firing can be associated with a more positive result for a specific tissue type / thickness for a specific surgery. Therefore, the latest control algorithm generated for the update may include a longer waiting time and / or a higher peak FTF associated with a more positive result.

[0035] The operational data may include multiple variables of the manner in which the device is used, e.g., wait time before firing, FTC, FTF, tissue characteristics, etc., which enables a more accurate comparison of the operational data, e.g., a positive result may be associated with a difference between the operational data involving one or more variables.

[0036] By aggregating operational and outcome data from multiple surgical procedures performed in different settings, the approach can generate control algorithm updates that improve outcomes in a manner not possible with approaches that rely on data from a single surgical device or procedure.

[0037] As an example of an improved outcome, the outcome of tissue resection may correspond to the integrity of the seal line, wherein less seal leakage is equivalent to a more positive or improved outcome than more seal leakage. The improved outcome may also correspond to a reduced risk of complications, such as a reduced risk of instrument failure, seal line leakage, staple misfiring, etc.

[0038] A first aspect of the control algorithm for each device may include at least one coefficient, at least one operating parameter, at least one limitation, 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 (e.g., sensed, detected, or recorded force values, time values, voltage / current values, displacement values, etc.) for the control algorithm coefficients, parameters, limitations, or settings allows the system to efficiently and quickly determine candidate updates to the control algorithm in a computationally efficient manner based on correlations between the data and the outcome data. Such direct inputs can be well-involved in the generation of control algorithm updates because statistical connections between these inputs and resulting changes in surgical outcomes can be found in a non-resource-intensive manner.

[0039] Alternatively or in addition to the first aspect of the control algorithm for 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 for each device may include at least one function, subroutine, process and / or algorithm. For example, the first aspect of the control algorithm may include a power algorithm used by one / each surgical device. A "power algorithm" may be a power response of a device (e.g., an electrosurgical or ultrasonic device) to an internal state of one / each device and / or to changes in tissue conditions over time. For example, a computing system may determine a correlation between positive results and a particular power algorithm involving treating small tissue sizes with low power and treating large tissue sizes with high power, and then generate a new control algorithm that includes the same power algorithm or a similar power algorithm (e.g., a power algorithm that shares at least some characteristics / behavior with the power algorithm) for use in other surgical devices to enable these other surgical devices to treat small tissue sizes with low power and treat large tissue sizes with high power.

[0040] Generating an updated state-of-the-art control algorithm may include 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 state-of-the-art control algorithm. Replacing / updating the coefficients / parameters / limits / settings / power algorithm of a control algorithm is a particularly advantageous means of generating a control algorithm update because a new control algorithm can be generated based on an old control algorithm, i.e., using the old algorithm as a "template," rather than having to extensively modify the algorithm or build the algorithm "from scratch."

[0041] The first operation data and the second operation data may be received together with additional operation data associated with a plurality of additional surgical operations, the additional operation data being associated with a first aspect of a control algorithm of a corresponding surgical device, each of the surgical devices being a first surgical device type. The first result data and the second result data may be received together with additional result data associated with the plurality of additional surgical operations. Generating the first aggregated data may also be based at least on the additional operation data and the additional result data. The greater the number of surgical operations and the diversity of surgical operations for which data is provided, the less the impact of outliers in the data on the determined correlations, and the higher the degree to which variables that may potentially affect surgical results (e.g., surgeon skill and experience, time of day, location, patient age and health status, etc.) are mitigated and controlled in the analysis. Therefore, it is advantageous to use data for a plurality of additional surgical operations in combination with data from the first surgical operation and the second surgical operation.

[0042] The surgical procedure may be a historical surgical procedure.

[0043] Each surgical device may be mechanically identical, and / or wherein the surgical devices may functionally differ only in their respective control algorithms. Using data from surgical devices that are mechanically identical (e.g., same make, model, and / or brand) and / or whose functional differences are entirely attributable to the specific control algorithms used in each device may be advantageous because doing so allows for significant simplification of data aggregation and analysis (and generation of control algorithm updates). For example, if two instruments are mechanically identical, then the values ​​of various (e.g.) forces / voltages / currents / velocities to be output by the devices may be analyzed without having to control for differences between specific construction differences of one device and another.

[0044] Each surgical procedure may be of the same surgical procedure type. Advantageously, using data from a single general surgical procedure type allows for the generation of more detailed and specific control algorithm updates that go beyond providing general improvements and produce control algorithms that are well suited to their specific surgical tasks.

[0045] Determining a correlation between the first aspect of the most recent control algorithm and the outcome data may include: determining that the first outcome data is indicative of a more positive outcome of the surgical procedure or step thereof compared to the second outcome data; and determining a difference in the first aspect of the control algorithm used in the first surgical procedure and the second surgical procedure; and correlating the difference in the first aspect of the control algorithm with the more positive outcome. Advantageously, the above-described embodiments allow improvements in surgical outcomes to be attributed to specific device settings or parameters responsible for those outcomes, such that success may be replicated in other instruments by including that setting / parameter in a control algorithm update.

[0046] For example, the outcome of the outcome data may correspond to the integrity of the seal line, wherein less seal leakage is equivalent to a more positive or improved outcome than more seal leakage. For another example, a more positive outcome may be an outcome without surgical complications, while a negative outcome is an outcome with one or more complications (e.g., instrument failure, seal line leakage, staple misfire, etc.). A more positive outcome may also be associated with more positive outcomes than negative outcomes, for example, increasing the ratio of positive outcomes.

[0047] Generating an updated state-of-the-art 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 surgical procedure that produced a positive outcome.

[0048] Generating an updated state-of-the-art control algorithm may include attributing differences in outcome data between at least the first surgical procedure and the second surgical procedure to differences in a first aspect of the control algorithm used in the procedures.

[0049] The computing system may be one of an edge computing system or a cloud computing system.

[0050] The first operational data may be received from the first surgical device, and the second operational data may be received from the second surgical device. Receiving the operational data directly from the device may increase the processing speed of the system as a whole, and reduce the latency of generating (and optionally distributing) control algorithm updates. Furthermore, this may eliminate the need for additional storage devices and / or any (e.g.) compression / truncation / rounding of the operational data, which may otherwise result in a loss of fidelity.

[0051] The first operational data and the second operational data may be received from a surgical hub or two different surgical hubs. Receiving all operational data from the same hub results in higher cohesion, lower latency, and reduced network requirements. Receiving data from two different hubs allows data from geographically different surgical environments to be used in the analysis to draw new conclusions that are not apparent from the data of a single surgical environment alone.

[0052] First outcome data associated with a first surgical procedure may be received from a surgical hub or a surgical visualization device.

[0053] The processor may be further configured to enable publishing of updated state-of-the-art control algorithms for download by surgical devices or surgical hubs via the interface. Making the updated state-of-the-art control algorithm available for download allows it to be deployed on a wide range of devices in potentially geographically diverse surgical environments so that the benefits associated with the improved control algorithm may be more readily obtained in these other environments (which otherwise may have to obtain the new control algorithm via slower alternative means).

[0054] The processor may be further configured to enable sending updated state-of-the-art control algorithms to multiple surgical hubs and / or multiple surgical devices. Sending the updated control algorithm to the hub and / or devices allows it to be deployed on a wide range of devices in potentially geographically diverse surgical environments so that the benefits associated with the improved control algorithm can be more easily obtained in these other environments (which might otherwise have to obtain the new control algorithm via slower alternative means).

[0055] The determined correlations may include a correlation between the first aspect of the most recent control algorithm and a negative surgical outcome, and a correlation between the first aspect of the most recent control algorithm and a positive surgical outcome. For example, the surgical outcome may correspond to the integrity of the seal line, with a seal with less leakage equating to a positive or improved outcome, and a seal with more leakage equating to a negative or reduced outcome (relative to the other). A threshold may be set to classify a positive or negative outcome, which for this example may be a threshold leakage. As another example, a more positive outcome may be an outcome with no surgical complications, while a negative outcome may be an outcome with one or more complications (e.g., instrument failure, seal line leakage, staple misfiring, etc.).

[0056] The result data may include first result data and second result data.

[0057] Both the first surgical device and the second surgical device can be surgical endoscopic cutting and closing devices or stapling devices. The first operating data or the second operating data can be related to one or more of: controlling energy source, cutting, suturing, knob orientation, body orientation, body position, anvil jaw force, and reloading alignment slot management.

[0058] A computer program product is described for causing a computer to perform any of the methods previously described.

[0059] A non-transitory computer-readable storage medium is described, the non-transitory computer-readable storage medium comprising computer-readable instructions which, when executed by a computer, cause the computer to perform any of the previously described methods. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 is a block diagram of a computer-implemented surgical system.

[0061] Figure 2 An exemplary surgical system is shown in a surgical operating room.

[0062] Figure 3 Exemplary surgical hubs that pair with various systems are illustrated.

[0063] Figure 4 A surgical data network having a set of communicating surgical hubs configured to be able to connect to a set of sensing systems, an environmental sensing system, a set of devices, etc. is illustrated.

[0064] Figure 5 A logic diagram of a control system for a surgical instrument is illustrated.

[0065] Figure 6 An exemplary surgical system is shown that includes a handle having a controller and a motor, an adapter releasably coupled to the handle, and a loading unit releasably coupled to the adapter.

[0066] Figure 7 An exemplary situational awareness surgical system is shown.

[0067] Figure 8 An exemplary process for autonomous updating of a surgical device control algorithm is shown.

[0068] Fig. 9 A block diagram of a computer-implemented adaptive surgical system is shown.

[0069] Fig.10 A diagram of an illustrative analysis system for updating a surgical instrument control program is shown.

[0070] Fig.11 An exemplary process for autonomous updating of a surgical device control algorithm is shown.

[0071] Fig.12 An exemplary process for autonomous updating of a surgical device control algorithm is shown.

[0072] Fig.13 is a flow chart of an exemplary process for autonomous updating of a surgical device control algorithm. DETAILED DESCRIPTION

[0073] Figure 120000. An exemplary surgical system such as surgical system 20000 may include one or more surgical systems (e.g., surgical subsystems) 20002, 20003, and 20004. For example, surgical system 20002 may include a computer-implemented interactive 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, such as Figure 2 The cloud computing system 20008 may 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-machine interface system 20012, and the like. The human-machine interface system is also referred to herein as a human-machine 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 for measuring one or more environmental attributes, such as Figure 2 As further described. The robotic system 20013 may include a plurality of devices for performing a surgical procedure, such as Figure 2 Further described.

[0074] The surgical system 20002 can communicate with a remote server 20009, which can be part of a cloud computing system 20008. In one example, the surgical system 20002 can communicate with the remote server 20009 via a cable / FIOS networking node of an Internet service provider. In one example, the patient sensing system can communicate directly with the remote server 20009. The surgical system 20002 and / or components therein can communicate with the remote server 20009 via a cellular transmit / receive point (TRP) or base station using one or more of the following cellular protocols: GSM / GPRS / EDGE (2G), UMTS / HSPA (3G), Long Term Evolution (LTE) or 4G, Advanced LTE (LTE-A), New Radio (NR), or 5G.

[0075] The surgical hub 20006 can cooperate and interact with one of the multiple devices that display images from the laparoscope and information from one or more other smart devices and one or more sensing systems 20011. The surgical hub 20006 can interact with one or more sensing systems 20011, one or more smart devices, and multiple displays. The surgical hub 20006 can be configured to collect measurement data from one or more sensing systems 20011 and send notification or control messages to the one or more sensing systems 20011. The surgical hub 20006 can send information including notification information to the human-machine interface system 20012 and / or receive information including notification information from the human-machine interface system. The human-machine interface system 20012 may include one or more human-machine interface devices (HIDs). The surgical hub 20006 can send and / or receive notification information or control information to convert into audio, display and / or control information to various devices communicating with the surgical hub.

[0076] For example, 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, such as Figure 1 The one or more sensing systems 20001 can measure data related to various biomarkers. The one or more sensing systems 20001 can use one or more sensors such as light sensors (e.g., photodiodes, photoresistors), mechanical sensors (e.g., motion sensors), acoustic sensors, electrical sensors, electrochemical sensors, pyroelectric sensors, infrared sensors, etc. to measure biomarkers. The one or more sensors can use one or more of the following sensing technologies to measure biomarkers as described herein: photoplethysmography, electrocardiography, electroencephalography, colorimetry, impedance tracing, potentiometric measurement, amperometric measurement, etc.

[0077] Biomarkers measured by the one or more sensing systems 20001 may include, but are not limited to, sleep, core body temperature, maximum oxygen uptake, physical activity, alcohol consumption, respiratory rate, oxygen saturation, blood pressure, blood glucose, heart rate variability, blood pH, hydration status, heart rate, skin conductance, peripheral temperature, tissue perfusion pressure, coughing and sneezing, gastrointestinal motility, gastrointestinal imaging, respiratory bacteria, edema, psychiatric factors, sweat, circulating tumor cells, autonomic nervous tone, circadian rhythm and / or menstrual cycle.

[0078] Biomarkers may be related to physiological systems, which may include, but are not limited to, behavior and psychology, cardiovascular system, renal system, skin system, nervous system, gastrointestinal system, respiratory system, endocrine system, immune system, tumor, musculoskeletal system and / or reproductive system. Information from biomarkers can be determined and / or used by, for example, a computer-implemented patient and surgical system 20000. Information from biomarkers can be determined and / or used by a computer-implemented patient and surgical system 20000, for example, to improve the system and / or improve patient outcomes. One or more sensing systems 20001, biomarkers 20005 and physiological systems are described in more detail in U.S. application 17 / 156,287 (Agent File No. END9290USNP1), filed on January 22, 2021, entitled "METHODOF ADJUSTING A SURGICAL PARAMETER BASED ON BIOMARKER MEASUREMENTS", the disclosure of which is incorporated herein by reference in its entirety.

[0079] Figure 2 An example of a surgical system 20002 in a surgical operating room is shown. Figure 2 As shown, a patient undergoes surgery by one or more healthcare professionals (HCPs). The HCP is monitored by one or more HCP sensing systems 20020 worn by the HCP. The HCP and the environment around the HCP may also be monitored by one or more environmental sensing systems, which may include, for example, a set of cameras 20021, a set of microphones 20022, and other sensors that may be deployed in an operating room. The HCP sensing system 20020 and the environmental sensing system may communicate with a surgical hub 20006, which in turn may communicate with one or more cloud servers 20009 of a cloud computing system 20008, such as Figure 1 The environmental sensing system may be used to measure one or more environmental attributes, such as the position of the HCP in the operating room, HCP movement, ambient noise in the operating room, temperature / humidity in the operating room, etc.

[0080] like Figure 2As shown, the main display 20023 and one or more audio output devices (e.g., speakers 20019) are positioned in the sterile area to be visible to the operator at the operating table 20024. In addition, the visualization / notification tower 20026 is positioned outside the sterile area. The visualization / notification tower 20026 may include a first non-sterile human-machine interaction device (HID) 20027 and a second non-sterile HID 20029 that are separated from each other. The HID can be a display or a display with a touch screen that allows people to directly interface with the HID. The human-machine interface system guided by the surgical hub 20006 can be configured to be able to coordinate the information flow of operators inside and outside the sterile area using HIDs 20027, 20029, and 20023. In one example, the surgical hub 20006 can cause the HID (e.g., the main HID 20023) to display notifications and / or information about patients and / or surgical procedures. In one example, the surgical hub 20006 can prompt for input and / or receive input from personnel in the sterile or non-sterile field. In one example, the surgical hub 20006 can 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 real-time feed of the surgical site on the main HID 20023. For example, the snapshot on the non-sterile display 20027 or 20029 can allow a non-sterile operator to perform diagnostic steps related to the surgical procedure.

[0081] In one aspect, the surgical hub 20006 can be configured to be able to route diagnostic input or feedback entered by a non-sterile operator at the visualization tower 20026 to the primary display 20023 within the sterile field, where the sterile operator at the operating table can view the diagnostic input or feedback. In one example, the input can be a modified form of a snapshot displayed on the non-sterile display 20027 or 20029, which can be routed to the primary display 20023 via the surgical hub 20006.

[0082] refer to Figure 2, surgical instrument 20031 is used in a surgical procedure as part of surgical system 20002. Hub 20006 can be configured to coordinate the flow of information to the display of surgical instrument 20031. For example, described in U.S. Patent Application Publication No. US2019-0200844A1 (U.S. Patent Application No. 16 / 209,385), filed on December 4, 2018, entitled "METHOD OF HUB COMMUNICATION, PROCESSING, STORAGE AND DISPLAY", the disclosure of which is incorporated herein by reference in its entirety. Diagnostic input or feedback entered by a non-sterile operator at visualization tower 20026 can be routed by hub 20006 to a surgical instrument display within the sterile field, where the operator of surgical instrument 20031 can view the diagnostic input or feedback. For example, exemplary surgical instruments suitable for use with surgical system 20002 are described under the heading “Surgical Instrument Hardware” in U.S. patent application publication No. US 2019-0200844A1 (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 incorporated herein by reference in its entirety.

[0083] Figure 2 An example of a surgical system 20002 for performing a surgical procedure on a patient lying flat on an operating table 20024 in a surgical operating room 20035 is shown. A robotic system 20034 may be used in a surgical procedure as part of the surgical system 20002. The robotic system 20034 may include a surgeon's console 20036, a patient side cart 20032 (surgical robot), and a surgical robotic hub 20033. The patient side cart 20032 may manipulate at least one removably coupled surgical tool 20037 through a minimally invasive incision in the patient's body while the surgeon observes the surgical site through the surgeon's console 20036. Images of the surgical site may be obtained through a medical imaging device 20030, which may be manipulated by the patient side cart 20032 to orient the imaging device 20030. The robotic hub 20033 may be used to process images of the surgical site for subsequent display to the surgeon through the surgeon's console 20036.

