System and method for identifying events during powered stapling

Abstract

A powered surgical stapler includes an end effector including a first jaw having a stapler cartridge storing a plurality of staples, a sled disposed in the stapler cartridge and movable to eject the plurality of staples, and a second jaw having an anvil configured to deform the plurality of staples ejected by the sled. The powered surgical stapler also includes a drive beam coupled to the end effector and configured to move the sled through the stapler cartridge, and a motor coupled to the drive beam and configured to move the drive beam. The powered surgical stapler further includes a sensor configured to measure a property of the motor. The powered surgical stapler additionally includes a controller configured to generate measured signal data based on the property of the motor and time, process the measured signal data to generate processed data, identify a pattern in the processed data, and control the motor in response to the pattern in the processed data.

Description

SYSTEM AND METHOD FOR IDENTIFYING EVENTS DURING POWERED STAPLINGCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Provisional Patent Application Serial No. 63 / 519,577, filed August 15, 2023 , the entire content of which is incorporated herein by reference.BACKGROUND1. Technical Field

[0002] The present disclosure relates to surgical devices. More specifically, the present disclosure relates to electromechanical surgical systems for performing stapling surgical procedures.2. Background of Related Art

[0003] Surgical fastener devices for applying fasteners or staples to tissue include surgical staplers, which may be manual or motor-powered. There are multiple types of powered surgical staplers, such as linear or circular staplers, which are specifically designed to perform certain types of surgical procedures, including endoscopic procedures that provide a real-time video of a surgical site through a laparoscopic or endoscopic camera.

[0004] When staples are ejected by powered surgical staplers, there is a perceptible twitch at the end of the firing, which is undesirable. At the end of the firing, the sled or stapler pusher ejecting the staples comes to an abrupt halt on the mechanical limit. The impact of the sled with the mechanical limit causes the twitch. Adverse events (e.g., missing or defective components, or staple malformation) may also occur during firing. There is a need to monitor operation of the powered surgical stapler to minimize twitch when the sled reaches the mechanical limit and to determine the occurrence of adverse events and to compensate for their occurrence.SUMMARY

[0005] The present disclosure provides a powered surgical stapler configured to analyze time series data collected during staple ejection, such as motor current, which contains featuresor markers that are indicative of the sled approaching an end stop. Such features can be detected using wavelets and the sled can be brought to a gentle stop before impacting the mechanical limit. In addition, certain missing components, e.g., sled, may generate features or markers in time series data such as motor current. Such features may also be identified using wavelets. Once identified, stapler operation can be halted upon identification of such features to eliminate or minimize hazards.

[0006] The wavelets analysis may be used to confirm proper staple formation by identifying features or markers in time series data such as motor current. This can be used to make a prediction of which staples may have fallen outside the optimal range. If so, the stapler may output an indication that a visual examination of the staple line is required. Qualitative assessment of the tissue using the disclosed wavelets processing may further be used for optimizing firing parameters. Tissue compression generates features or markers in time series data such as motor current. These features may be also analyzed using machine learning algorithms to optimize firing parameters such as speed, force, and tissue relaxation time.

[0007] According to one embodiment of the present disclosure, a powered surgical stapler is disclosed. The powered surgical stapler includes an end effector including a first jaw having a stapler cartridge storing a plurality of staples, a sled disposed in the stapler cartridge and movable to eject the plurality of staples, and a second jaw having an anvil configured to deform the plurality of staples ejected by the sled. The powered surgical stapler also includes a drive beam coupled to the end effector and configured to move the sled through the stapler cartridge, and a motor coupled to the drive beam and configured to move the drive beam. The powered surgical stapler further includes a sensor configured to measure an operational property of the motor. The powered surgical stapler additionally includes a controller configured to: generate measured signal data based on the property of the motor and time; process the measured signal data to generate processed data; identify a pattern in the processed data; and control the motor in response to the identified pattern in the processed data.