[0084] Other types of robotic systems may be readily adapted for use 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 No. US2019-0201137A1, filed on December 4, 2018, entitled “METHOD OF ROBOTIC HUB COMMUNICATION, DETECTION, AND CONTROL” (U.S. Patent Application No. 16 / 209,407), the disclosure of which is incorporated herein by reference in its entirety.

[0085] Various examples of cloud-based analytics performed by cloud computing system 20008 and suitable for use with the present disclosure are described in U.S. patent application publication No. US2019-0206569 A1 (U.S. patent application No. 16 / 209,403), entitled “METHOD OF CLOUD BASED DATA ANALYTICS FOR USE WITH THE HUB,” filed on December 4, 2018, the disclosure of which is incorporated herein by reference in its entirety.

[0086] 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.

[0087] 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 multiple portions of the surgical field. The one or more image sensors may receive light reflected or refracted from the surgical field, including light reflected or refracted from tissue and / or surgical instruments.

[0088] One or more illumination sources may be configured to radiate electromagnetic energy in the visible spectrum as well as in the invisible spectrum. The visible spectrum (sometimes referred to as the optical spectrum or the luminescent spectrum) is that 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. A typical human eye will respond to wavelengths in the range of about 380 nm to about 750 nm in air.

[0089] The invisible spectrum (e.g., non-luminescent spectrum) is the portion of the electromagnetic spectrum below and above the visible spectrum (i.e., wavelengths below about 380 nm and above about 750 nm). The invisible spectrum is not detectable by the human eye. Wavelengths greater than about 750 nm are longer than the red visible spectrum, and they become invisible infrared (IR), microwaves, and radio electromagnetic radiation. Wavelengths less than about 380 nm are shorter than the violet spectrum, and they become invisible ultraviolet, x-ray, and gamma-ray electromagnetic radiation.

[0090] In various aspects, the imaging device 20030 is configured for use in minimally invasive surgery. Examples of imaging devices suitable for use in the present disclosure include, but are not limited to, arthroscopes, angioscopes, bronchoscopes, choledochoscopes, colonoscopes, cytoscopes, duodenoscopes, enteroscopes, esophago-duodenoscopes (gastroscopes), endoscopes, laryngoscopes, nasopharyngeal-renal endoscopes, sigmoidoscopes, thoracoscopes, and ureteroscopes.

[0091] The imaging device can use multispectral monitoring to distinguish the topography and the underlying structure. A multispectral image is an image that captures image data within a specific wavelength range across the electromagnetic spectrum. The wavelengths can be separated by filters or by using instruments that are sensitive to specific wavelengths, including light from frequencies outside the visible light range, such as IR and ultraviolet. Spectral imaging allows the extraction of additional information that the human eye fails to capture with its red, green, and blue receptors. The use of multispectral imaging is described in more detail under the title "Advanced Imaging Acquisition Module" in U.S. Patent Application Publication No. US2019-0200844 A1 (U.S. Patent Application No. 16 / 209,385) filed on December 4, 2018, entitled "METHOD OF HUB COMMUNICATION, PROCESSING, STORAGE AND DISPLAY", the disclosure of which is incorporated herein by reference in its entirety. After completing the surgical task to perform one or more of the previously described tests on the treated tissue, multispectral monitoring can be a useful tool for relocating the surgical site. It is self-evident that during any surgery, the operating room and surgical equipment need to be strictly sterilized. The strict sanitation and sterilization conditions required in the "surgical room" (i.e., operating room or treatment room) require the highest possible sterility of all medical devices and equipment. Part of this sterilization process is the need to sterilize any material that contacts the patient or penetrates the sterile field, including the imaging device 20030 and its attachments and components. It should be understood that the sterile field can be considered as a designated area that is considered to be free of microorganisms, such as in a tray or in a sterile towel, or the sterile field can be considered as the area around the patient prepared for surgery. The sterile field can include properly dressed scrubbed team members, as well as all equipment and fixtures in the area.

[0092] Figure 1 The wearable sensing system 20011 shown may include one or more sensing systems, such as Figure 2HCP sensing system 20020 shown. HCP sensing system 20020 may include a sensing system for monitoring and detecting a set of physical states and / or a set of physiological states of a healthcare provider (HCP). The HCP may typically be a surgeon or one or more healthcare providers assisting the surgeon or other healthcare providers. In one example, sensing system 20020 may measure a set of biomarkers to monitor the heart rate of the HCP. In one example, a sensing system 20020 (e.g., a watch or wristband) worn on the surgeon's wrist may use an accelerometer to detect hand motion and / or shaking and determine the amplitude and frequency of the tremor. Sensing system 20020 may send measurement data associated with the set of biomarkers and data associated with the surgeon's physical state to surgical hub 20006 for further processing. One or more environmental sensing devices may send environmental information to surgical hub 20006. For example, an 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 thermometers for measuring temperature and hygrometers for measuring humidity of the environment in the operating room. A surgical hub 20006, either alone or in communication with a cloud computing system, may use surgeon biomarker measurement data and / or environmental sensing information to modify the average latency of a control algorithm or a robotic interface of a handheld instrument, for example, to minimize tremor. In one example, the HCP sensing system 20020 may measure one or more surgeon biomarkers associated with the HCP and send measurement data associated with the surgeon biomarkers to the surgical hub 20006. The HCP sensing system 20020 may communicate with the surgical hub 20006 using one or more of the following RF protocols: Bluetooth, Bluetooth Low Energy (BLE), Bluetooth Smart, Zigbee, Z-wave, IPv6 Low Power Wireless Personal Area Network (6LoWPAN), Wi-Fi. Surgeon biomarkers may include one or more of the following: stress, heart rate, etc. Environmental measurements from the operating room may include ambient noise levels associated with surgeons or patients, surgeon and / or personnel movement, surgeon and / or personnel attention levels, etc.

[0093] The surgical hub 20006 can use the surgeon biomarker measurement data associated with the HCP to adaptively control one or more surgical instruments 20031. For example, the surgical hub 20006 can 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 can send the control program based on situational awareness and / or context about the importance or criticality of the task. When control is needed, the control program can instruct the instrument to change operation to provide more control.

[0094] Figure 3 An exemplary surgical system 20002 with a surgical hub 20006 is shown. The surgical hub 20006 can be paired with a wearable sensing system 20011, an environmental sensing system 20015, a human-machine interface system 20012, a robotic system 20013, and a smart device 20014 via modular controls. 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 Figure 3 As shown, the hub 20006 also includes a smoke evacuation module 20054 and / or a suction / irrigation module 20055. Various modules and systems can be connected to the modular controls directly via a router or via a communication module 20056. The operating room devices can be coupled to cloud computing resources and data storage devices via the modular controls. The human-machine interface system 20012 may include a display subsystem and a notification subsystem.

[0095] The modular control may be coupled to a non-contact sensor module. The non-contact sensor module may use an ultrasonic, laser-type and / or similar non-contact measuring device to measure the size of the operating room and generate a mapping of the surgical room. Other distance sensors may be used to determine the boundaries of the operating room. The ultrasonic-based non-contact sensor module may scan the operating room by emitting a burst of ultrasound and receiving an echo when it bounces back from the walls of the operating room, as described in the title "Surgical Hub Spatial Awareness Within an Operating Room" in U.S. Provisional Patent Application Serial No. 62 / 611,341, entitled "INTERACTIVE SURGICAL PLATFORM" filed on December 28, 2017, which is incorporated herein by reference in its entirety. The sensor module may be configured to be able to determine the size of the operating room and adjust the Bluetooth pairing distance limit. The laser-based non-contact sensor module may scan the operating room by emitting a laser pulse, receiving a laser pulse that bounces back from the walls of the operating room, and comparing the phase of the emitted pulse with the received pulse to determine the size of the operating room and adjust the Bluetooth pairing distance limit.

[0096] During surgery, the application of energy to tissue for sealing and / or cutting is often associated with smoke evacuation, suction of excess fluid, and / or flushing of tissue. Fluid lines, power lines, and / or data lines from different sources are often tangled during surgery. Solving this problem during surgery can waste valuable time. Disconnecting a line may require disconnecting the line from its corresponding module, which may require resetting the module. The hub modular housing 20060 provides a unified environment for managing power lines, data lines, and fluid lines, which reduces the frequency of tangling between such lines. Various aspects of the present disclosure provide a surgical hub 20006 for use in surgery involving application of energy to tissue at a surgical site. The surgical hub 20006 includes a hub housing 20060 and a combined generator module slidably accommodated in a docking base of the hub housing 20060. The docking base 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 seated in a single unit. In one aspect, the combined generator module also includes a smoke exhaust component for connecting the combined generator module to at least one energy delivery cable of a surgical instrument, at least one smoke exhaust component configured to be able to exhaust smoke, fluid and / or particles generated by applying therapeutic energy to tissue, and a fluid line extending from a remote surgical site to the smoke exhaust component. In one aspect, the fluid line can be a first fluid line, and the second fluid line can extend from the remote surgical site to the suction and flushing module 20055 slidably accommodated in the hub housing 20060. In one aspect, the hub housing 20060 may include a fluid interface. Certain surgical procedures may require more than one energy type to be applied to the tissue. One energy type may be more conducive to cutting tissue, while another different energy type may be more conducive to sealing tissue. For example, a bipolar generator can be used to seal tissue, while an ultrasonic generator can be used to cut sealed tissue. Aspects of the present disclosure provide a solution in which a hub modular housing 20060 is configured to accommodate different generators and facilitate interactive communication between them. One of the advantages of the hub modular housing 20060 is that it enables rapid removal and / or replacement of various modules. Aspects of the present disclosure provide a modular surgical housing for use in surgical procedures involving application of energy to tissue. The modular surgical housing includes a first energy generator module, the first energy generator module is configured to generate a first energy for application to tissue, and a first docking base, the first docking base includes a first docking port, the first docking port includes a first data and power contact, wherein the first energy generator module is slidably movable into electrical engagement with the power and data contacts, and wherein the first energy generator module is slidably movable out of electrical engagement with the first power and data contacts.Further describing the above, the modular surgical shell also includes a second energy generator module, the second energy generator module is configured to generate a second energy different from the first energy for application to tissue, and a second docking base, the second docking base includes a second docking port, the second docking port includes second data and power contacts, wherein the second energy generator module is slidably movable into electrical engagement with the power and data contacts, and wherein the second energy generator is slidably movable out of electrical contact with the second power and data contacts. In addition, the modular surgical shell also includes a communication bus between the first docking port and the second docking port, which is configured to facilitate communication between the first energy generator module and the second energy generator module. Reference. Figure 3 , aspects of the present disclosure are presented as a hub modular housing 20060 that allows modular integration of a generator module 20050, a smoke evacuation module 20054, and a suction / irrigation module 20055. The hub modular housing 20060 also facilitates interactive communication between the modules 20059, 20054, 20055. The generator module 20050 may have integrated monopolar components, bipolar components, and ultrasonic components supported in a single housing unit that is slidably inserted into the hub modular housing 20060. The generator module 20050 may be configured to be connectable 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 through the hub modular housing 20060. The hub modular housing 20060 can be configured to facilitate the insertion of multiple generators and interactive communication between the generators docked into the hub modular housing 20060 so that the generators will function as a single generator.

[0097] Figure 4 A surgical data network having a set of communication hubs according to at least one aspect of the present disclosure is shown, the communication hubs being configured to connect a set of sensing systems, environmental sensing systems, and a set of other modular devices located in one or more operating rooms, patient recovery rooms, or rooms in the medical facility specifically equipped for surgical operations to a cloud.

[0098] like Figure 4As shown, the surgical hub system 20060 may include a modular communication hub 20065 that is configured to connect modular devices located in a medical facility to a cloud-based system (e.g., a cloud computing system 20064, which may include a remote server 20067 coupled to a remote storage device 20068). The modular communication hub 20065 and the devices may be connected in a room in the medical facility that is specifically equipped for surgical operations. In one aspect, the modular communication hub 20065 may include a network hub 20061 and / or a network switch 20062 that communicates with a network router 20066. The modular communication hub 20065 may be coupled to a local computer system 20063 to provide local computer processing and data manipulation.

[0099] The computer system 20063 may include a processor and a network interface 20100. The processor may be coupled to a communication module, a storage device, a memory, a non-volatile memory, and an input / output (I / O) interface via a system bus. The system bus may be any of several types of bus structures including a memory bus or a memory controller, a peripheral bus or an external bus, and / or a local bus using any of a variety of available bus architectures, including but not limited to a 9-bit bus, an Industry Standard Architecture (ISA), a Micro Charmel Architecture (MSA), an Extended ISA (EISA), Intelligent Drive Electronics (IDE), a VESA Local Bus (VLB), a Peripheral Component Interconnect (PCI), a USB, an Advanced Graphics Port (AGP), a Personal Computer Memory Card International Association bus (PCMCIA), a Small Computer System Interface (SCSI), or any other peripheral bus.

[0100] The controller may be any single-core or multi-core processor, such as those offered by Texas Instruments under the trade name ARM Cortex. In one aspect, the processor may be a commercially available LM4F230H5QR ARM Cortex-M4F processor core, such as Texas Instruments, which includes 256KB of single-cycle flash or other non-volatile memory (up to 40MHz) on-chip memory, a pre-fetch buffer for improved execution above 40MHz, 32KB of single-cycle sequential random access memory (SRAM), a 32KB MCU loaded with Internal read-only memory (ROM) for software, 2KB electrically erasable programmable read-only memory (EEPROM), and / or one or more pulse width modulation (PWM) modules, one or more quadrature encoder input (QEI) simulations, one or more 12-bit analog-to-digital converters (ADCs) with 12 analog input channels, details of which can be found in the product data sheet.

[0101] In one example, the processor may include a safety controller including two families based on controllers such as TMS570 and RM4x, also known by Texas Instruments under the trade name Hercules ARMCortex R4. The safety controller may be configured to be specifically designed for IEC 61508 and ISO 26262 safety critical applications, etc., to provide advanced integrated safety features while delivering scalable execution, connectivity and memory options.

[0102] It should be understood that the computer system 20063 may include software that acts as an intermediary between a user and the basic computer resources described in a suitable operating environment. Such software may include an operating system. An operating system that may be stored on a disk storage device may be used to control and allocate the resources of a computer system. System applications may utilize the operating system to manage resources through program modules and program data stored in system memory or disk storage devices. It should be understood that the various components described herein may be implemented using various operating systems or combinations of operating systems.

[0103] The user can input commands or information into the computer system 20063 through an input device coupled to the I / O interface. The input device may include, but is not limited to, a pointing device, such as a mouse, a touch ball, a stylus, a touch pad, a keyboard, a microphone, a joystick, a game pad, a satellite dish, a scanner, a TV tuner card, a digital camera, a digital video camera, a web camera, etc. These and other input devices are connected to the processor 20102 through the system bus via an interface port. The interface port includes, for example, a serial port, a parallel port, a game port, and a USB. The output device uses the same type of port as the input device. Therefore, for example, a USB port can 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 is provided to illustrate that some output devices, such as monitors, displays, speakers, and printers, may need special adapters in other output devices. The output adapter may include, by way of example, but is not limited to, a video and sound card that provides a connection device between the output device and the system bus. It should be noted that other devices or systems such as remote computers can provide both input capabilities and output capabilities.

[0104] Computer system 20063 can be operated in a networked environment using a logical connection with one or more remote computers (such as cloud computers) or local computers. A remote cloud computer can be a personal computer, a server, a router, a network PC, a workstation, a microprocessor-based appliance, a peer device or other public network node, etc., and generally includes many or all of the elements described relative to the computer system. For simplicity, only a memory storage device with a remote computer is shown. A remote computer can be logically connected to a computer system via a network interface, and then physically connected via a communication connection. A network interface can cover communication networks such as a local area network (LAN) and a wide area network (WAN). LAN technology can include a fiber distributed data interface (FDDI), a copper distributed data interface (CDDI), Ethernet / IEEE 802.3, a token ring / IEEE 802.5, etc. WAN technology can include, but is not limited to, point-to-point links, circuit switching networks such as integrated services digital networks (ISDN) and variants thereof, packet switching networks, and digital subscriber lines (DSL).

[0105] In various examples, computer system 20063 may include an image processor, an image processing engine, a media processor, or any dedicated digital signal processor (DSP) for processing digital images. The image processor may employ parallel computing with single instruction, multiple data (SIMD) or multiple instructions, multiple data (MIMD) techniques to improve speed and efficiency. The digital image processing engine may perform a range of tasks. The image processor may be a system on a chip with a multi-core processor architecture.

[0106] The communication connection may refer to the hardware / software used to connect the network interface to the bus. Although the communication connection is shown for exemplary clarification within the computer system 20063, it may also be located outside the computer system 20063. The hardware / software necessary to connect to the network interface may include internal and external technologies for exemplary purposes only, such as modems, including conventional telephone-grade modems, cable modems, fiber optic modems and DSL modems, ISDN adapters, and Ethernet cards. In some examples, an RF interface may also be used to provide a network interface.

[0107] The surgical data network associated with the surgical hub system 20060 can be configured as passive, intelligent, or switched. A passive surgical data network acts as a conduit for data, enabling it to be transferred from one device (or segment) to another device (or segment) and to cloud computing resources. An intelligent surgical data network includes additional features to enable monitoring of traffic through the surgical data network and configuration of each port in the network hub 20061 or 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.