[0008] Implementations of the above embodiment may include one or more of the following features. According to one aspect of the above embodiment, the property of the motor may be one of current draw, force, or torque. The measured signal data may be a plot of the property over time. The controller may be further configured to identify a region of interest in the plot. The controller may be also configured to generate template signal data based on theregion of interest. The controller may be additionally configured to correlate the template signal data to the measured signal data to generate correlated signal data. The controller may be also configured to identify a pattern in the correlated signal data corresponding to an approaching end stop for the sled. The controller may be further configured to identify a pattern in the correlated signal data corresponding to the sled being missing from the stapler cartridge. The controller may be also configured to generate wavelet signal data using a wavelet transform function on the plot. The controller may be additionally configured to identify a pattern in the wavelet signal data corresponding to an approaching end stop for the sled. The controller may be also configured to identify a pattern in the wavelet signal data corresponding to improper staple formation of at least one staple of the plurality of staples. The controller may be further configured to output a prompt informing of the improper staple formation. The powered surgical stapler may include a display screen configured to display the prompt.

[0009] According to another embodiment of the present disclosure, a method for controlling a powered surgical stapler is disclosed. The method includes activating a motor to advance a drive beam coupled to an end effector. The end effector includes a first jaw having a stapler cartridge storing a plurality of staples, a sled disposed in the stapler cartridge and movable by the drive beam to eject the plurality of staples, and a second jaw having an anvil configured to deform the plurality of staples ejected by the sled. The method also includes measuring, at a sensor, at least one property of the motor and generating, at a controller, measured signal data based on the at least one property of the motor and time. The method further includes: processing, at the controller, the measured signal data to generate processed data; identifying, at the controller, a pattern in the processed data; and controlling, by the controller, the motor in response to the pattern in the processed data.

[0010] Implementations of the above embodiment may include one or more of the following features. According to one aspect of the above embodiment, processing the measured signal data may further include: identifying a region of interest in the measured signal data; generating template signal data based on the region of interest; and correlating the template signal data to the measured signal data to generate correlated signal data. Identifying the pattern may further include identifying a pattern in the correlated signal data corresponding to at least one of an approaching end stop for the sled or the sled being missing from the stapler cartridge. Processing the measured signal data may further include generating wavelet signal data using awavelet transform function on the measured signal data. Identifying the pattern may further include identifying a pattern in the wavelet signal data corresponding to an approaching end stop for the sled. Identifying the pattern may further include identifying a pattern in the wavelet signal data corresponding to improper staple formation of at least one staple of the plurality of staples. The method may additionally include outputting a prompt informing of the improper staple formation on a display screen.BRIEF DESCRIPTION OF THE DRAWINGS

[0011] Embodiments of the present disclosure are described herein with reference to the accompanying drawings, wherein:

[0012] FIG. 1 is a perspective view of a powered linear stapler including a handle assembly, an adapter assembly, and an end effector, according to an embodiment of the present disclosure;

[0013] FIG. 2 is a perspective view of a powered circular stapler including a handle assembly, an adapter assembly, and an end effector, according to an embodiment of the present disclosure;

[0014] FIG. 3 is a schematic diagram of the handle assembly, the adapter assembly, and the end effector of FIG. 1 ;

[0015] FIG. 4 is a perspective, exploded view of a loading unit of FIG. 1, according to an embodiment of the present disclosure;

[0016] FIG. 5 is a flowchart showing a method for determining events from a measured signal according to an embodiment of the present disclosure;

[0017] FIG. 6 is a measured signal plot of current draw over time generated by a controller based on current draw sensor according to an embodiment of the present disclosure;

[0018] FIG. 7A is a region of interest (ROI) of the plot of FIG. 6 extracted and normalized by the controller;

[0019] FIG. 7B is the normalized and smoothed ROI of the plot of FIG. 6;

[0020] FIG. 7C is a flipped, normalized, smoothed ROI of the plot FIG. 6;

[0021] FIG. 8 shows the measured signal plot of FIG. 6 and a template signal plot generated by the controller based on the processed ROI of FIGS. 7A-C including a pattern corresponding to an approaching end stop;

[0022] FIG. 9 shows the measured signal plot of FIG. 6 and a template signal plot generated by the controller based on the processed ROI of FIGS. 7A-C including a pattern corresponding to normal sled advancement;

[0023] FIG. 10 shows the measured signal plot of FIG. 6 and a template signal plot generated by the controller based on the processed ROI of FIGS. 7A-C including a pattern corresponding to a missing staple ejecting sled;

[0024] FIG. 11 shows the measured signal plot of FIG. 6 and a processed plot using discrete wavelet transform according to an embodiment of the present disclosure;

[0025] FIG. 12 shows the measured signal plot of FIG. 6 and processed plots using continuous wavelet transform according to an embodiment of the present disclosure;

[0026] FIG. 13 shows the measured signal plot of FIG. 6, a wavelet processed plot, and a moving average filtered plot according to an embodiment of the present disclosure;

[0027] FIG. 14 shows the wavelet processed plot including outlier peaks and valleys for determining proper staple formation according to an embodiment of the present disclosure; and

[0028] FIG. 15 is a flowchart showing a method for determining events from a measured signal according to another embodiment of the present disclosure.DETAILED DESCRIPTION OF EMBODIMENTS

[0029] Embodiments of the presently disclosed surgical devices, and adapter assemblies for surgical devices and / or handle assemblies are described in detail with reference to the drawings, in which like reference numerals designate identical or corresponding elements in each of the several views. As used herein the term “distal” refers to that portion of the surgical instrument, or component thereof, farther from the user, while the term “proximal” refers to that portion of the surgical instrument, or component thereof, closer to the user.

[0030] Wavelet analysis according to the present disclosure may be implemented as software instructions executable by a processor controlling operation of any suitable powered surgical stapler, such as, a linear stapler 20 of FIG. 1 and a circular stapler 30 of FIG. 2. With reference to FIGS. 1 and 2, each of the staplers 20 and 30 may share a common power platform, i.e., a handle assembly 12 including one or more motors, a power source, a main controller, storage device, transmitter / receiver, etc. The stapler 20 also includes a linear adapter 22 configured to connect the handle assembly 12 to a loading unit 24 including an end effector 26having a first jaw having a stapler cartridge and a second jaw having an anvil (FIG. 4). The liner adapter 22 includes various mechanical linkages coupling the end effector 26 with the handle assembly 12 enabling actuation of the end effector 26 to perform various functions, e.g., clamp, staple, cut. For further details regarding the construction and operation of the linear stapler components, reference may be made U.S. Patent No. 9,839,425, filed on March 30, 2015, the entire contents of which being incorporated by reference herein.

[0031] The stapler 30 also includes a circular adapter 32 configured to connect the handle assembly 12 to an end effector 36 having a reload 38 with a stapler cartridge 31. The end effector 36 also includes an anvil 34 that is movable relative to the reload 38. The circular adapter 32 includes various mechanical linkages coupling the end effector 36 with the handle assembly 12 enabling actuation of the end effector 36 to perform various functions, e.g., clamp, staple, cut. For further details regarding the construction and operation of the circular stapler components, reference may be made U.S. Patent No. 11,045,199, filed on May 7, 2018, the entire contents of which being incorporated by reference herein.

[0032] With reference to FIG. 3, the handle assembly 101 includes a main controller circuit board 142, a rechargeable battery 144 configured to supply power to any of the electrical components of handle assembly 101, and a plurality of motors, e.g., a first motor 152a, a second motor 152b, and a third motor 152c coupled to the battery 144. The handle assembly 101 also includes a display 146. In embodiments, the motors 152a, 152b, 152c may be coupled to any suitable power source configured to provide electrical energy to the motors 152a, 152b, 152c, such as an AC / DC transformer. Each of the motors 152a, 152b, 152c is coupled a motor controller 143 which controls the operation of the corresponding motors 152a, 152b, 152c including the flow of electrical energy from the battery 144 to the motors 152a, 152b, 152c. A main controller 147 is provided that controls the handle assembly 101. The main controller 147 is configured to execute software instructions embodying algorithms, such as clamping, stapling, and cutting algorithms which control operation of the handle assembly 101.