[0108] Modular devices 1a-1n located in an operating room may be coupled to a modular communication hub 20065. A network hub 20061 and / or a network switch 20062 may be coupled to a network router 20066 to connect devices 1a-1n to a cloud computing system 20064 or a local computer system 20063. Data associated with devices 1a-1n may be transmitted to a cloud-based computer via a router for remote data processing and manipulation. Data associated with devices 1a-1n may also be transmitted to a local computer system 20063 for local data processing and manipulation. Modular devices 2a-2m located in the same operating room may also be coupled to a network switch 20062. A network switch 20062 may be coupled to a network hub 20061 and / or a network router 20066 to connect devices 2a-2m to a cloud 20064. Data associated with devices 2a-2m may be transmitted to a cloud computing system 20064 via a network router 20066 for data processing and manipulation. Data associated with the devices 2a-2m may also be transmitted to the local computer system 20063 for local data processing and manipulation.

[0109] 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. The one or more sensing systems 20069 may communicate with the computer system 20063 or cloud server 20067 of the surgical hub system 20060 directly via one of the network routers 20066 or via a network hub 20061 or a network switch 20062 that communicates with the network router 20066.

[0110] The sensing system 20069 may be coupled to a network router 20066 to connect the sensing system 20069 to a local computer system 20063 and / or a cloud computing system 20064. Data associated with the sensing system 20069 may be transmitted to the cloud computing system 20064 for data processing and manipulation via the network router 20066. Data associated with the sensing system 20069 may also be transmitted to the local computer system 20063 for local data processing and manipulation.

[0111] like Figure 4 As shown, the surgical hub system 20060 can be expanded by interconnecting multiple network hubs 20061 and / or multiple network switches 20062 with multiple network routers 20066. A modular communication hub 20065 can be included in a modular control tower that is configured to accommodate multiple devices 1a-1n / 2a-2m. A local computer system 20063 can also be included in 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 during a surgical procedure, for example. In various aspects, the devices 1a-1n / 2a-2m can include, for example, 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 connected to a display, and / or a non-contact sensor module in other modular devices of the modular communication hub 20065 that can be connected to a surgical data network.

[0112] In one aspect, Figure 4The illustrated surgical hub system 20060 may include a combination of a network hub, a network switch, and a network router that connects the device 1a-1n / 2a-2m or the sensing system 20069 to the cloud base system 20064. One or more of the devices 1a-1n / 2a-2m or the sensing system 20069 coupled to the network hub 20061 or the network switch 20062 can collect data in real time and transmit the data to a cloud computer for data processing and operation. It should be understood that cloud computing relies on shared computing resources rather than using local servers or personal devices to process software applications. The term "cloud" can be used as a metaphor for the "Internet", although the term is not so limited. Thus, the term "cloud computing" may be used herein to refer to a "type of Internet-based computing" in which different services (such as servers, storage devices, and applications) are delivered to a 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 devices connected to the modular communication hub 20065 and / or computer system 20063 via the Internet. The cloud infrastructure may be maintained by a cloud service provider. In this case, the 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 can perform a large 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 be connected to a computer that communicates with cloud computing resources and storage devices.

[0113] By applying cloud computer data processing technology to the data collected by the devices 1a-1n / 2a-2m, the surgical data network can provide improved surgical results, reduced costs and improved patient satisfaction. At least some of the devices 1a-1n / 2a-2m can be used to observe tissue status to evaluate the leakage or perfusion of the sealed tissue after tissue sealing and cutting surgery. At least some of the devices 1a-1n / 2a-2m can be used to identify pathology, such as the effects of disease, using cloud-based computing to examine data including images of body tissue samples for diagnostic purposes. This may include positioning and edge confirmation of tissue and phenotype. At least some of the devices 1a-1n / 2a-2m can be used to identify the anatomical structure of the body using various sensors integrated with imaging devices and technologies (such as overlapping images captured by multiple imaging devices). The data (including image data) collected by the devices 1a-1n / 2a-2m can be transmitted to the cloud computing system 20064 or the local computer system 20063 or both for data processing and manipulation, including image processing and manipulation. The data can be analyzed to improve surgical outcomes by determining whether further treatment can be pursued, such as endoscopic interventions, emerging technologies, targeted radiation, targeted interventions, and the application of precision robotics to tissue-specific sites and conditions. Such data analysis can be further processed using outcome analysis, and using standardized methods can provide helpful feedback to confirm surgical treatments and surgeon behavior, or suggest modifications to surgical treatments and surgeon behavior.

[0114] Applying cloud computer data processing techniques to the measurement data collected by the sensing systems 20069, the surgical data network can provide improved surgical results, improved recovery results, reduced costs, and improved patient satisfaction. At least some of the sensing systems 20069 can be used to assess the physiological condition of a surgeon operating on a patient, or a patient preparing for surgery, or a patient recovering after surgery. The cloud-based computing system 20064 can be used to monitor biomarkers associated with the surgeon or patient in real time, and can be used to generate a surgical plan based at least on the measurement data collected prior to surgery, provide control signals to surgical instruments during surgery, and notify patients of complications during the post-operative period.

[0115] The operating room devices 1a-1n can be connected to the modular communication hub 20065 through a wired channel or a wireless channel, depending on the configuration of the devices 1a-1n to the network hub 20061. In one aspect, the network hub 20061 can be implemented as a local network broadcast device that works on the physical layer of the open system interconnection (OSI) model. The network hub can provide connections with devices 1a-1n located in the same operating room network. The network hub 20061 can collect data in the form of packets and send it to the router in half-duplex mode. The network hub 20061 may not store any media access control / Internet protocol (MAC / IP) for transmitting device data. Only one device in the device 1a-1n can send data through the network hub 20061 at a time. The network hub 20061 may not have a routing table or intelligence about where to send information and broadcast all network data on each connection and broadcast all network data to the remote server 20067 of the cloud computing system 20064. 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.

[0116] The operating room devices 2a-2m may be connected to the network switch 20062 via a wired channel or a wireless channel. The network switch 20062 operates in the data link layer of the OSI model. The network switch 20062 may be a multicast device for connecting the devices 2a-2m located in the same operating room to the network. The network switch 20062 may send data to the network router 20066 in the form of a frame and may operate in full-duplex mode. Multiple devices 2a-2m may simultaneously send data via the network switch 20062. The network switch 20062 stores and uses the MAC addresses of the devices 2a-2m to transmit data.

[0117] The network hub 20061 and / or the network switch 20062 may be coupled to a network router 20066 to connect to the cloud computing system 20064. The network router 20066 operates in the network layer of the OSI model. The network router 20066 generates routes for transmitting data packets received from the network hub 20061 and / or the network switch 20062 to cloud-based computer resources to further process and manipulate data collected by any or all of the devices 1a-1n / 2a-2m and the wearable sensing system 20011. The network router 20066 may be employed to connect two or more different networks located in different locations, such as, for example, different operating rooms in the same medical facility or different networks located in different operating rooms in different medical facilities. The network router 20066 may send data to the cloud computing system 20064 in the form of packets and operate in full-duplex mode. Multiple devices may send data simultaneously. The network router 20066 may use IP addresses to transmit data.

[0118] In one example, the network hub 20061 may be implemented as a USB hub that allows multiple USB devices to be connected to a host computer. A USB hub may expand a single USB port to multiple levels so that more ports are available to connect devices to a host system computer. The network hub 20061 may include wired or wireless capabilities for receiving information through a wired channel or a wireless channel. In one aspect, a wireless USB short-range, high-bandwidth wireless radio communication protocol may be used for communication between devices 1a-1n and devices 2a-2m located in an operating room.

[0119] In an example, the operating room devices 1a-1n / 2a-2m and / or the sensing system 20069 can communicate with the modular communication hub 20065 via the Bluetooth wireless technology standard for exchanging data from fixed devices and mobile devices over short distances (using short wavelength UHF radio waves from 2.4GHz to 2.485GHz in the ISM band) and building a personal area network (PAN). The operating room devices 1a-1n / 2a-2m and / or the sensing system 20069 can communicate with the modular communication hub 20065 via a variety of wireless or wired communication standards or protocols, including but not limited to Bluetooth, low-power Bluetooth, near field communication (NFC), Wi-Fi (IEEE802.11 series), WiMAX (IEEE 802.16 series), IEEE 802.20, New Radio (NR), Long Term Evolution (LTE) and Ev-DO, HSPA+, HSDPA+, HSUPA+, EDGE, GSM, GPRS, CDMA, TDMA, DECT and its Ethernet derivatives, and any other wireless and wired protocols designated as 3G, 4G, 5G and above. The computing module may include multiple communication modules. For example, the first communication module can be dedicated to shorter-range wireless communications, such as Wi-Fi and Bluetooth Low Energy, Bluetooth Smart, while the second communication module can be dedicated to longer-range wireless communications, such as GPS, EDGE, GPRS, CDMA, WiMAX, LTE, Ev-DO, HSPA+, HSDPA+, HSUPA+, EDGE, GSM, GPRS, CDMA, TDMA, etc.

[0120] Modular communication hub 20065 may serve as a central connection for one or more of operating room devices 1a-1n / 2a-2m and / or sensing systems 20069 and may process data types referred to as frames. Frames may carry data generated by devices 1a-1n / 2a-2m and / or sensing systems 20069. When modular communication hub 20065 receives a frame, the frame may be amplified and / or sent to a network router 20066, which may transmit the data to a cloud computing system 20064 or a local computer system 20063 using a plurality of wireless or wired communication standards or protocols, as described herein.

[0121] Modular communication hub 20065 can be used as a standalone device or connected to a compatible network hub 20061 and network switch 20062 to form a larger network. Modular communication hub 20065 is generally easy to install, configure, and maintain, making it a good option for networking operating room devices 1a-1n / 2a-2m.

[0122] Figure 5 A logic diagram of a control system 20220 of a surgical instrument or surgical tool according to one or more aspects of the present disclosure is shown. The surgical instrument or surgical tool may be configurable. The surgical instrument may include a surgical fixation device dedicated to the upcoming surgery, such as an imaging device, a surgical stapler, an energy device, an endoscopic cutting and closing device, etc. For example, the surgical instrument may include any of an electric stapler, a powered stapler generator, an energy device, a pre-energy device, a pre-energy jaw device, an endoscopic cutting and closing clamp, an energy device generator, an operating room imaging system, a smoke evacuator, a suction-irrigation device, an insufflation system, etc. The system 20220 may include a control circuit. The control circuit may include a microcontroller 20221, which includes 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 a longitudinally movable displacement member to drive an I-beam blade element. A tracking system 20228 may be configured to determine the position of the longitudinally movable displacement member. Position information may be provided to a processor 20222, which may be programmed or configured to determine the position of the longitudinally movable drive member and the position of the firing member, firing rod, and I-beam blade element. Additional motors may be provided at the tool driver interface to control I-beam firing, closure tube travel, shaft rotation, and joint motion. A display 20224 may display a variety of operating conditions of the instrument and may include a touch screen function for data input. The information displayed on the display 20224 may be superimposed with an image acquired via an endoscopic imaging module.

[0123] The microcontroller 20221 may be any single-core or multi-core processor, such as those offered by Texas Instruments under the trade name ARM Cortex. In one aspect, the main microcontroller 20221 may be a LM4F230H5QR ARM Cortex-M4F processor core available from, for example, Texas Instruments, which includes 256KB of single-cycle flash memory or other non-volatile memory (up to 40MHz) on-chip memory, a pre-fetch buffer for improving performance above 40MHz, 32KB of single-cycle SRAM, a CPU loaded with Internal ROM for software, 2KB EEPROM, one or more PWM modules, one or more QEI analog and / or one or more 12-bit ADC with 12 analog input channels, details of which can be found in the product datasheet.

[0124] The microcontroller 20221 may include a safety controller including two controller-based families such as TMS570 and RM4x, known also from Texas Instruments under the trade name Hercules ARM CortexR4. The safety controller may be configured to be specifically used for IEC 61508 and ISO 26262 safety-critical applications, etc., to provide advanced integrated safety features while delivering scalable execution, connectivity and memory options.

[0125] The microcontroller 20221 can be programmed to perform various functions, such as precise control of the speed and position of the knife and articulation system. In one aspect, the microcontroller 20221 may include a processor 20222 and a memory 20223. The electric motor 20230 may be a brushed DC (DC) motor having a gearbox and a mechanical link to the articulation or knife system. In one aspect, the motor driver 20229 may be an A3941 available from Allegro Microsystems, Inc. Other motor drivers can be easily replaced for use in a tracking system 20228 including an absolute positioning system. A detailed description of the absolute positioning system is described in U.S. Patent Application Publication No. 2017 / 0296213, entitled "SYSTEMS AND METHODS FOR CONTROLLING ASURGICAL STAPLING AND CUTTING INSTRUMENT," published on October 19, 2017, which is incorporated herein by reference in its entirety.

[0126] The microcontroller 20221 can be programmed to provide precise control of the speed and position of the displacement member and articulation system. The microcontroller 20221 can be configured to be able to calculate the response in the software of the microcontroller 20221. The calculated response can be compared with the measured response of the actual system to obtain an "observed" response, which is used for actual feedback decisions. The observed response can be a favorable tuning value that balances the smooth continuous nature of the simulated response with the measured response, which can detect external influences on the system.

[0127] The motor 20230 may be controlled by a motor driver 20229 and may be employed by a firing system of a surgical instrument or tool. 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 including a field effect transistor (FET). The motor 20230 may be powered by a power assembly that may be releasably mounted to a handle assembly or a tool housing for supplying control power to a surgical instrument or tool. The power assembly may include a battery that may include a plurality of battery cells connected in series that may be used as a power source to provide power to a surgical instrument or tool. In some cases, the battery cells of the power assembly may be replaceable and / or rechargeable. In at least one example, the battery cell may be a lithium-ion battery that may be coupled to the power assembly and detachable from the power assembly.

[0128] The motor driver 20229 may be an 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 unique charge pump regulator that provides full (>10V) gate drive for battery voltages as low as 7V and allows the A3941 to operate at a reduced gate drive as low as 5.5V. A bootstrap capacitor may be used to provide the above-mentioned battery supply voltage required for the N-channel MOSFET. The internal charge pump of the high-side driver may allow DC (100% duty cycle) operation. A diode or synchronous rectification may be used to drive the full bridge in fast decay mode or slow decay mode. In slow decay mode, current recirculation may pass through the high-side FET or the low-side FET. The power FET may be protected from breakdown by a resistor-adjustable dead time. Comprehensive diagnostics provide indications of undervoltage, overtemperature, and power bridge faults, and can be configured to protect the power MOSFETs under most short circuit conditions.Other motor drivers can be easily substituted for use in tracking system 20228 including an absolute positioning system.

[0129] The tracking system 20228 may include a controlled motor drive circuit arrangement including a position sensor 20225 according to one aspect of the present disclosure. The position sensor 20225 for the absolute positioning system can provide 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, which includes a rack of drive teeth for meshing engagement with a corresponding drive gear of the gear reducer assembly. In some examples, the displacement member can represent a firing member, which can be adapted and configured to include a rack of drive teeth. In some examples, the displacement member can represent a firing rod or an I-beam, each of which can be adapted and configured to include a rack of drive teeth. Therefore, as used herein, the term displacement member can be generally used to refer to any movable member of a surgical instrument or tool such as a drive member, a firing member, a firing rod, 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 rod, and the I-beam. Therefore, 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 can be coupled to any position sensor 20225 suitable for measuring linear displacement. Therefore, the longitudinally movable drive member, firing member, firing rod or I-beam or a combination thereof can 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 sliding 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 movable linearly arranged Hall effect sensors, an optical sensing 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 movable linearly arranged photodiodes or photodetectors, or any combination thereof.

[0130] The electric motor 20230 may include a rotatable shaft operably docked with a gear assembly that is mounted on a displacement member in meshing engagement with a set of drive teeth or a rack of drive teeth. The sensor element may be operably coupled to the gear assembly so that a single rotation of the position sensor 20225 element corresponds to a certain linear longitudinal translation of the displacement member. The arrangement of the transmission and sensor may be connected to a linear actuator via a rack and pinion arrangement, or to a rotary actuator via a spur gear or other connection. The power source may supply power to the absolute positioning system, and the output indicator may display the output of the absolute positioning system. The displacement member may represent a longitudinally movable drive member that includes a rack of drive teeth formed thereon for meshing engagement with a corresponding drive gear of a gear reducer assembly. The displacement member may represent a longitudinally movable firing member, a firing rod, an I-beam, or a combination thereof.

[0131] A single rotation of the sensor element associated with the position sensor 20225 may be equivalent to a longitudinal linear displacement d1 of the displacement member, where d1 is the longitudinal linear distance the displacement member moves from point "a" to point "b" following a single rotation of the sensor element coupled to the displacement member. The sensor arrangement may be connected via a gear reduction that enables the position sensor 20225 to complete one or more rotations for a full stroke of the displacement member. The position sensor 20225 may complete multiple rotations for a full stroke of the displacement member.

[0132] A series of switches (where n is an integer greater than one) may be employed, alone or in combination with gear reduction, to provide unique position signals for more than one rotation of the position sensor 20225. The state of the switches may be fed back to the microcontroller 20221, which applies logic to determine a unique position signal corresponding to the longitudinal linear displacement d1+d2+...dn of the displacement member. The output of the position sensor 20225 is provided to the microcontroller 20221. The position sensor 20225 of this sensor arrangement may include an array of magnetic sensors, analog rotation sensors (such as potentiometers), or analog Hall effect elements, which output a unique combination of position signals or values.

[0133] Position sensor 20225 may include any number of magnetic sensing elements, such as, for example, magnetic sensors classified according to whether they measure the total magnetic field or the vector components of the magnetic field. The technology used to produce the above two types of magnetic sensors can cover many aspects of physics and electronics. The technology used for magnetic field sensing may include probe coils, fluxgates, optical pumps, nuclear spins, superconducting quantum interference devices (SQUIDs), Hall effects, anisotropic magnetoresistance, giant magnetoresistance, magnetic tunnel junctions, giant magnetoimpedance, magnetostrictive / piezoelectric composites, magnetosensitive diodes, magnetosensitive transistors, optical fibers, magneto-optics, and magnetic sensors based on microelectromechanical systems, etc.