[0033] The motor controller 143 includes a plurality of sensors 160a ... 160n configured to measure operational states of the motors 152a, 152b, 152c and the battery 144. The sensors 160a-n include a strain gauge 160b and may also include voltage sensors, current sensors, temperature sensors, telemetry sensors, optical sensors, and combinations thereof. The strain gauge 160b may be disposed within the linear adapter 22 and / or the circular adapter 32. Thesensors 160a-160n may measure voltage, current, and other electrical properties of the electrical energy supplied by the battery 144. The sensors 160a-160n may also measure angular velocity (e.g., rotational speed) as revolutions per minute (RPM), torque, temperature, current draw, and other operational properties of the motors 152a, 152b, 152c. The sensor 160a also includes an encoder configured to count revolutions or other indicators of the motors 152a, 152b, 152c, which is then used by the main controller 147 to calculate linear movement of components movable by the motors 152a, 152b, 152c. Angular velocity may be determined by measuring the rotation of the motors 152a, 152b, 152c or a drive shaft (not shown) coupled thereto and rotatable by the motors 152a, 152b, 152c. The position of various axially movable drive shafts may also be determined by using various linear sensors disposed in or in proximity to the shafts or extrapolated from the RPM or angular position measurements. In embodiments, torque may be calculated based on the regulated current draw of the motors 152a, 152b, 152c at a constant RPM. In further embodiments, the motor controller 143 and / or the main controller 147 may measure time and process the above-described values as a function of time, including integration and / or differentiation, e.g., to determine the rate of change in the measured values. The main controller 147 is also configured to determine distance traveled of various components of the adapters 22 / 32 and / or the end effector 24 / 36 by counting revolutions of the motors 152a, 152b, 152c.

[0034] The motor controller 143 is coupled to the main controller 147, which includes a plurality of inputs and outputs for interfacing with the motor controller 143. In particular, the main controller 147 receives measured sensor signals from the motor controller 143 regarding operational status of the motors 152a, 152b, 152c and the battery 144 and, in turn, outputs control signals to the motor controller 143 to control the operation of the motors 152a, 152b, 152c based on the sensor readings and specific algorithm instructions. The main controller 147 is also configured to accept a plurality of user inputs from a user interface (e.g., switches, buttons, touch screen, etc.) coupled to the main controller 147.

[0035] The main controller 147 is also coupled to a memory 141. The memory 141 may include volatile (e.g., RAM) and non-volatile storage configured to store data, including software instructions for operating the handle assembly 101. The main controller 147 is also coupled to the strain gauge 160b using a wired or a wireless connection and is configured to receive strainmeasurements from the strain gauge 160b which are used during operation of the handle assembly 101.

[0036] The handle assembly 101 includes a plurality of motors 152a, 152b, 152c each including a respective motor shaft (not explicitly shown) extending therefrom and configured to drive a respective transmission assembly. Rotation of the motor shafts by the respective motors functions to drive shafts and / or gear components of adapters 22 / 32 in order to perform the various operations of handle assembly 101. In particular, motors 152a, 152b, 152c of handle assembly 101 are configured to drive shafts and / or gear components of adapter assemblies 22 and 32 in order to actuate the end effectors 206 and 306.

[0037] The handle assembly 101 also includes a communication interface 162 configured to connect to an external computer (not shown) using a wired (e.g., Firewire®, USB®, Serial RS232®, Serial RS485®, USART®, Ethernet®, etc.) or wireless (e.g., Bluetooth®, ANT3®, KNX®, ZWave®, X10® Wireless USB®, IrDA®, Nanonet®, Tiny OS®, ZigBee®, 802.11 IEEE, and other radio, infrared, UHF, VHF communications and the like) connection. The computer may be configured to store the data transmitted thereto by the staplers 20 / 30 as well as process and analyze the data.

[0038] Referring to FIG. 4, the loading unit 24 includes a drive assembly 360 having a flexible drive shaft 364 having a distal end which is secured to a drive beam 365, and a proximal engagement section 368. Engagement section 368 includes a stepped portion defining a shoulder 370. A proximal end of engagement section 368 includes diametrically opposed inwardly extending fingers 372. Fingers 372 engage a hollow drive member 374 to fixedly secure drive member 374 to the proximal end of shaft 364. Drive member 374 defines a proximal porthole which receives a connection member (not shown) of adapter 22 when loading unit 24 is attached to distal coupling 230 of adapter 22.

[0039] When drive assembly 360 is advanced distally within end effector 26, an upper beam of drive beam 365 moves within a channel defined between anvil plate 312 and anvil cover 310 and a lower beam moves within a channel of the staple cartridge 305 and over the exterior surface of carrier 316 to close end effector 26 and fire staples therefrom.

[0040] Proximal body portion 302 of loading unit 24 includes a sheath or outer tube 301 enclosing an upper housing portion 301a and a lower housing portion 301b. The housing portions 301a and 301b enclose an articulation link 366 having a hooked proximal end 366awhich extends from a proximal end of loading unit 24. Hooked proximal end 366a of articulation link 366 engages a coupling hook (not shown) of adapter 22 when loading unit 24 is secured to distal housing 232 of adapter 22. When drive bar (not shown) of adapter 22 is advanced or retracted as described above, articulation link 366 of loading unit 24 is advanced or retracted within loading unit 24 to pivot end effector 26 in relation to a distal end of proximal body portion 302.

[0041] As illustrated in Fig. 4 above, cartridge assembly 308 of end effector 26 includes a staple cartridge 305 supportable in carrier 316. Staple cartridge 305 defines a central longitudinal slot 305a, and three linear rows of staple retention slots 305b positioned on each side of longitudinal slot 305a. Each of staple retention slots 305b receives a single staple 307 and a portion of a staple pusher 309. During operation of instrument 100, drive assembly 360 abuts an actuation sled 350 and pushes actuation sled 350 through cartridge 305. As the actuation sled 350 moves through cartridge 305, cam wedges of the actuation sled 350 sequentially engage staple pushers 309 to move staple pushers 309 vertically within staple retention slots 305b and sequentially eject a single staple 307 therefrom for formation against anvil plate 312.

[0042] The loading unit 24 may also include one or more mechanical lockout mechanisms, such as those described in commonly- owned U.S. Patent No. 5,071,052, 5,397,046, 5413,267, 5,415,335, 5,715,988, 5,718,359, 6,109,500, the entire contents of all of which are incorporated by reference herein.

[0043] The stapler 20 according to the present disclosure are configured to measure motor current draw, force, or torque imparted by the motor during any motor actuated movement or actuation, e.g., clamping, staple ejection, cutting, articulation, rotation, etc. The controller 147 is configured to analyze the sensor signals as a function of time, e.g., generate a plot, and to identify various features, e.g., patterns, peaks, and / or valleys, which convey useful information regarding operation of the stapler 20 enabling the controller 147 to identify various events based on the features of the plots.

[0044] The controller 147 may implement frequency domain analysis to analyze signals, which is effective for signals that are stationary in time, i.e., do not change characteristics over time. In addition, as spectral and other characteristics change over time, then frequency and domain analysis does not covey the temporal characteristics. Moreover, actuation signals (e.g.,firing signal) not only change over time, but also include ‘one-time’ events, which are difficult to detect using traditional frequency domain analysis. Also, the temporal information, i.e., the time at which an event occurs, is used by the controller 147 to respond in real-time, e.g., stopping the motor, outputting alert, etc. Since temporal information is not available in traditional frequency domain analysis, the controller 147 according to the present disclosure also incorporates wavelets analysis, which provides frequency and temporal information pertaining to the actuation signal. Frequency information may be used to detect features of interest in the signal and temporal information, which provides the specific time when the event has occurred.

[0045] The controller is configured to analyze the actuation signal (e.g., signal collected during firing) to identify the various events using correlation analysis as well as discrete and continuous wavelets transforms. The signal features which may be detected using these methods include end-stop mechanical obstructions, e.g., differentiating between mechanical end-stop and thick tissue which may manifest itself as a mechanical end-stop, end stop, missing component, and staple malformation. The signal features may be used to detect an end stop before the sled 350, which advances a knife and ejects the staples in a linear stapler, impacts the mechanical stop to reduce or eliminate twitch at the end of firing. The signal features may also be used to detect missing components such as the sled 350. Additionally, the signal features may be used to detect patterns that deviate from those representing proper staple formation.

[0046] Detecting a specific feature or pattern within a given signal using cross correlation method comprises of two parts. In the first part, a representative signal containing the desired feature or pattern is collected. The feature is isolated from the signal. A template signal is then generated from the feature or pattern. Generating the template signal may involve smoothing and normalizing operations. The template signal is flipped to off-set the effect of convolution. This template is stored in the non-volatile memory of the processor for use during normal operation.