[0134] The position sensor 20225 for the tracking system 20228 including the absolute positioning system may include a magnetic rotation absolute positioning system. The position sensor 20225 may be implemented as an AS5055EQFT monolithic magnetic rotation position sensor available from Austria Microsystems, AG. The position sensor 20225 interfaces with the microcontroller 20221 to provide an absolute positioning system. The position sensor 20225 may be a low voltage and low power component and may include four Hall effect elements in the area of ​​the position sensor 20225 that may be located above the magnet. A high resolution ADC and an intelligent power management controller may also be provided on the chip. A coordinate rotation digital computer (CORDIC) processor (also known as the bitwise method and Volder algorithm) may be provided to perform a simple and efficient algorithm to calculate hyperbolic and trigonometric functions, which requires only addition, subtraction, bit shifting, and table lookup 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 12 or 14 bit resolution. The position sensor 20225 may be an AS5055 chip provided in a small QFN 16-pin 4×4×0.85 mm package.

[0135] The tracking system 20228 including an absolute positioning system may include and / or be programmed to implement feedback controllers such as PID, state feedback, and adaptive controllers. The power source converts the signal from the feedback controller into a physical input to the system: in this case, voltage. Other examples include PWM of voltage, current, and force. In addition to the position measured by the position sensor 20225, other sensors may be provided to measure physical parameters of the physical system. In some aspects, one or more other sensors may include sensor arrangements such as those described in U.S. Patent No. 9,345,481, entitled “STAPLE CARTRIDGE TISSUE THICKNESS SENSOR SYSTEM,” issued on May 24, 2016, which is incorporated herein by reference in its entirety; U.S. Patent Application Publication No. 2014 / 0263552, entitled “STAPLE CARTRIDGE TISSUETHICKNESS SENSOR SYSTEM,” issued on September 18, 2014, which is incorporated herein by reference in its entirety; and U.S. Patent Application Serial No. 15 / 628,175, entitled “TECHNIQUES FOR ADAPTIVE CONTROL OF MOTOR VELOCITY OF A SURGICAL STAPLING AND CUTTING INSTRUMENT,” filed on June 20, 2017, which is incorporated herein by reference in its entirety. In a digital signal processing system, the absolute positioning system is coupled to a digital data acquisition system, where the output of the absolute positioning system will have a finite resolution and sampling frequency. The absolute positioning system may include comparison and combination circuits to combine the calculated response with the measured response using an algorithm (such as weighted average and theoretical control loop) that drives the calculated response toward the measured response. The calculated response of the physical system may take into account characteristics such as mass, inertia, viscous friction, inductance and resistance to predict the state and output of the physical system by knowing the input.

[0136] Thus, the absolute positioning system can provide the absolute position of the displacement member when the instrument is powered on, and does not retract or advance the displacement member to a reset (zero or home) position as may be required by conventional rotary encoders, which simply count the number of forward or backward steps taken by the motor 20230 to infer the position of the device actuator, drive rod, knife, etc.

[0137] Sensor 20226 (such as, for example, a strain gauge or micro strain gauge) can be configured to measure one or more parameters of the end effector, such as, for example, the amplitude of the strain applied to the anvil during the clamping operation, which can indicate the closing force applied to the anvil. The measured strain can be converted into a digital signal and provided to the processor 20222. Alternatively or in addition to sensor 20226, sensor 20227 (such as, for example, a load sensor) can measure the closing force applied to the anvil by the closed drive system. Sensor 20227 (such as, for example, a load sensor) can measure the firing force applied to the I-beam during the firing stroke of the surgical instrument or tool. The I-beam is configured to engage a wedge-shaped sliding member, which is configured to enable the nail driver to cam upward to push the nail out to deform and contact the anvil. The I-beam may also include a sharp cutting edge, which can be used to cut tissue when the I-beam is pushed distally by the firing rod. Alternatively, a current sensor 20231 can be employed to measure the current drawn by the motor 20230. For example, the force required to advance the firing member can correspond to the current drawn by the motor 20230. The measured force can be converted into a digital signal and provided to the processor 20222.

[0138] For example, a strain gauge sensor 20226 can be used to measure the force applied to the tissue by the end effector. A strain gauge can be coupled to the end effector to measure the force on the tissue processed by the end effector. A system for measuring the force applied to the tissue grasped by the end effector may include a strain gauge sensor 20226, such as a micro strain gauge, which can be configured to measure, for example, 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 applied to the jaw member of the end effector during the clamping operation, which can indicate tissue compression. The measured strain can be converted into a digital signal and provided to the processor 20222 of the microcontroller 20221. The load sensor 20227 can measure the force used to operate the knife element, for example, to cut the tissue captured between the anvil and the nail magazine. A magnetic field sensor can be used to measure the thickness of the captured tissue. The measurement results of the magnetic field sensor can also be converted into a digital signal and provided to the processor 20222.

[0139] The microcontroller 20221 can characterize the corresponding values ​​of the selected position of the firing member and / or the velocity of the firing member using the measurements of tissue compression, tissue thickness, and / or the force required to close the end effector on the tissue measured by the sensors 20226, 20227, respectively. In one embodiment, the memory 20223 can store techniques, formulas, and / or lookup tables that can be used by the microcontroller 20221 in the evaluation.

[0140] The control system 20220 of the surgical instrument or tool may also include wired or wireless communication circuitry to communicate with the surgical hub 20065, such as Figure 4 shown.

[0141] Figure 6 An exemplary surgical system 20280 according to the present disclosure is shown and may include a surgical instrument 20282 that communicates with a console 20294 or a portable device 20296 via a wired and / or wireless connection through a local area network 20292 and / or a cloud network 20293. 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 a 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 to measure 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 situ loading or multi-fire loading unit (MFLU) that allows the clinician to fire multiple fasteners multiple times without removing the loading unit 20287 from the surgical site to reload the loading unit 20287.

[0142] The first jaw 20291 and the second jaw 20290 can be configured to clamp tissue therebetween, fire fasteners through the clamped tissue, and cut the clamped tissue. The first jaw 20291 can be configured to fire at least one fastener multiple times, or can be configured to include a replaceable multiple firing fastener bin, which includes multiple fasteners (e.g., nails, clamps, etc.) that can be fired more than once before being replaced. The second jaw 20290 may include an anvil that deforms the fastener or otherwise fixes the fastener when the fastener is ejected from the multiple firing fastener bin.

[0143] The handle 20297 may include a motor coupled to the drive shaft to affect the rotation of the drive shaft. The handle 20297 may include a control interface for selectively activating the motor. The control interface may include a button, a switch, a lever, a slider, a touch screen, and any other suitable input mechanism or user interface that can be engaged by a clinician to activate the motor.

[0144] The control interface of the handle 20297 can communicate with the controller 20298 of the handle 20297 to selectively activate the motor to affect the rotation of the drive shaft. The controller 20298 can be disposed within the handle 20297 and configured to receive input from the control interface and adapter data from the adapter 20285 or loading unit data from the loading unit 20287. The controller 20298 can analyze the input from the control interface and the data received from the adapter 20285 and / or the loading unit 20287 to selectively activate the motor. The handle 20297 can also include a display that can be viewed by a clinician during use of the handle 20297. The display can be configured to display a portion of the adapter or loading unit data before, during, or after firing the instrument 20282.

[0145] 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 should be understood that the loading unit identification device 20288 may communicate with the adapter identification device 20284, which relays or transmits communications from the loading unit identification device 20288 to the controller 20298.

[0146] The adapter 20285 may also include a plurality of sensors 20286 (one is shown) disposed about it to detect various conditions 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 retraction force of the adapter 20285, the number of pauses of the adapter 20285 during firing, etc.). The plurality of sensors 20286 may provide input to the adapter identification device 20284 in the form of data signals. The data signals of the plurality of sensors 20286 may be stored in the adapter identification device 20284, or may be used to update the adapter data stored in the adapter identification device. The data signals of the plurality of sensors 20286 may be analog or digital. The plurality of sensors 20286 may include a force gauge to measure the force applied to the loading unit 20287 during firing.

[0147] The handle 20297 and the adapter 20285 can 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 can be a direct electrical interface (i.e., comprising electrical contacts that engage with each other to transmit energy and signals therebetween). Additionally or alternatively, the electrical interface can be a contactless electrical interface to wirelessly transmit energy and signals therebetween (e.g., inductive transmission). It is also conceivable that the adapter identification device 20284 and the controller 20298 can communicate wirelessly with each other via a wireless connection separate from the electrical interface.

[0148] The handle 20297 may include a transceiver 20283 that is 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 also 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 to the console 20294, the instrument data including the serial number of the attachment adapter (e.g., adapter 20285) attached to the handle 20297, the serial number of the loading unit (e.g., loading unit 20287) attached to the adapter 20285, and the serial number of the multi-fire fastener cartridge loaded into the loading unit. Thereafter, the console 20294 may transmit data associated with the attached cartridge, loading unit, and adapter, respectively (e.g., cartridge data, loading unit data, or adapter data) back to the controller 20298. The controller 20298 may display a message on a local instrument display, or transmit the 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.

[0149] Figure 7A diagram of a situational awareness surgical system 5100 is illustrated in accordance with at least one aspect of the present disclosure. Data sources 5126 may include, for example, modular devices 5102 (the modular devices may include sensors configured to detect parameters associated with the patient, HCP, and environment and / or the modular devices themselves), databases 5122 (e.g., an EMR database containing patient records), and patient monitoring devices 5124 (e.g., a blood pressure (BP) monitor and an electrocardiogram (EKG) monitor), HCP monitoring devices 35510, and / or environmental monitoring devices 35512. The surgical hub 5104 may be configured to be able to derive contextual information related to the surgical procedure from the data, for example, based on a particular combination of the received data or a particular order in which the data is received from the data sources 5126. The contextual information inferred from the received data may include, for example, the type of surgical procedure being performed, the particular step of the surgical procedure being performed by the surgeon, the type of tissue being operated on, or the body cavity being the subject of the surgery. This ability of some aspects of the surgical hub 5104 to derive or infer information related to the surgical procedure from the received data may be referred to as "situational awareness." For example, the surgical hub 5104 may incorporate a situational awareness system, which is hardware and / or programming associated with the surgical hub 5104 that derives contextual information related to the surgical procedure from data received and / or surgical planning information received from the edge computing system 35514 or the enterprise cloud server 35516.

[0150] The situational awareness system of the surgical hub 5104 may be configured to be able to derive contextual information from data received from the data source 5126 in a variety of different ways. For example, the situational awareness system may include a pattern recognition system or a machine learning system (e.g., an artificial neural network) that has been trained on training data to associate various inputs (e.g., 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 contextual information about the surgical procedure. The machine learning system may be trained to accurately derive contextual information about the surgical procedure from the provided inputs. In an example, the situational awareness system may include a lookup table that stores pre-characterized environmental information about the surgical procedure associated with one or more inputs (or input ranges) corresponding to environmental information. In response to a query using one or more inputs, the lookup table may return corresponding contextual information used by the situational awareness system to control the modular device 5102. In an example, the contextual information received by the situational awareness system of the surgical hub 5104 may be associated with a specific control adjustment or a set of control adjustments for one or more modular devices 5102. In an example, the situational awareness system may include an additional machine learning system, lookup table, or other such system that generates or retrieves one or more control adjustments for one or more modular devices 5102 when contextual information is provided as input.

[0151] The surgical hub 5104 incorporating a situational awareness system can provide numerous benefits to the surgical system 5100. One benefit can include improved interpretation of sensed and collected data, which in turn will improve processing accuracy and / or use of the data during the surgical procedure. Returning to the previous example, the situational awareness surgical hub 5104 can determine the type of tissue being operated on; thus, when an unexpectedly high force is detected for the end effector of a closing surgical instrument, the situational awareness surgical hub 5104 can properly ramp up or ramp down the motor speed of the surgical instrument for the tissue type.

[0152] The type of tissue being operated on can affect the adjustment of the compression rate and load threshold of the surgical stapling and cutting instrument for a specific tissue gap measurement value. The situational awareness surgical hub 5104 can infer whether the surgical procedure being performed is a thoracic procedure or an abdominal procedure, thereby allowing the surgical hub 5104 to determine whether the tissue clamped by the end effector of the surgical stapling and cutting instrument is lung tissue (for thoracic surgery) or stomach tissue (for abdominal surgery). The surgical hub 5104 can then appropriately adjust the compression rate and load threshold of the surgical stapling and cutting instrument for the type of tissue.

[0153] The type of body cavity being operated on during an insufflation procedure can affect the function of the smoke evacuator. The situational awareness surgical hub 5104 can determine whether the surgical site is under pressure (by determining that the surgical procedure is utilizing insufflation) and determine the type of surgery. Since one type of surgery can often be performed in a particular body cavity, the surgical hub 5104 can then control the motor rate of the smoke evacuator appropriately for the body cavity in which the operation is being performed. Thus, the situational awareness surgical hub 5104 can provide consistent smoke evacuation for both thoracic and abdominal surgeries.

[0154] The type of surgery being performed can affect the optimal energy level at which an ultrasonic surgical instrument or radio frequency (RF) electrosurgical instrument operates. For example, arthroscopic surgery may require a higher energy level because the end effector of the ultrasonic surgical instrument or RF electrosurgical instrument is immersed in a fluid. 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 or ultrasonic amplitude (e.g., "energy level") of the generator to compensate for the fluid-filled environment. Relatedly, the type of tissue being operated on can affect the optimal energy level at which an ultrasonic surgical instrument or RF electrosurgical instrument operates. The situational awareness surgical hub 5104 can determine the type of surgical procedure being performed and then customize the energy level of the ultrasonic surgical instrument or RF electrosurgical instrument, respectively, according to the expected tissue profile of the surgical procedure. In addition, the situational awareness surgical hub 5104 can be configured to be able to adjust the energy level of the ultrasonic surgical instrument or RF electrosurgical instrument throughout the surgical procedure rather than just on a surgery-by-surgery basis. The situational awareness surgical hub 5104 can determine the step of a surgical procedure being performed or to be subsequently performed and then update the control algorithm of the generator and / or ultrasonic surgical instrument or RF electrosurgical instrument to set the energy level to a value appropriate for the expected tissue type based on the surgical step.

[0155] In an example, data may be extracted from additional data sources 5126 to improve conclusions extracted by the surgical hub 5104 from one data source 5126. The situational awareness surgical hub 5104 may augment the data it receives from the modular device 5102 with contextual information about the surgical procedure that has been constructed from other data sources 5126. For example, the situational awareness surgical hub 5104 may be configured to be able to determine whether hemostasis has occurred (e.g., whether bleeding at a surgical site has stopped) based on video or image data received from a medical imaging device. The surgical hub 5104 may be further configured to be able to compare 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) to determine the integrity of the staple line or tissue weld. The situational awareness system of the surgical hub 5104 may take into account the physiological measurement data to provide additional context when analyzing the visual data. Additional context can be useful when the visualization data itself may be uncertain or incomplete.

[0156] For example, if the situational awareness surgical hub 5104 determines that a subsequent step of the surgery requires the use of an RF electrosurgical instrument, it can actively activate the generator connected to the instrument. Actively activating the energy source can allow the instrument to be ready for use as soon as the previous step of the surgery is completed.

[0157] The situational awareness surgical hub 5104 may determine whether the current or subsequent steps of the surgical procedure require a different view or degree of magnification on the display based on the features that the surgeon anticipates needing to view at the surgical site. The surgical hub 5104 may proactively change the displayed view (e.g., provided by a medical imaging device for the visualization system) accordingly, such that the display is automatically adjusted throughout the surgical procedure.

[0158] The situational awareness surgical hub 5104 can determine which step of the surgical procedure is being performed or will be performed next and whether specific data or comparisons between data are required for that step of the surgical procedure. The surgical hub 5104 can be configured to automatically call up data screens based on the step of the surgical procedure being performed without having to wait for the surgeon to request that specific information.

[0159] Errors may be checked during the setup of a surgical procedure or during the course of a surgical procedure. For example, the situational awareness surgical hub 5104 may determine whether the operating room is correctly or optimally set up for the surgical procedure to be performed. The surgical hub 5104 may be configured to be able to determine the type of surgical procedure being performed, retrieve the corresponding list, product location, or setup requirements (e.g., from a memory), and then compare the current operating room layout with the standard layout determined by the surgical hub 5104 for the type of surgical procedure being performed. In some examples, the surgical hub 5104 may compare a list of items for surgery and / or a list of devices paired with the surgical hub 5104 with a suggested or expected list of items and / or devices for a given surgical procedure. If there is any discontinuity between the lists, the surgical hub 5104 may provide an alert indicating that a particular modular device 5102, a patient monitoring device 5124, an HCP monitoring device 35510, an environmental monitoring device 35512, and / or other surgical items are missing. In some examples, the surgical hub 5104 can determine the relative distance or position of the modular device 5102 and the patient monitoring device 5124, for example, via a proximity sensor. The surgical hub 5104 can compare the relative position of the devices to a suggested or expected layout for a particular surgical procedure. If there are any discontinuities between the layouts, the surgical hub 5104 can be configured to provide an alert indicating that the current layout for the surgical procedure deviates from the suggested layout.

[0160] The situational awareness surgical hub 5104 may determine if a surgeon (or other HCP) is making an error or otherwise deviating from an expected course of action during a surgical procedure. For example, the surgical hub 5104 may be configured to determine the type of surgical procedure being performed, retrieve a corresponding list of steps or order of equipment use (e.g., from memory), and then compare the steps being performed or equipment being used during the surgical procedure to the expected steps or equipment determined by the surgical hub 5104 for the type of surgical procedure being performed. The surgical hub 5104 may provide an alert indicating that an unexpected action is being performed or an unexpected device is being utilized at a particular step in the surgical procedure.

[0161] The surgical instruments (and other modular devices 5102) can be adjusted for the specific context of each surgical procedure (such as adjusting to different tissue types) and verification 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 surgical room according to the specific context of the surgery.

[0162] Figure 8An example of an autonomous update of a surgical device control algorithm 49300 is shown. The control algorithm may be pre-installed on 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 a surgical device type associated with the surgical device. As shown, a surgical device 49305 (e.g., Fig. 9 The modular device 9050 described herein can be used for a surgical procedure 49302 in an operating room 49303. For example, the surgical device 49305 can be a surgical stapler pre-installed with a baseline control algorithm associated with controlling closing force (FTC) and / or a baseline control algorithm associated with controlling firing force (FTF), or a baseline control algorithm associated with controlling FTC and FTF. The surgical device 49305 can be any of the modular devices described herein.

[0163] When surgical device 49305 is activated in operating room 49303, surgical device 49305 may communicate (e.g., pair or link) with surgical hub 49306 (e.g., before surgical device 49305 is operated as part of surgical procedure 49302). It may be determined whether the pre-installed baseline control algorithm of the activated surgical device 49305 is the latest version, e.g., in response to surgical device 49305 communicating with surgical hub 49306. In some examples, surgical hub 49306 may push the latest version (e.g., the latest version) of the control algorithm to surgical device 49305. In response, surgical device 49305 may determine whether the pre-installed baseline control algorithm is the same version as the latest version of the control algorithm. If it is determined that the pre-installed baseline control algorithm is an older version, surgical device 49305 may replace it with the latest version from surgical hub 49306; otherwise, surgical device 49305 discards the latest version from surgical hub 49306. In some examples, the surgical device 49305 may communicate with the surgical hub 49306 to determine whether its pre-installed baseline control algorithm is the latest version. The surgical device 49305 may 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 may retrieve the latest version of the control algorithm from the surgical hub.

[0164] The surgical device 49305 may be operated (e.g., by a surgeon) during a surgical procedure 49302 (which may be a segmentectomy). Perioperative data (such as operational data associated with the surgical device 49305) may be sensed by the surgical device 49305. For example, the operational data may include a wait time before initiating a firing. The operational data may include an FTC over time. The operational data includes an FTF over time. The operational data may be sent to a surgical hub 49306. Perioperative data (such as outcome data of the surgical procedure 49302) may be sent to the surgical hub 49306. For example, the outcome data may include data indicating whether there is an air or fluid leak at the surgical site, whether the staples of a particular staple line are properly formed, and / or whether there is bleeding at the surgical site. The operational data and outcome data may be paired as paired data 49304. Paired data 49304 may also include control algorithm information associated with the operational data. For example, the control algorithm information may include a unique identifier and version number of a control algorithm that controls the FTC. Perioperative data is described in more detail in the detailed description of Figure 194 in U.S. patent application publication US20190206562 A1 (U.S. patent application 16 / 209,416), entitled “Method of hub communication, processing, display, and cloud analytics,” filed on December 4, 2018, the disclosure of which is incorporated herein by reference in its entirety.

[0165] The surgical hub 49306 may send pairing data 49304 associated with the surgical procedure 49302 to a remote system 49312. The remote system may include a remote server 49314 (e.g., Fig. 9 The remote system may be a cloud computing system, for example, 9100 as described in FIG. 15 and FIG. 17 .

[0166] The surgical hub 49306 may send paired data 49308 associated with a plurality of surgical procedures 49302 to a remote system 49312 (e.g., after paired data 49304 from different surgical procedures has been accumulated over a period of time). The plurality of surgical procedures may be of one surgical procedure type or more than one surgical procedure type. The surgical hub 49306 may send other perioperative data associated with the plurality of surgical procedures 49302 to the remote system 49312, such as preoperative data including patient-specific information (e.g., age, employer, body mass index (BMI), or any data that can be used to determine the patient's identity).

[0167] In one example, the surgical hub 49306 may be located within a data protection boundary 49322 associated with a medical facility (e.g., a hospital), such as a Health Insurance Portability and Accountability Act (HIPAA) boundary. If the paired data 49304 includes patient private information, such as age, employer, body mass index (BMI), or any data that can be used to determine the patient's identity, the surgical hub 49306 may edit the paired data 49308 before sending it to the remote system 49312. The editing process is described in more detail under the heading "Data Management and Collection" in U.S. Patent Application Publication No. US20190206562 A1 (U.S. Patent Application No. 16 / 209,385), filed on December 4, 2018, entitled "Method of hub communication, processing, storage and display," the disclosure of which is incorporated herein by reference in its entirety.

[0168] The remote system 49312 may receive the paired data 49308. The remote system 49312 (e.g., in response) may aggregate and / or analyze the received paired data 49308 to determine whether there is a correlation between the operational data and the outcome data. The remote system 49312 may determine that a control algorithm associated with a surgical device type (e.g., a surgical stapler) requires an update based on determining that there is a correlation between the operational data and the outcome data. For example, if the remote system 49312 determines that there is a correlation between one aspect of the control algorithm and a negative outcome, the remote system 49312 may determine that an updated control algorithm 49316 for the surgical device type is required and may generate the updated control algorithm 49316.

[0169] The remote system 49312 (e.g., in response to generating an updated control algorithm 49316) may send the updated control algorithm 49316 to the surgical hub 49306. In response to receiving the updated control algorithm 49316, the surgical hub 49306 may push it to the paired surgical device 49318 of the surgical device type associated with the updated control algorithm 49316 (e.g., if the corresponding control algorithm installed on 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, but not to the surgical devices that are in communication with the surgical hub and have already received the updated control algorithm 49316. In some examples, the surgical hub 49306 can push updated control algorithms to a paired surgical device in communication with the surgical hub 49306. The paired surgical device can determine whether to update a corresponding algorithm installed thereon, for example, as described herein.

[0170] Fig. 9 A block diagram of a computer-implemented adaptive surgical system 9060 configured to adaptively generate control program updates for a modular apparatus 9050 is shown in accordance with at least one aspect of the present disclosure.

[0171] The modular device includes a module that can be housed within a surgical hub (e.g., such as a combination of Figure 3 and Fig. 9 The invention relates to a surgical device or instrument that can be connected to various modules. The modular device includes, for example, an intelligent surgical instrument, a medical imaging device, a suction / irrigation device, a smoke evacuator, an energy generator, a ventilator, and an insufflator. The various operations of the modular device described herein can be controlled by one or more control algorithms. The control algorithm can be executed on the modular device itself, on a surgical hub paired with a specific modular device, or on both the modular device and the surgical hub (e.g., via a distributed computing architecture). In some examples, the control algorithm of the modular device can control the device based on data sensed by the modular device itself (i.e., by sensors in the modular device, sensors on the modular device, or sensors connected to the modular device). The data can be related to the patient being operated on (e.g., tissue properties or insufflation pressure) or the modular device itself (e.g., the rate at which the knife is advanced, the motor current, or the energy level). For example, the control algorithm of the surgical stapling and cutting instrument can control the rate at which the motor of the instrument drives its knife through the tissue according to the resistance encountered by the knife as it advances.

[0172] Although a "smart" device including a control algorithm responsive to sensed data may be an improvement over a "dumb" device that operates without regard to sensed data, if the control program of the device is not adapted or updated over time in response to the collected data, the device may continue to repeat errors or otherwise perform suboptimally. In one example, the operational data collected by the modular device may be combined with the results of each operation (or its steps). The combination may be sent to an analysis system. In one example, the situational awareness system of the surgical hub paired with the modular device may infer the surgical results, as described in U.S. patent application serial number 15 / 940,654 entitled "SURGICAL HUB SITUATIONAL AWARENESS", which is incorporated herein by reference in its entirety. The analysis system may analyze data aggregated from a group of modular devices or a specific type of modular device to determine under what conditions the control program of the analyzed modular device controls the modular device suboptimally (e.g., if there are repeated faults or errors in the control program, or if an alternative algorithm performs in an excellent manner) or under what conditions medical staff suboptimally utilize modular devices. The analysis system may then generate an update to repair or improve the control program of the modular device. Different types of modular devices may be controlled by different control programs; therefore, the control program update may be specific to the type of modular device on which the analysis system is performing suboptimally. The analysis system may then push the update to the appropriate modular device connected to the analysis system through the surgical hub.

[0173] 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 depicted, it should be noted that the surgical system 9060 may 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 instructions stored thereon and a data relay interface 9030 through which data is transmitted to the analysis system 9100. In one example, the surgical hub 9000 also includes a user interface 9090 having an input device 9092 (e.g., a capacitive touch screen or keyboard) for receiving input from a user and an output device 9094 (e.g., a display screen) for providing output to the user. The output may include data from a user's query input, a recommendation for a product or product mixture to be used in a given surgery, and / or instructions for actions to be performed before, during, or after a surgical procedure. The surgical hub 9000 also includes an interface 9040 for communicatively coupling a modular device 9050 to the surgical hub 9000. In one aspect, the interface 9040 includes a transceiver capable of being communicatively connected to the modular device 9050 via a wireless communication protocol. The modular device 9050 may include, for example, surgical stapling and cutting instruments, electrosurgical instruments, ultrasonic instruments, insufflators, ventilators, and display screens. In one example, the surgical hub 9000 may also be 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 may also be communicatively coupled to one or more databases 9054 or external computer systems, such as an EMR database of a medical facility where the surgical hub 9000 is located.

[0174] When the modular device 9050 is connected to the surgical hub 9000, the surgical hub 9000 may sense or receive perioperative data from the modular device 9050, and then associate the received perioperative data with surgical procedure outcome data. The perioperative data indicates how the modular device 9050 is controlled during the surgical procedure. The surgical outcome data includes data associated with the outcome from the surgical procedure (or its steps), which may include whether the surgical procedure (or its steps) has a positive or negative outcome. For example, the outcome data may include whether the patient suffers from postoperative complications from a particular surgery or whether there is a leak (e.g., bleeding or air leak) at a particular nail or incision line. The surgical hub 9000 may obtain surgical procedure outcome data by receiving data from an external source (e.g., from an 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 situational awareness system. For example, data about postoperative complications may be retrieved from the EMR database 9054, and data about nail or incision line leakage may be directly detected or inferred by the situational awareness system. Surgical procedure outcome data may be inferred by the situational awareness system from data received from various data sources including the modular device 9050 itself, the patient monitoring device 9052, and a database 9054 to which the surgical hub 9000 is connected.

[0175] The surgical hub 9000 may transmit the associated modular device 9050 data and result data to the analysis system 9100 for processing thereon. By transmitting both perioperative data and procedure result data indicating how to control the modular device 9050, the analysis system 9100 may associate different ways of controlling the modular device 9050 with surgical results for a particular procedure type. In one example, 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 may include a memory and a processor coupled to the memory that executes instructions stored thereon to analyze the received data. In some examples, the analysis servers 9070 are connected in a distributed computing architecture and / or utilize a cloud computing architecture. Based on the paired data, the analysis system 9100 may then learn the optimal or preferred operating parameters of various types of modular devices 9050, generate adjustments to the control programs of the on-site modular devices 9050, and then transmit (or "push") updates to the control programs of the modular devices 9050.

[0176] Additional details regarding the computer-implemented interactive surgical system 9060 are described herein, including a surgical hub 9000 and various modular devices 9050 that may be connected thereto.

[0177] To help understand Fig. 9The process 9100 shown and other concepts discussed above, Fig.10 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 is shown. In one example, a surgical hub 9000 or a network of surgical hubs 9000 may be communicatively coupled to the analysis system 9100, as described above in Fig. 9 . The analysis system 9100 is configured to filter and analyze the modular device 9050 data associated with the surgical procedure result data to determine whether adjustments need to be made to the control program of the modular device 9050. The surgical hub 9100 can then push updates to the modular device 9050 through the surgical hub 9000 as needed. In the depicted example, the analysis system 9100 includes a cloud computing architecture. The modular device 9050 perioperative data received by the surgical 9000 hub from its paired modular devices 9050 may include, for example, firing force (i.e., the force required to advance the cutting member of the surgical stapling instrument through the tissue), closing force (i.e., the force required to clamp the jaws of the surgical stapling instrument on the tissue), power algorithm (i.e., the change in power of the electrosurgical or ultrasonic instrument over time in response to the internal state of the instrument and / or tissue conditions), tissue characteristics (e.g., impedance, thickness, stiffness, etc.), tissue gap (i.e., the thickness of the tissue) and closing rate (i.e., the rate at which the jaws of the instrument are clamped closed). It should be noted that the modular device 9050 data transmitted to the analysis system 9100 is not limited to a single type of data and may include multiple different data types paired with surgical outcome data. Surgical outcome data for a surgical procedure (or a step thereof) may include, for example, whether there is bleeding at the surgical site, whether there is air or fluid leakage at the surgical site, and whether the nails of a particular nail line are properly formed. The surgical outcome data may also include, for example, positive or negative results determined by the surgical hub 9000 or the analysis system 9100 or associated with it. The modular device 9050 data and the procedure outcome data corresponding to the modular device 9050 perioperative data can be paired together or otherwise associated with each other when they are uploaded to the analysis system 9100, so that the analysis system 9100 can identify trends in the procedure results based on the underlying data of the modular device 9050 that produced each specific result. In other words, the analysis system 9100 may aggregate the modular device 9050 data and the surgical outcome data to search for trends or patterns in the underlying device modular data 9050 that may indicate adjustments that may be made to the control program of the modular device 9050.

[0178] In the depicted example, the combination Fig. 9The analysis system 9100 of the process 9200 is receiving 9202 modular device 9050 data and surgical outcome data. When transmitted to the analysis system 9100, the procedure outcome data can be associated or paired with the modular device 9050 data corresponding to the operation of the modular device 9050 that resulted in a specific procedure outcome. The modular device 9050 perioperative data and the corresponding procedure outcome data can be referred to as a data pair. The data is depicted as including a first set 9212 of data associated with a successful surgical outcome and a second set 9214 of data associated with a negative surgical outcome. For this particular example, a subset of the data 9212, 9214 received 9202 by the analysis system 9100 is highlighted to further illustrate the concepts discussed herein.

[0179] For the first data pair 9212a, the modular device 9050 data includes closing force (FTC) over time, firing force (FTF) over time, tissue type (parenchyma), tissue condition (tissue from a patient with emphysema and has been irradiated), the number of instrument firings (third time), an anonymous timestamp (to protect patient confidentiality while still allowing the analysis system to calculate the elapsed time between firings and other such metrics), and an anonymous patient identifier (002). The surgical outcome data includes data indicating the absence of bleeding, which corresponds to a successful outcome (i.e., successful firing of the surgical stapling instrument). For the second data pair 9212b, the modular device 9050 data includes the waiting time before the instrument is fired (which corresponds to the first firing of the instrument), FTC over time, FTF over time (which indicates that there is a force peak near the end of the firing stroke), tissue type (1.1 mm vascular), tissue condition (tissue has been irradiated), the number of instrument firings (first time), an anonymous timestamp, and an anonymous patient identifier (002). The surgical outcome data includes data indicating the presence of a leak, which corresponds to a negative outcome (i.e., a failure to fire the surgical stapling instrument). For the third data pair 9212c, the modular device 9050 data includes the waiting time before the instrument is fired (which corresponds to the first firing of the instrument), FTC over time, FTF over time, tissue type (1.8 mm vessels), tissue condition (no significant condition), number of firings of the instrument (first time), an anonymous timestamp, and an anonymous patient identifier (012). The surgical outcome data includes data indicating the presence of a leak, which corresponds to a negative outcome (i.e., a failure to fire the surgical stapling instrument). It should be noted again that the data is for illustrative purposes only to aid in understanding 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.

[0180] As the analysis system 9100 receives 9202 perioperative data from the communicatively connected surgical hub 9000, the analysis system 9100 proceeds to aggregate and / or store the data according to the type of surgery (or steps thereof) associated with the data, the type of modular device 9050 that generated the data, and other such categories. By arranging the data accordingly, the analysis system 9100 can analyze the data set to identify correlations between specific ways of controlling each specific type of modular device 9050 and positive or negative procedure outcomes. Based on whether a specific way of controlling a modular device 9050 is associated with a positive or negative procedure outcome, the analysis system 9100 can determine 9204 whether the control program for the type of modular device 9050 should be updated.

[0181] For this particular example, the analysis system 9100 performs a first analysis 9216 of the data set by analyzing the peak FTF 9213 (i.e., the maximum FTF for each specific firing of the surgical stapling instrument) relative to 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 the particular data set. In other words, there is no different distribution of the peak FTF 9213 for positive and negative results. Since there is no specific correlation between the peak FTF 9213 and the positive or negative results, the analysis system 9100 will determine that a control program update to address this variable is not required. In addition, the analysis system 9100 performs a second analysis 9216b of the data set by analyzing the waiting time 9215 before the instrument is fired relative to the number of firings 9211. For this particular analysis 9216b, the analysis system 9100 can determine that there are different negative result distributions 9217 and positive result distributions 9219. In this exemplary case, negative outcome distribution 9217 has a mean of 4 seconds and positive outcome distribution has a mean of 11 seconds. Thus, analysis system 9100 may determine that there is a correlation between wait time 9215 and the outcome type for this surgical procedure. That is, negative outcome distribution 9217 indicates that there is a relatively large ratio of negative outcomes within wait times of 4 seconds or less. Based on this analysis 9216b confirming that there is a large divergence between negative outcome distribution 9217 and positive outcome distribution 9219, analysis system 9100 may then determine 9204 that a control program update should be generated 9208.