[0047] With reference to FIG. 5, a method for generating and storing template signal is shown. At step 400, one of the motors 152a is actuated to advance the sled 350 to clamp tissue, eject staples, and cut the tissue. While the motor 152a is actuating the sled 350, the current draw, torque, force, or any other parameter of the motor 152a or of the mechanical linkages actuated by the motor 152a is measured at step 402. The parameter(s) can be measured by sampling current periodically. In embodiments, the sampling rate may be from about 100 per second to about10,000 per second, in embodiments from about 500 per second to about 1,000 per second. The samples may then be used by the controller 147 to analyze the operation of the motor 152a based on the measured properties.

[0048] At step 404, the current draw values are stored in memory 141 along with a timestamp for each current value. The current values may be used to generate a current draw plot 450 as shown in FIG. 6. The plot 450 is a visual representation of the current draw over time and the controller 147 may perform the data analysis without displaying or generating a visual representation of the plot 450. In other words, the controller 147 may generate the data structure represented by the plot 450 and store the same in memory 141. At step 406, the controller 147 then identifies a region of interest (ROI) 452 in the plot 450 corresponding to an event of interest, e.g., end stop. The ROI 452 is processed, which includes extracting, normalizing as ROI 452a, smoothing to obtain ROI 452b, and flipping the ROI 452 to generate ROI 452c, as shown in FIGS. 7A-C, respectively. Convolution and cross-correlation are mathematically similar, except in convolution one of the signals is flipped. Thus, for computing correlation, the pattern signal is pre-flipped to offset the effect of convolution.

[0049] As noted above, the ROI 452 may be any portion of the plot 450 that corresponds in time to an event the controller 147 is instructed to identify, e.g., approaching end stop. At step 408, a template signal plot is generated based on the ROI 452. At step 410, the template signal is stored in controller non-volatile memory for pattern identification during use of the stapler as described below in the method shown in FIG. 15.

[0050] FIG. 8 illustrates determination of an event (e.g., approaching end stop) based on the correlation plot 460, which shows a peak 462 at time sample no. 5348 where the template pattern created from the ROI 452 matches the pattern in the measured signal plot 450. At step 412, the pattern in the correlation plot 460 is identified, which is based on the amplitude of a peak of the correlation plot 460. In particular, the amplitude of the peak at the specific time exceeds the previous peaks corresponding to staple ejections and the initial contact with the sled 350.

[0051] The method of FIG. 5 may also be used to detect other events, such as mechanical faults with the loading unit 24. The stapler 20 has many mechanical and electrical components. A damaged or missing component is manifested in the motor current draw in the form a unique feature in the measured signal plot 450. In other words, the measured signal of the current drawis affected by a missing sled and the feature produced by the missing sled may be identified using correlation.

[0052] FIG. 9 shows a normal measured signal plot 450 and the output of the correlation plot 460, generated in the manner described above. The correlation plot 460 includes a peak 464 with an approximate amplitude of 195 at around sample no. 1000. This is significantly different when the sled is missing.

[0053] FIG. 10 shows a firing where the sled 350 is missing with a measured signal plot 450’, and the output of the correlation plot 460’ generated using step 408. In the vicinity of 1000 samples, the amplitude of the correlation is approximately 33 of the correlation plot 460’ which is significantly lower than about 195 seen in a normal firing of the correlation plot 460 (indicative of the sled 350 being moved). This difference between the correlation amplitudes is used to set a threshold to detect the presence or absence of the sled 350.

[0054] FIG. 15 shows a method for determining events from a measured signal and controlling the stapler. At step 401, one of the motors 152a is actuated to advance the sled 350 to clamp tissue, eject staples, and cut the tissue. While the motor 152a is actuating the sled 350, the current draw, torque, force, or any other parameter of the motor 152a or of the mechanical linkages actuated by the motor 152a is measured at step 403. The parameter(s) can be measured by sampling current periodically. In embodiments, the sampling rate may be from about 100 per second to about 10,000 per second, in embodiments from about 500 per second to about 1,000 per second. The samples may then be used by the controller 147 to analyze the operation of the motor 152a based on the measured properties.

[0055] At step 412, the signal measured in step 403 (plot 450) is correlated to the stored template signal of plot 452c to generate a correlation plot 460, which is a waveform of varying amplitude. In particular, the controller 147 performs real-time cross correlation with template signal stored in non-volatile memory. The amplitude of the correlation plot 460 is high in regions where the template signal matches the feature(s) being sought in the measured signal plot 450, i.e., end stop detection of FIG. 8 and missing sled 350 of FIGS. 9 and 10.