[0182] Once the analysis system 9100 analyzes the data set and determines 9204 that adjustments to the control program of the particular modular device 9050 that is the subject of the data set will improve the performance of the modular device 9050, the analysis system 9100 then generates 9208 a control program update accordingly. In this exemplary case, the analysis system 9100 may determine based on the analysis 9216b of the data set that a control program update 9218 that recommends a wait time of more than 5 seconds will prevent a 90% distribution of negative results with a 95% confidence interval. Alternatively, the analysis system 9100 may determine based on the analysis 9216b of the data set that a control program update 9218 that recommends a wait time of more than 5 seconds will result in a greater rate of positive results than a rate of negative results. Thus, the analysis system 9100 may determine that a particular type of surgical instrument should wait more than 5 seconds before firing under particular tissue conditions so that negative results are less common than positive results. Based on either or both of these constraints used to generate 9208 a control program update that the analysis system 9100 determines is satisfied by the analysis 9216b, the analysis system 9100 may generate 9208 a control program update 9218 for a surgical instrument that causes the surgical instrument, in a given situation, to impose a wait time of 5 seconds or more before a particular surgical instrument can be fired, or causes the surgical instrument to display a warning or suggestion to the user indicating to the user that the user should wait at least 5 seconds before firing the instrument. The analysis system 9100 may utilize various other constraints to determine whether to generate 9208 a control program update, such as whether the control program update reduces the rate of negative outcomes by a certain percentage or whether the control program update maximizes the rate of positive outcomes.

[0183] After generating 9208 the control program updates 9218, the analysis system 9100 then transmits 9210 the control program updates 9218 for the appropriate type of modular devices 9050 to the surgical hub 9000. In one example, when the modular devices 9050 corresponding to the control program updates 9218 are next connected to the surgical hub 9000 that has downloaded the control program updates 9218, the modular devices 9050 then automatically download the updates 9218. In another example, the surgical hub 9000 controls the modular devices 9050 in accordance with the control program updates 9218, rather than transmitting the control program updates 9218 directly to the modular devices 9050 themselves.

[0184] Fig.11An exemplary process for autonomous updating of a surgical device control algorithm is shown. As shown, there may be multiple operating rooms 49303 in a medical facility (e.g., a hospital). Multiple operating rooms 49303 may be within a data protection boundary 49322 associated with the medical facility (e.g., as described herein). Multiple operating rooms 49303 (e.g., via surgical hub 49306) may send pairing data 49308 to remote system 49401 (e.g., after editing patient private information in pairing data 49308 as described herein, beyond data protection boundary 49322).

[0185] Remote system 49401 (eg, remote system 49312) may include autonomous update subsystem 49410. Autonomous update subsystem 49410 may include analysis server 49414 (eg, Figure 8 49314) and data storage 49410 (e.g., Figure 8 49310 described in ). The analysis server 49414 may include an aggregation process 49402 and an analysis process 49404.

[0186] The data store 49410 may store a set of control algorithms 49406. The set of control algorithms 49406 may include the latest versions of the 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 may be stored in the data store 49410. A unique identifier and version number for each control algorithm may be stored in the data store 49410.

[0187] After the remote system 49401 receives the pairing data 49308, the autonomous update subsystem 49410 may determine whether the pairing data 49308 is to be discarded or used to generate a control algorithm update. In an example, the autonomous update subsystem 49410 may determine whether the control algorithms included in the pairing data 49308 (e.g., control algorithm information associated with the operational data portion of the pairing data 49308) are up to date. If the version numbers associated with the control algorithms indicate that they are not the latest version numbers, the autonomous update subsystem 49410 may 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 may perform further processing on the associated pairing data / operational data.

[0188] Aggregation process 49402 may aggregate paired data 49308 based on surgical device type. Aggregation process 49402 may aggregate paired data 49308 based on surgical procedure type. Aggregation process 49402 may aggregate paired data 49308 based on surgical device type and surgical procedure type. Aggregation process 49402 may aggregate paired data 49308 based on any other such classification.

[0189] Based on the aggregated paired data, the analysis process 49404 can identify correlations between aspects of the operational data and outcome data associated with the surgical device. For example, the analysis process 49404 can perform an analysis of the data set by analyzing the wait time 9215 before the surgical stapler is fired relative to the firing number 9211 (e.g., Fig.10 9216b in the analysis). Analysis process 49404 may determine that there are different negative outcome distributions (e.g., Fig.10 9217 in the analysis 9216b shown) and the distribution of positive results (e.g., as Fig.10 9219 in analysis 9216b shown in FIG. 40). In one example, a negative result distribution may have a mean of 4 seconds and a positive result distribution may have a mean of 11 seconds. Analysis process 49404 may determine the wait time (e.g., Fig.10 There is a correlation between the analysis 9215 in 9216b shown and the type of outcome for the surgical procedure, that is, the negative outcome distribution may indicate that there is a relatively large ratio of negative outcomes within a wait time of 4 seconds or less.

[0190] Based on the analysis, the analysis process 49404 may determine that an update to a control algorithm associated with a control wait time (e.g., an FTC control algorithm such as 49408) 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 wait time before firing must be at least 5 seconds. The updated control algorithm 49316 may be sent to the data store 49406 to update or replace the existing control algorithm 49408. The updated control algorithm 49316 may be sent to multiple operating rooms 49303 (e.g., surgical hubs 49306 in operating rooms 49303).

[0191] Autonomous update subsystem 49410 may autonomously update control algorithms as described herein as remote system 49401 continues to receive paired data 49308 from multiple operating rooms 49303. Such paired data 49308 may be received from operating rooms 49303 in a medical facility, medical facilities in a geographic location, medical facilities in a geographic region, or medical facilities in various geographic regions.

[0192] In one example, paired data 49308 may be received from a geographic region that has not previously sent paired data 49308 to the remote system 49401. In this case, the paired data 49308 from the geographic region may present a different data pattern, for example, because the surgeon / medical professional may be trained to operate the surgical device differently. For example, based on the aggregated paired data (including paired data 49308 from the new geographic region), the analysis process 49404 (e.g., in the example Fig.10 Analysis 9216b shown) may identify a new correlation between wait time and outcome data in the case of a surgical stapler. A negative outcome distribution may appear to have a mean value of 16 seconds. Based on this analysis, analysis process 49404 may determine 9204 that another update to a control algorithm (e.g., an FTC control algorithm, such as 49408) associated with controlling wait 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 wait time before firing must be at least 5 seconds and less than 16 seconds. The updated control algorithm 49316 may be sent to a data store 49406 to update or replace an existing control algorithm 49408. The updated control algorithm 49316 may be sent to a plurality of operating rooms 49303 (e.g., a surgical hub 49306 in an operating room 49303).

[0193] Fig.12 An exemplary process for autonomous updating of a surgical device control algorithm is shown. As shown, there may be multiple data protection boundaries 49322 associated with corresponding medical facilities (e.g., as described herein). The surgical hub 49306 within each 49322 may send pairing data 49308 to the remote system 49312 (e.g., as described in Figures 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 Figures 14 and 17).

[0194] Alternatively, the surgical hub 49306 within each 49322 may send paired data 49308 to the edge computing device 49502, and in response, the edge computing device 49502 may 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 more detail in U.S. patent application Ser. No. 17 / 384,151, filed on July 23, 2021, entitled “MULTI-LEVEL SURGICAL DATA ANALYSIS SYSTEM,” the disclosure of which is incorporated herein by reference in its entirety.

[0195] Similar to remote system 49312 (or remote system 49401), edge computing device 49502 may include autonomous update subsystem 49410, such as Fig.11 The edge computing device 49502 may determine that a control algorithm associated with a surgical device type (e.g., a surgical stapler) requires updating based on determining that there is a correlation between the operational data and the outcome 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 outcome, the remote system 49312 may determine that an updated control algorithm 49316 for the surgical device type is required and may generate the updated control algorithm 49316. In response to generating the updated control algorithm 49316, the edge computing device 49502 may send the updated control algorithm 49316 to the surgical hub 49306.

[0196] Unlike remote system 49312, edge computing device 49502 is located within data protection boundary 49322, and therefore, surgical hub 49306 may send pairing data 49308 to edge computing device 49502 in an unedited form. In this case, aggregation process 49402 of edge computing device 49502 may further aggregate unedited pairing data 49308 based on patient private information, and analysis process 49404 may further identify correlations between aspects of operational data and outcome data associated with surgical devices based on patient private information. For example, aggregation process 49402 of edge computing device 49502 may aggregate unedited pairing data 49308 by surgical device type and by patient age group. In one example, analysis process 49404 may perform analysis (e.g., Fig.10 Analysis 9216b in Figure 9216b) and can identify the distribution of negative and positive outcomes for each patient age group. In this way, an updated control algorithm for a surgical stapler (e.g., an updated FTC control algorithm) can include different constraints for different age groups, such as for patients 20 years old or younger, the waiting time must be at least 5 seconds, for patients between 20 and 50 years old, the waiting time must be at least 7 seconds, and for patients over 50 years old, the waiting time must be at least 10 seconds.

[0197] Fig.13 FIG. 49 is a flow chart of an exemplary process 49500 for autonomous updating of a surgical device control algorithm. The process may be performed by Fig.12 The remote system 49312 or edge computing device 49502 is executed.

[0198] At 49510, operational data associated with a surgical procedure is received. For example, first operational data associated with a first surgical procedure and second operational data associated with a second surgical procedure may be received. The first operational data may be associated with a first aspect of a control algorithm of a first surgical device. The second operational data may be associated with a first aspect of a control algorithm of a second surgical device. The first surgical device and the second surgical device may be a first surgical device type. For example, the first operational data may be received from the first surgical device, and the second operational data may be received from the second surgical device. For example, the first operational data and the second operational data may be received from a surgical hub or two different surgical hubs.

[0199] At 49512, results data associated with the surgical procedure can be received. For example, first results data associated with a first surgical procedure can be received. Second results data associated with a second surgical procedure can be received. For example, the first results data associated with the first surgical procedure can be received from a surgical hub or a surgical visualization device.

[0200] At 49514, the control algorithm is determined to be the most recent 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 most recent control algorithm associated with the first surgical device type.

[0201] At 49516, aggregate data may be generated based on the operation data and the result data. For example, the aggregate 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.

[0202] At 49518, a correlation between the control algorithm and the result data can be determined. For example, based on at least the first aggregated data, a correlation between a first aspect of a most recent control algorithm and the result data can be determined.

[0203] For example, the determined correlations may include a correlation between a first aspect of a state-of-the-art control algorithm and a negative surgical outcome, and a correlation between a first aspect of a state-of-the-art control algorithm and a positive surgical outcome.

[0204] At 49520, an updated control algorithm may be generated. For example, based on the determined correlation, an updated latest control algorithm may be generated. For example, the updated latest control algorithm may be published for download by a surgical device or surgical hub via an interface. For example, the updated latest control algorithm may be sent to multiple surgical hubs.

[0205] Autonomous adjustments to control algorithms and / or methods of a scope, a viewing instrument associated with the scope, and / or a display system associated with the scope may be used to configure device capture, device operation, and / or device display aspects, for example to maximize communication data and user control over displayed data.

[0206] Display data associated with the instrument may be autonomously adapted based on recorded use. Autonomous adjustments to the video stream and / or interpretation of a portion of the video stream may be used to adjust system notifications and warnings. System parameters may be automatically adjusted based on the movement of the detected tool (e.g., via a camera or viewing mirror). In an example, if the device is moved closer and in position to begin a use sequence (such as cutting with an energy tool), the system may automatically begin preparing the device to perform associated use tasks (such as heating the blade, performing safety and / or functional checks, etc.). In an example, if the device is moved away from the intended use site (such as near a critical structure) and / or the device is out of the field of view, the device may be placed in a safe mode or may be prevented from continued use.

[0207] Adjustments to the proximity of tissue may be made based on detected device position and / or detected device use. The method used by the device to approach tissue may be video captured. Based on the proximity, the user may be notified of different approaches. The system may automatically attempt to compensate for poor angles of attack (e.g., associated with the device).

[0208] Calibration of control algorithms may be implemented. Autonomous control algorithm calibration may be implemented so that different devices may operate to the same actual results. In one example, linear staplers may measure their corresponding speeds (e.g., cutting member advancement speed or staple firing speed) as part of a factory data set. Such speed data may be aggregated against speed data associated with other linear staplers. For example, aggregation may be performed in a remote system (such as remote system 49312) or an edge computing system (such as edge computing device 49502). An updated speed algorithm may be generated and may be pushed to the linear stapler for use in future operations. When used in a surgical procedure, the linear stapler may be able to perform a calibrated approach (e.g., using a calibrated speed algorithm) to obtain optimized results. In an example, different linear staplers may operate at different firing speeds within some manufacturing constraints. For example, by calibrating the firing algorithm using firing speed data, this manufacturing difference may be used as data aggregation / data analysis (e.g., Fig.11 49402 and 49404) to determine the optimization algorithm output of the firing speed. Other calibrations (e.g., required calibrations based on factory settings) can be skipped or not performed (e.g., after the optimization algorithm has been determined and no further calibrations are required).

[0209] Calibration of the control algorithm associated with the surgical device can be performed during surgery. The surgical device can start firing. Based at least on the results during firing, the surgical device can adjust (e.g., can adjust) the associated control algorithm during firing and / or between two firings. In an example, the surgical device can (e.g., can) detect that it has stopped working. Based at least on detecting that it has stopped working, the surgical device that has stopped working can automatically adjust its force gain (e.g., in an area where it should not stop working). In an example, the articulation mechanism may degenerate over time and may result in poor repeatability. The articulation mechanism may include an articulation mechanism associated with an end effector, which may include articulation force, articulation angle, articulation direction, and adjustments to them during articulation. The articulation mechanism is described in more detail in PCT patent application publication WO2018142277A1, 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. This degradation of the articulation mechanism may cause zero position shift. The surgical device may (eg, in response) automatically adjust accordingly. For example, the surgical device may automatically adjust based on a visual indication of the angle or force at which the articulation system is biased during trocar withdrawal.

[0210] Calibration may be performed at regular time intervals. The frequency of calibration may be determined based on the last known calibration of the system (e.g., the time associated with the last known calibration of the system). In one example, the system may (e.g., intentionally) introduce a degree of uncertainty into the automatic optimization. In an example, the system may be outside its optimal calibration time window and may still be within acceptable usage limits. Therefore, the uncertainty offset may continue to increase over time. The system may make recommendations and / or optimizations based on associated uncertainty limits. For example, when the system makes recommendations and / or optimizations, it may not allow optimization of parameters or settings that exceed its associated uncertainty limits. The system may perform checks, such as periodic checks. In one example, the system may perform a calibration check when the system is idle or not operating. In one example, the system may perform a calibration check that may be automatically performed at the beginning of a surgical procedure and / or when a surgical device is coupled to a surgical system (e.g., a robotic surgical system), the surgical device is initialized, or the surgical device is reset.

[0211] The system may perform calibration error checks. For example, the system may perform one or more checks on different areas to confirm whether the calibration was performed correctly. By evaluating the data from the different areas, the system may determine whether the calibration was actually valid or if there is another problem within the system that may have caused an invalid calibration. The different areas may refer to a group of subsystem components. For example, the lighting or sight mirror system may perform a sequence with an expected output to determine whether a calibration error is valid. In one example, different subsystems may be used to move a motor and check whether the calibration was performed correctly.

[0212] Intraoperative adjustments to surgical procedures may be implemented. Based on surgical decisions within the surgeon's control, the system may make adjustments to control algorithms and / or surgical device operations. In an example, the surgeon may hold the surgical device for a given amount of time. This amount of time may be a fraction of the recommended holding time. This amount of time may be selected to achieve optimal performance of the surgical device. Based on the available data set, the surgical device may adjust the firing speed to optimize the results of the suturing and cutting operations accordingly.

[0213] Limits for algorithm adjustments or device operation adjustments can be created to limit surgical operations, and the limits can be dynamically changed based on the progress of the surgical procedure. In an example, a surgeon can fire a linear stapler on a combination of healthy tissue and diseased tissue. Based on tissue measurements, the linear stapler can be loaded with a specific profile. When the stapler knife and slider transition from healthy tissue to diseased tissue, the limits and parameters of the surgical device can be automatically adjusted to the new profile. In an example, the surgeon may be performing a sleeve gastrectomy on a patient and may have performed an initial firing using a blue staple cartridge. In response to measuring compression time or tissue thickness, the stapler can adjust the firing algorithm based on the data set of the blue staple cartridge. For the next firing, the surgeon can switch to a different staple cartridge, so the system can automatically load a new data set about limits or other parameters within the algorithm or surgical device.

[0214] The following is a list of numbered embodiments that may or may not be claimed:

[0215] 1. A computing system for autonomous surgical device control algorithm adaptation, the computing system comprising:

[0216] A processor configured to:

[0217] receiving first operational data associated with a first surgical procedure and second operational data associated with a second surgical procedure, wherein the first operational data is associated with a first aspect of a control algorithm for a first surgical device, wherein the second operational data is associated with a first aspect of a control algorithm for a second surgical device, and wherein the first surgical device and the second surgical device are of a first surgical device type;

[0218] receiving first outcome data associated with the first surgical procedure and second outcome data associated with the second surgical procedure;

[0219] determining that each of the control algorithm of the first surgical device and the control algorithm of the second surgical device is a most recent control algorithm associated with the first surgical device type;

[0220] generating first aggregated data based at least on the first operation data, the second operation data, the first result data, and the second result data;

[0221] determining, based at least on the first aggregated data, a correlation between the first aspect of the most recent control algorithm and result data; and

[0222] Based on the determined correlations, an updated state-of-the-art control algorithm is generated.

[0223] Advantageously, the system according to Embodiment 1 can improve surgical outcomes when a suboptimal control algorithm is used in one or more surgical devices, or when the control algorithm of one or more surgical devices is not suitable for the surgical environment and context (e.g., type of surgery, surgeon preference, operating method, etc.) in which the control algorithm is used.

[0224] As a non-limiting example, the control algorithm may control one or both of the closing force (FTC) and the firing force (FTF). The first / second operating data may include one or more of the following: FTC over time, waiting time before initiating firing, FTF over time, tissue characteristics (e.g., impedance, thickness, stiffness, etc.), tissue gaps (i.e., thickness of tissue), tissue type, tissue condition, number of firings of the instrument, etc. By comparing the first operating data and the second operating data and the first result and the second result, the correlation between the data and the result can be determined, for example, a longer waiting time and / or a higher peak FTF before firing can be associated with a more positive result for a specific tissue type / thickness for a specific surgery. Therefore, the latest control algorithm generated for the update may include a longer waiting time and / or a higher peak FTF associated with a more positive result.

[0225] The operational data may include multiple variables of the manner in which the device is used, e.g., wait time before firing, FTC, FTF, tissue characteristics, etc., which enables a more accurate comparison of the operational data, e.g., a positive result may be associated with a difference between the operational data involving one or more variables.

[0226] By aggregating operational and outcome data from multiple surgical procedures performed in different settings, the system can generate control algorithm updates that improve outcomes in a manner that is not possible with systems that rely on data from a single surgical device or procedure.

[0227] As an example of an improved outcome, the outcome of tissue resection may correspond to the integrity of the seal line, wherein less seal leakage is equivalent to a more positive or improved outcome than more seal leakage. The improved outcome may also correspond to a reduced risk of complications, such as a reduced risk of instrument failure, seal line leakage, staple misfiring, etc.

[0228] 2. A computing system according to embodiment 1, wherein the first aspect of the control algorithm for each device includes at least one coefficient, at least one operating parameter, at least one limitation and / or at least one setting, and / or at least one function, subroutine, process and / or algorithm of the control algorithm.

[0229] 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.) allows the system to efficiently and quickly determine candidate updates to the control algorithm in a computationally efficient manner based on correlations between that data and outcome data. Such direct inputs can be well-involved in the generation of control algorithm updates because statistical connections between these inputs and resulting changes in surgical outcomes can be found in a non-resource-intensive manner.

[0230] Alternatively or in addition to the first aspect of the control algorithm for 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 for each device may include at least one function, subroutine, process and / or algorithm. For example, the first aspect of the control algorithm may include a power algorithm used by one / each surgical device. A "power algorithm" may be a power response of a device (e.g., an electrosurgical or ultrasonic device) to an internal state of one / each device and / or to changes in tissue conditions over time. For example, a computing system may determine a correlation between positive results and a particular power algorithm involving treating small tissue sizes with low power and treating large tissue sizes with high power, and then generate a new control algorithm that includes the same power algorithm or a similar power algorithm (e.g., a power algorithm that shares at least some characteristics / behavior with the power algorithm) for use in other surgical devices to enable these other surgical devices to treat small tissue sizes with low power and treat large tissue sizes with high power.

[0231] 3. A computing system according to embodiment 1 or embodiment 2, wherein generating the updated latest control algorithm includes replacing at least one coefficient, operating parameter, limitation 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, limitation and / or setting, and / or power algorithm associated with the updated latest control algorithm.

[0232] Replacing / updating the coefficients / parameters / limits / settings / power algorithms of a control algorithm is a particularly advantageous means of generating control algorithm updates because the new control algorithm can be generated based on the old control algorithm, i.e., using the old algorithm as a "template", rather than having to make wholesale modifications to the algorithm or building the algorithm "from scratch".

[0233] 4. The computing system of any one of embodiments 1 to 3, wherein:

[0234] the first operational data and the second operational data are received along with additional operational data associated with a plurality of additional surgical procedures, the additional operational data being associated with a first aspect of a control algorithm for a respective surgical device, each surgical device being of the first surgical device type;

[0235] The first outcome data and the second outcome data are received along with additional outcome data associated with the plurality of additional surgical procedures; and

[0236] Generating the first aggregate data is also based at least on the further operational data and the further result data.

[0237] The greater the number of surgeries and the diversity of surgeries for which data is provided to the computing system, the less impact outliers in the data will have on the determined correlations, and the greater the degree to which variables that could potentially affect surgical outcomes (e.g., surgeon skill and experience, time of day, location, patient age and health, etc.) are mitigated and controlled for in the analysis. Thus, it is advantageous to use data for multiple additional surgeries in conjunction with data from a first surgical procedure and a second surgical procedure.

[0238] 5. A computing system according to any one of embodiments 1 to 4, wherein the surgical procedure is a historical surgical procedure.

[0239] 6. A computing system according to any one of embodiments 1 to 5, wherein each of the surgical devices is mechanically identical and / or wherein the surgical devices functionally differ only in their respective control algorithms.

[0240] Using data from surgical devices that are mechanically identical (e.g., same make, model, and / or brand) and / or whose functional differences are entirely attributable to the specific control algorithms used in each device can be advantageous because doing so allows for significant simplification of data aggregation and analysis (and generation of control algorithm updates). For example, if two instruments are mechanically identical, then the values ​​of various (e.g.) forces / voltages / currents / velocities to be output by the devices can be analyzed without having to control for differences between specific construction differences of one device and another.

[0241] 7. A computing system according to any one of embodiments 1 to 6, wherein each surgical procedure is of the same type of surgical procedure.

[0242] Advantageously, using data from a single general surgical procedure type allows for the generation of more detailed and specific control algorithm updates that go beyond providing general improvements and produce control algorithms that are well suited for their specific surgical tasks.

[0243] 8. The computing system of any one of embodiments 1 to 7, wherein determining the correlation between the first aspect of the most recent control algorithm and result data comprises:

[0244] determining that the first outcome data indicates a more positive outcome of a surgical procedure or step thereof than the second outcome data; and

[0245] determining a difference in the first aspect of the control algorithm used in the first surgical procedure and the second surgical procedure; and

[0246] The difference in the first aspect of the control algorithm is associated with the more positive outcome.

[0247] Advantageously, the above-described embodiments allow improvements in surgical outcomes to be attributed to specific device settings or parameters responsible for those outcomes, such that success can be replicated in other instruments by including that setting / parameter in a control algorithm update.

[0248] For example, the results of the outcome data may correspond to the integrity of the seal line, wherein less seal leakage is equivalent to a more positive or improved outcome than more seal leakage. For another example, a more positive outcome may be an outcome without surgical complications, while a negative outcome is an outcome with one or more complications (e.g., instrument failure, seal line leakage, staple misfire, etc.).

[0249] A more positive outcome may also be associated with more positive outcomes than negative outcomes, eg, increasing the ratio of positive outcomes.

[0250] 9. The computing system of embodiment 8, wherein generating the updated state-of-the-art control algorithm comprises: using coefficients, operating parameters, limitations, and

[0251] and / or settings to replace at least one coefficient, operating parameter, limit, and / or setting.

[0252] 10. A computing system according to any one of embodiments 1 to 9, wherein generating the updated latest control algorithm includes: attributing the difference in outcome data between at least the first surgical procedure and the second surgical procedure to the difference in the first aspect of the control algorithm used in the procedures.

[0253] 11. A computing system according to any one of embodiments 1 to 10, wherein the computing system is one of an edge computing system or a cloud computing system.

[0254] 12. A 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.

[0255] Receiving operational data directly from the device can improve the processing speed of the system as a whole and reduce the delay in generating (and optionally distributing) control algorithm updates. In addition, this can eliminate the need for additional storage devices and / or the need for any (e.g.) compression / truncation / rounding of operational data, which may otherwise result in a loss of fidelity.

[0256] 13. A computing system according to any one of embodiments 1 to 12, wherein the first operational data and the second operational data are received from a surgical hub or two different surgical hubs.

[0257] Receiving all operational data from the same hub results in a more cohesive system with lower latency and reduced network requirements. Receiving data from two different hubs allows data from geographically diverse surgical settings to be used in the analysis to draw new conclusions that are not apparent from the data of a single surgical setting alone.

[0258] 14. A computing system according to any one of embodiments 1 to 13, wherein the first result data associated with the first surgical procedure is received from a surgical hub or a surgical visualization device.

[0259] 15. A computing system according to any one of embodiments 1 to 14, wherein the processor is further configured to be able to publish the updated latest control algorithm for download by the surgical device or surgical hub via an interface.

[0260] Making the updated state-of-the-art control algorithm available for download allows it to be deployed on a wide range of devices in potentially geographically diverse surgical environments, so that the benefits associated with the improved control algorithm can be more readily obtained in these other environments (which might otherwise have to obtain the new control algorithm via slower alternative means).

[0261] 16. A computing system according to any one of embodiments 1 to 15, wherein the processor is further configured to be able to send the updated latest control algorithm to multiple surgical hubs and / or multiple surgical devices.

[0262] Sending the updated control algorithm to the hub and / or device allows it to be deployed on a wide range of devices in potentially geographically diverse surgical environments, so that the benefits associated with the improved control algorithm can be more easily obtained in these other environments (which might otherwise have to obtain the new control algorithm via slower alternative means).

[0263] 17. A computing system according to any one of embodiments 1 to 16, wherein the determined correlations include correlations between the first aspect of the latest control algorithm and negative surgical outcomes, and correlations between the first aspect of the latest control algorithm and positive surgical outcomes.

[0264] For example, a surgical outcome may correspond to the integrity of the seal line, with a seal with less leakage equating to a positive or improved outcome, and a seal with more leakage equating to a negative or reduced outcome (relative to the other). A threshold may be set to classify a positive or negative outcome, which for this example may be a threshold leakage. As another example, a more positive outcome may be an outcome with no surgical complications, while a negative outcome may be an outcome with one or more complications (e.g., instrument failure, seal line leakage, staple misfiring, etc.).

[0265] 18. A 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.

[0266] 19. An intelligent surgical device, comprising:

[0267] A processor configured to:

[0268] receiving an updated control algorithm associated with the intelligent surgical device from a surgical hub;

[0269] determining that the updated control algorithm is more current than a control algorithm installed on the intelligent surgical device;

[0270] replacing the control algorithm with the updated control algorithm; and operating using the updated control algorithm.

[0271] 20. An intelligent 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.

[0272] 21. A method for autonomous surgical device control algorithm adaptation, the method comprising:

[0273] receiving first operational data associated with a first surgical procedure and second operational data associated with a second surgical procedure, wherein the first operational data is associated with a first aspect of a control algorithm for a first surgical device, wherein the second operational data is associated with a first aspect of a control algorithm for a second surgical device, and wherein the first surgical device and the second surgical device are of a first surgical device type;

[0274] receiving first outcome data associated with the first surgical procedure and second outcome data associated with the second surgical procedure;

[0275] determining that each of the control algorithm of the first surgical device and the control algorithm of the second surgical device is a most recent control algorithm associated with the first surgical device type;

[0276] generating first aggregated data based at least on the first operation data, the second operation data, the first result data, and the second result data;

[0277] determining, based at least on the first aggregated data, a correlation between the first aspect of the most recent control algorithm and result data; and

[0278] Based on the determined correlations, an updated state-of-the-art control algorithm is generated.

[0279] Advantageously, the method according to embodiment 21 can improve surgical outcomes in situations where a suboptimal control algorithm is used in one or more surgical devices, or the control algorithm of one or more surgical devices is not suitable for the surgical environment and context in which the control algorithm is used (e.g., type of surgery, surgeon preference, operating method, etc.).

[0280] As a non-limiting example, the control algorithm may control one or both of the closing force (FTC) and the firing force (FTF). The first / second operating data may include one or more of the following: FTC over time, waiting time before initiating firing, FTF over time, tissue characteristics (e.g., impedance, thickness, stiffness, etc.), tissue gaps (i.e., thickness of tissue), tissue type, tissue condition, number of firings of the instrument, etc. By comparing the first operating data and the second operating data and the first result and the second result, the correlation between the data and the result can be determined, for example, a longer waiting time and / or a higher peak FTF before firing can be associated with a more positive result for a specific tissue type / thickness for a specific surgery. Therefore, the latest control algorithm generated for the update may include a longer waiting time and / or a higher peak FTF associated with a more positive result.

[0281] The operational data may include multiple variables of the manner in which the device is used, e.g., wait time before firing, FTC, FTF, tissue characteristics, etc., which enables a more accurate comparison of the operational data, e.g., a positive result may be associated with a difference between the operational data involving one or more variables.

[0282] By aggregating operational and outcome data from multiple surgical procedures performed in different settings, the approach can generate control algorithm updates that improve outcomes in a manner not possible with approaches that rely on data from a single surgical device or procedure.

[0283] As an example of an improved outcome, the outcome of tissue resection may correspond to the integrity of the seal line, wherein less seal leakage is equivalent to a more positive or improved outcome than more seal leakage. The improved outcome may also correspond to a reduced risk of complications, such as a reduced risk of instrument failure, seal line leakage, staple misfiring, etc.

[0284] 22. A method according to embodiment 21, wherein the first aspect of the control algorithm for each device includes at least one coefficient, at least one operating parameter, at least one limitation and / or at least one setting, and / or at least one function, subroutine, process and / or algorithm of the control algorithm.

[0285] 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.) allows the system to efficiently and quickly determine candidate updates to the control algorithm in a computationally efficient manner based on correlations between that data and outcome data. Such direct inputs can be well-involved in the generation of control algorithm updates because statistical connections between these inputs and resulting changes in surgical outcomes can be found in a non-resource-intensive manner.

[0286] Alternatively or in addition to the first aspect of the control algorithm for 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 for each device may include at least one function, subroutine, process and / or algorithm. For example, the first aspect of the control algorithm may include a power algorithm used by one / each surgical device. A "power algorithm" may be a power response of a device (e.g., an electrosurgical or ultrasonic device) to an internal state of one / each device and / or to changes in tissue conditions over time. For example, a computing system may determine a correlation between positive results and a particular power algorithm involving treating small tissue sizes with low power and treating large tissue sizes with high power, and then generate a new control algorithm that includes the same power algorithm or a similar power algorithm (e.g., a power algorithm that shares at least some characteristics / behavior with the power algorithm) for use in other surgical devices to enable these other surgical devices to treat small tissue sizes with low power and treat large tissue sizes with high power.

[0287] 23. A method according to embodiment 21 or embodiment 22, wherein generating the updated latest control algorithm includes replacing at least one coefficient, operating parameter, limitation 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, limitation and / or setting, and / or power algorithm associated with the updated latest control algorithm.

[0288] Replacing / updating the coefficients / parameters / limits / settings / power algorithms of a control algorithm is a particularly advantageous means of generating control algorithm updates because the new control algorithm can be generated based on the old control algorithm, i.e., using the old algorithm as a "template", rather than having to make wholesale modifications to the algorithm or building the algorithm "from scratch".

[0289] 24. The method according to any one of embodiments 21 to 23, wherein:

[0290] the first operational data and the second operational data are received along with additional operational data associated with a plurality of additional surgical procedures, the additional operational data being associated with a first aspect of a control algorithm for a respective surgical device, each surgical device being of the first surgical device type;

[0291] The first outcome data and the second outcome data are received along with additional outcome data associated with the plurality of additional surgical procedures; and

[0292] Generating the first aggregate data is also based at least on the further operational data and the further result data.

[0293] The greater the number of surgeries for which data is provided and the greater the diversity of the surgeries, the less impact outliers in the data will have on the determined correlations, and the greater the degree to which variables that could potentially affect surgical outcomes (e.g., surgeon skill and experience, time of day, location, patient age and health, etc.) are mitigated and controlled for in the analysis. Thus, it is advantageous to use data for multiple additional surgeries in conjunction with data from the first and second surgeries.

[0294] 25. A method according to any one of embodiments 21 to 24, wherein the surgical procedure is a historical surgical procedure.

[0295] 26. A method according to any one of embodiments 21 to 25, wherein each of the surgical devices is mechanically identical and / or wherein the surgical devices functionally differ only in their respective control algorithms.

[0296] Using data from surgical devices that are mechanically identical (e.g., same make, model, and / or brand) and / or whose functional differences are entirely attributable to the specific control algorithms used in each device can be advantageous because doing so allows for significant simplification of data aggregation and analysis (and generation of control algorithm updates). For example, if two instruments are mechanically identical, then the values ​​of various (e.g.) forces / voltages / currents / velocities to be output by the devices can be analyzed without having to control for differences between specific construction differences of one device and another.

[0297] 27. A method according to any one of embodiments 21 to 26, wherein each surgical procedure is the same type of surgical procedure.

[0298] Advantageously, using data from a single general surgical procedure type allows for the generation of more detailed and specific control algorithm updates that go beyond providing general improvements and produce control algorithms that are well suited for their specific surgical tasks.

[0299] 28. The method of any one of embodiments 21 to 27, wherein determining the correlation between the first aspect of the most recent control algorithm and result data comprises:

[0300] determining that the first outcome data indicates a more positive outcome of a surgical procedure or step thereof than the second outcome data; and

[0301] determining a difference in the first aspect of the control algorithm used in the first surgical procedure and the second surgical procedure; and

[0302] The difference in the first aspect of the control algorithm is associated with the more positive outcome.

[0303] Advantageously, the above-described embodiments allow improvements in surgical outcomes to be attributed to specific device settings or parameters responsible for those outcomes, such that success can be replicated in other instruments by including that setting / parameter in a control algorithm update.

[0304] For example, the results of the outcome data may correspond to the integrity of the seal line, wherein less seal leakage is equivalent to a more positive or improved outcome than more seal leakage. For another example, a more positive outcome may be an outcome without surgical complications, while a negative outcome is an outcome with one or more complications (e.g., instrument failure, seal line leakage, staple misfire, etc.).

[0305] A more positive outcome may also be associated with more positive outcomes than negative outcomes, eg, increasing the ratio of positive outcomes.

[0306] 29. A method according to embodiment 28, wherein generating the updated latest control algorithm includes: replacing at least one coefficient, operating parameter, limitation and / or setting with a coefficient, operating parameter, limitation and / or setting associated with the control algorithm used in at least one surgical procedure that produced the positive result.

[0307] 30. A method according to any one of embodiments 21 to 29, wherein generating the updated latest control algorithm includes attributing the difference in outcome data between at least the first surgical procedure and the second surgical procedure to the difference in the first aspect of the control algorithm used in the procedures.

[0308] 31. A method according to any one of embodiments 21 to 30, wherein the computing system is one of an edge computing system or a cloud computing system.

[0309] 32. A method according to any one of embodiments 21 to 31, wherein the first operation data is received from the first surgical device and the second operation data is received from the second surgical device.

[0310] Receiving operational data directly from the device can improve the processing speed of the system as a whole and reduce the delay in generating (and optionally distributing) control algorithm updates. In addition, this can eliminate the need for additional storage devices and / or the need for any (e.g.) compression / truncation / rounding of operational data, which may otherwise result in a loss of fidelity.

[0311] 33. A method according to any one of embodiments 21 to 32, wherein the first operation data and the second operation data are received from a surgical hub or two different surgical hubs.

[0312] Receiving all operational data from the same hub results in greater cohesion, lower latency, and reduced network requirements. Receiving data from two different hubs allows data from geographically diverse surgical settings to be used in the analysis to draw new conclusions that are not apparent from data from a single surgical setting alone.

[0313] 34. A method according to any one of embodiments 21 to 33, wherein the first result data associated with the first surgical procedure is received from a surgical hub or a surgical visualization device.

[0314] 35. A method according to any one of embodiments 21 to 34, wherein the processor is further configured to be able to publish the updated latest control algorithm for download by the surgical device or surgical hub via an interface.

[0315] Making the updated state-of-the-art control algorithm available for download allows it to be deployed on a wide range of devices in potentially geographically diverse surgical environments, so that the benefits associated with the improved control algorithm can be more readily obtained in these other environments (which might otherwise have to obtain the new control algorithm via slower alternative means).

[0316] 36. A method according to any one of embodiments 21 to 35, wherein the processor is further configured to be able to send the updated latest control algorithm to multiple surgical hubs and / or multiple surgical devices.

[0317] Sending the updated control algorithm to the hub and / or device allows it to be deployed on a wide range of devices in potentially geographically diverse surgical environments, so that the benefits associated with the improved control algorithm can be more easily obtained in these other environments (which might otherwise have to obtain the new control algorithm via slower alternative means).

[0318] 37. A method according to any one of embodiments 21 to 36, wherein the determined correlations include correlations between the first aspect of the latest control algorithm and negative surgical outcomes, and correlations between the first aspect of the latest control algorithm and positive surgical outcomes.

[0319] For example, a surgical outcome may correspond to the integrity of the seal line, with a seal with less leakage equating to a positive or improved outcome, and a seal with more leakage equating to a negative or reduced outcome (relative to the other). A threshold may be set to classify a positive or negative outcome, which for this example may be a threshold leakage. As another example, a more positive outcome may be an outcome with no surgical complications, while a negative outcome may be an outcome with one or more complications (e.g., instrument failure, seal line leakage, staple misfiring, etc.).

[0320] 38. A method according to any one of embodiments 21 to 37, wherein the result data includes the first result data and the second result data.

[0321] 39. A computing system according to any one of embodiments 1 to 18 or a method according to any one of embodiments 21 to 38, wherein both the first surgical device and the second surgical device are surgical endoscopic cutting and sealing devices or stapling devices, and wherein the first operating data and the second operating data are related to one or more of: controlling energy source, cutting, suturing, knob orientation, body orientation, body position, anvil jaw force, and reloading alignment slot management.

[0322] 40. A computer program product for causing a computer to execute the method according to any one of embodiments 21 to 39.

[0323] 41. A non-transitory computer-readable storage medium, comprising computer-readable instructions, which, when executed by a computer, cause the computer to perform a method according to any one of embodiments 21 to 39.

[0324] The following are numbered aspects of the disclosure that may or may not be claimed:

[0325] 1. A computing system for autonomous surgical device control algorithm adaptation, the computing system comprising:

[0326] A processor configured to:

[0327] receiving first operational data associated with a first surgical procedure and second operational data associated with a second surgical procedure, wherein the first operational data is associated with a first aspect of a control algorithm for a first surgical device, wherein the second operational data is associated with a first aspect of a control algorithm for a second surgical device, and wherein the first surgical device and the second surgical device are of a first surgical device type;

[0328] receiving first outcome data associated with the first surgical procedure and second outcome data associated with the second surgical procedure;

[0329] determining that each of the control algorithm of the first surgical device and the control algorithm of the second surgical device is a most recent control algorithm associated with the first surgical device type;

[0330] generating first aggregated data based at least on the first operation data, the second operation data, the first result data, and the second result data;

[0331] determining, based at least on the first aggregated data, a correlation between the first aspect of the most recent control algorithm and result data; and

[0332] Based on the determined correlations, an updated state-of-the-art control algorithm is generated.

[0333] 2. The computing system according to aspect 1, wherein the computing system is one of an edge computing system or a cloud computing system.

[0334] 3. The computing system of aspect 1, wherein the first operating data is received from the first surgical device and the second operating data is received from the second surgical device.

[0335] 4. A computing system according to aspect 1, wherein the first operational data and the second operational data are received from a surgical hub or two different surgical hubs.

[0336] 5. The computing system of aspect 1, wherein the surgical hub or the surgical visualization

[0337] The device receives the first result data associated with the first surgical procedure.

[0338] 6. A computing system according to aspect 1, wherein the processor is further configured to publish the updated latest control algorithm for downloading by a surgical device or a surgical hub via an interface.

[0339] 7. A computing system according to aspect 1, wherein the processor is further configured to be able to send the updated latest control algorithm to multiple surgical hubs.

[0340] 8. The computing system of aspect 1, wherein the determined correlations include correlations between the first aspect of the state-of-the-art control algorithm and negative surgical outcomes, and correlations between the first aspect of the state-of-the-art control algorithm and positive surgical outcomes.

[0341] 9. The computing system of aspect 1, wherein the result data comprises the first result data and the second result data.

[0342] 10. An intelligent surgical device, comprising:

[0343] A processor configured to:

[0344] receiving an updated control algorithm associated with the intelligent surgical device from a surgical hub;

[0345] determining that the updated control algorithm is more current than a control algorithm installed on the intelligent surgical device;

[0346] replacing the control algorithm with the updated control algorithm; and

[0347] Operates using the updated control algorithm.

[0348] 11. The intelligent surgical device of aspect 10, wherein replacing the control algorithm with the updated control algorithm comprises replacing coefficients associated with the control algorithm with updated coefficients associated with the updated control algorithm.

[0349] 12. A method for autonomous surgical device control algorithm adaptation, the method comprising:

[0350] receiving first operational data associated with a first surgical procedure and second operational data associated with a second surgical procedure, wherein the first operational data is associated with a first aspect of a control algorithm for a first surgical device, wherein the second operational data is associated with a first aspect of a control algorithm for a second surgical device, and wherein the first surgical device and the second surgical device are of a first surgical device type;

[0351] receiving first outcome data associated with the first surgical procedure and second outcome data associated with the second surgical procedure;

[0352] determining that each of the control algorithm of the first surgical device and the control algorithm of the second surgical device is a most recent control algorithm associated with the first surgical device type;

[0353] generating first aggregated data based at least on the first operation data, the second operation data, the first result data, and the second result data;

[0354] determining, based at least on the first aggregated data, a correlation between the first aspect of the most recent control algorithm and result data; and

[0355] Based on the determined correlations, an updated state-of-the-art control algorithm is generated.

[0356] 13. The method of clause 12, wherein the computing system is one of an edge computing system or a cloud computing system.

[0357] 14. The method of aspect 12, wherein the first operating data is received from the first surgical device and the second operating data is received from the second surgical device.

[0358] 15. A method according to aspect 12, wherein the first operation data and the second operation data are received from a surgical hub or two different surgical hubs.

[0359] 16. The method of aspect 12, wherein the first result data associated with the first surgical procedure is received from a surgical hub or a surgical visualization device.

[0360] 17. The method according to aspect 12, wherein the processor is further configured to publish the updated latest control algorithm for downloading by the surgical device or surgical hub via an interface.

[0361] 18. A method according to aspect 12, wherein the processor is further configured to be able to send the updated latest control algorithm to multiple surgical hubs.

[0362] 19. The method of aspect 12, wherein the determined correlations include correlations between the first aspect of the latest control algorithm and negative surgical outcomes, and correlations between the first aspect of the latest control algorithm and positive surgical outcomes.

[0363] 20. The method of aspect 12, wherein the result data comprises the first result data and the second result data.

Claims

1. A computing system for autonomous surgical device control algorithm adaptation, the computing system comprising: A processor configured to: receiving first operational data associated with a first surgical procedure and second operational data associated with a second surgical procedure, wherein the first operational data is associated with a first aspect of a control algorithm for a first surgical device, wherein the second operational data is associated with a first aspect of a control algorithm for a second surgical device, and wherein the first surgical device and the second surgical device are of a first surgical device type; receiving first outcome data associated with the first surgical procedure and second outcome 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 a most recent control algorithm associated with the first surgical device type; generating first aggregated data based at least on the first operation data, the second operation data, the first result data, and the second result data; determining, based at least on the first aggregated data, a correlation between the first aspect of the most recent control algorithm and result data; and Based on the determined correlations, an updated state-of-the-art control algorithm is generated.

2. The computing system of claim 1, wherein: The first aspect of the control algorithm for each device comprises at least one coefficient, 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 of the control algorithm.

3. A computing system according to claim 1 or claim 2, wherein: 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.

4. A computing system according to any one of claims 1 to 3, wherein: the first operational data and the second operational data are received along with additional operational data associated with a plurality of additional surgical procedures, the additional operational data being associated with a first aspect of a control algorithm for a respective surgical device, each surgical device being of the first surgical device type; The first outcome data and the second outcome data are received along with additional outcome data associated with the plurality of additional surgical procedures; and Generating the first aggregate data is also based at least on the further operational data and the further result data.

5. The computing system according to any one of claims 1 to 4, wherein: The surgical procedure described is a historical surgical procedure.

6. The computing system according to any one of claims 1 to 5, wherein: Each of the surgical devices is mechanically identical and / or wherein the surgical devices functionally differ only in their respective control algorithms.

7. The computing system according to any one of claims 1 to 6, wherein: Each surgical procedure was the same type of surgery.

8. The computing system according to any one of claims 1 to 7, wherein: Determining the correlation between the first aspect of the most recent control algorithm and result data comprises: determining that the first outcome data indicates a more positive outcome of a surgical procedure or step thereof than the second outcome data; and determining a difference in the first aspect of the control algorithm used in the first surgical procedure and the second surgical procedure; and The difference in the first aspect of the control algorithm is associated with the more positive outcome.

9. The computing system of claim 8, wherein: Generating the updated state-of-the-art control algorithm includes replacing at least one coefficient, operating parameter, limitation and / or setting with a coefficient, operating parameter, limitation and / or setting associated with the control algorithm used in at least one surgical procedure that produced the positive result.

10. The computing system according to any one of claims 1 to 9, wherein: Generating the updated state-of-the-art control algorithm includes attributing differences in outcome data between at least the first surgical procedure and the second surgical procedure to differences in the first aspect of the control algorithm used in the procedures.

11. The computing system according to any one of claims 1 to 10, wherein: The computing system is one of an edge computing system or a cloud computing system.

12. The computing system according to any one of claims 1 to 11, wherein: The first operating data is received from the first surgical device, and the second operating data is received from the second surgical device.

13. The computing system according to any one of claims 1 to 12, wherein: The first operational data and the second operational data are received from a surgical hub or two different surgical hubs.

14. The computing system according to any one of claims 1 to 13, wherein: The first result data associated with the first surgical procedure is received from a surgical hub or a surgical visualization device.

15. The computing system of any one of claims 1 to 14, wherein: The processor is further configured to publish the updated latest control algorithm for downloading by the surgical device or surgical hub via an interface.

16. The computing system of any one of claims 1 to 15, wherein: The processor is further configured to send the updated latest control algorithm to multiple surgical hubs and / or multiple surgical devices.

17. The computing system of any one of claims 1 to 16, wherein: The determined correlations include a correlation between the first aspect of the state-of-the-art control algorithm and a negative surgical outcome, and a correlation between the first aspect of the state-of-the-art control algorithm and a positive surgical outcome.

18. The computing system of any one of claims 1 to 17, wherein: The result data includes the first result data and the second result data.

19. An intelligent surgical device, comprising: A processor configured to: receiving an updated control algorithm associated with the intelligent surgical device from a surgical hub; determining that the updated control algorithm is more current than a control algorithm installed on the intelligent surgical device; replacing the control algorithm with the updated control algorithm; and Operates using the updated control algorithm.

20. The intelligent surgical device according to claim 19, 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.

21. A method for autonomous surgical device control algorithm adaptation, the method comprising: receiving first operational data associated with a first surgical procedure and second operational data associated with a second surgical procedure, wherein the first operational data is associated with a first aspect of a control algorithm for a first surgical device, wherein the second operational data is associated with a first aspect of a control algorithm for a second surgical device, and wherein the first surgical device and the second surgical device are of a first surgical device type; receiving first outcome data associated with the first surgical procedure and second outcome 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 a most recent control algorithm associated with the first surgical device type; generating first aggregated data based at least on the first operation data, the second operation data, the first result data, and the second result data; determining, based at least on the first aggregated data, a correlation between the first aspect of the most recent control algorithm and result data; and Based on the determined correlations, an updated state-of-the-art control algorithm is generated.

22. The method according to claim 21, wherein: The first aspect of the control algorithm for each device comprises at least one coefficient, 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 of the control algorithm.

23. The method of claim 21 or claim 22, wherein: 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.

24. A method according to any one of claims 21 to 23, wherein: the first operational data and the second operational data are received along with additional operational data associated with a plurality of additional surgical procedures, the additional operational data being associated with a first aspect of a control algorithm for a respective surgical device, each surgical device being of the first surgical device type; The first outcome data and the second outcome data are received along with additional outcome data associated with the plurality of additional surgical procedures; and Generating the first aggregate data is also based at least on the further operational data and the further result data.

25. The method according to any one of claims 21 to 24, wherein: The surgical procedure described is a historical surgical procedure.

26. The method according to any one of claims 21 to 25, wherein: Each of the surgical devices is mechanically identical and / or wherein the surgical devices functionally differ only in their respective control algorithms.

27. The method according to any one of claims 21 to 26, wherein: Each surgical procedure was the same type of surgery.

28. The method according to any one of claims 21 to 27, wherein: Determining the correlation between the first aspect of the most recent control algorithm and result data comprises: determining that the first outcome data indicates a more positive outcome of a surgical procedure or step thereof than the second outcome data; and determining a difference in the first aspect of the control algorithm used in the first surgical procedure and the second surgical procedure; and The difference in the first aspect of the control algorithm is associated with the more positive outcome.

29. The method according to claim 28, wherein: Generating the updated state-of-the-art control algorithm includes replacing at least one coefficient, operating parameter, limitation and / or setting with a coefficient, operating parameter, limitation and / or setting associated with the control algorithm used in at least one surgical procedure that produced the positive result.

30. The method according to any one of claims 21 to 29, wherein: Generating the updated state-of-the-art control algorithm includes attributing differences in outcome data between at least the first surgical procedure and the second surgical procedure to differences in the first aspect of the control algorithm used in the procedures.

31. The method according to any one of claims 21 to 30, wherein: The computing system is one of an edge computing system or a cloud computing system.

32. The method according to any one of claims 21 to 31, wherein: The first operating data is received from the first surgical device, and the second operating data is received from the second surgical device.

33. The method according to any one of claims 21 to 32, wherein: The first operational data and the second operational data are received from a surgical hub or two different surgical hubs.

34. The method according to any one of claims 21 to 33, wherein: The first result data associated with the first surgical procedure is received from a surgical hub or a surgical visualization device.

35. The method according to any one of claims 21 to 34, wherein: The processor is further configured to publish the updated latest control algorithm for downloading by the surgical device or surgical hub via an interface.

36. The method according to any one of claims 21 to 35, wherein: The processor is further configured to send the updated latest control algorithm to multiple surgical hubs and / or multiple surgical devices.

37. The method according to any one of claims 21 to 36, wherein: The determined correlations include a correlation between the first aspect of the state-of-the-art control algorithm and a negative surgical outcome, and a correlation between the first aspect of the state-of-the-art control algorithm and a positive surgical outcome.

38. The method according to any one of claims 21 to 37, wherein: The result data includes the first result data and the second result data.

39. The computing system of any one of claims 1 to 18 or the method of any one of claims 21 to 38, wherein: Both the first surgical device and the second surgical device are surgical endoscopic cutting and sealing devices or stapling devices, and wherein the first operating data and the second operating data are related to one or more of: controlling energy source, cutting, suturing, knob orientation, body orientation, body position, anvil jaw force, and reloading alignment slot management.

40. A computer program product for causing a computer to execute the method according to any one of claims 21 to 39.

41. A non-transitory computer-readable storage medium comprising computer-readable instructions which, when executed by a computer, cause the computer to perform the method of any one of claims 21 to 39.

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