[0056] At step 413, the controller 147 identifies a pattern indicative of an event based on amplitude of a peak in correlated signal data. Cross correlation makes it possible to detect patterns as they occur in real-time. There is no need to store the entire plot first. While it is certainly possible to capture and store the entire plot and then perform cross correlation, doing sowill prevent the controller from responding in real-time to fast occurring patterns. Cross correlation would be suitable for identifying fast occurring events like the end-stop and stopping the motor before the sled impacts the mechanical stop and causes twitch.

[0057] At step 414, the motor 152a is controlled based on the identified pattern. If the pattern indicative of the approaching end stop is identified, the motor 152a is signaled to stop before the sled 350 impacts the mechanical limit at time sample no. 5740 thereby preventing twitching of the end effector 26. If the pattern indicative of the missing sled 350 is identified, the motor 152a is signaled to stop before continuing operation.

[0058] Wavelet transform may also be used as an alternative to correlation steps 404-410 or in addition thereto. The controller 147 performs wavelet transform operation on the measured signal plot 450 to generate a wavelets plot. The controller 147 may use either discrete or continuous wavelet transforms. Discrete wavelet transform (DWT) may be performed using a db6 wavelet followed by multi-resolution analysis. The resulting output is shown as wavelet plot 470 in FIG. 11, which includes two peaks 472 and 474. The first peak 472 is at a sample no. 940 is from the feature representative of the sled 350 being contacted and may be ignored as this is indicative of proper operation of the end effector 26. The second peak occurs at sample no. 5410 and represents the feature of interest, i.e., approaching end stop. The region of interest is about 329 samples from the mechanical limit, i.e., the end stop. This lead time is sufficient to stop the motor 152a and prevent the sled from impacting the mechanical limit and causing twitch. The amplitude of the first and second peaks 472 and 474 is significantly higher (i.e., 0.84) as compared to the rest of the signal which has an average amplitude of 0. The pattern in the wavelet plot 470 is identified, which is based on the amplitude of the peak 474. In particular, the amplitude of the peak at the specific time exceeds the previous peaks corresponding to staple ejections and the initial contact with the sled 350 (i.e., peak 472).

[0059] Continuous wavelet transform (CWT) may also be used for feature detection. Because CWT is generally computationally less efficient as compared to DWT, CWT may be better suited for precise localization of transients in a signal. CWT may be performed at two different center frequencies. With reference FIG. 12, using the measured signal plot 450, a first CWT plot 550 is generated at a first center frequency, e.g., 40.51 Hz and a second CWT plot 560 is generated at a second center frequency, e.g., 0.84 Hz,. The second CWT plot 560 is used by the controller 147 to detect the approaching end stop based on a first peak 562, which occurs at asample no. 5219 having an amplitude (e.g., 0.36) that is about 100% higher than the next highest peak 564 (e.g., 0.18). The distance in samples between the first and second peaks 562 and 564 allows for setting a threshold for detecting the approaching end-stop feature. The first peak 562 at sample no. 5219 is about 500 samples before the mechanical limit at sample no. 5736. This provides ample opportunity to stop the motor before the sled impacts the mechanical limit and causes twitch.

[0060] The measured signal plot 450 may also be processed using wavelets transform to detect patterns that deviate from normal staple formation. In particular, wavelets can be used to filter the measured signal plot 450 without losing resolution. The controller 147 performs a wavelet transform operation on the measured signal plot 450. FIG. 13 shows an enlarged portion, i.e., ROI, of the original measured signal plot 450, a wavelet filtered (i.e., processed) plot 480, and a moving average filtered plot 490, for comparison. Wavelet filtered plot 480 retains peaks and valleys while filtering out high frequency noise. Moving average filter plot 490 tends to smooth out some of the peaks and valleys which causes loss of resolution.

[0061] Wavelet filtered plot 480 retains important features of the measured signal plot 450 and is normalized and centered around zero. This makes it easier to analyze the plot for outliers using pattern matching, or statistical methods for detecting outliers like standard deviation, box plots etc. The controller 147 identify patterns indicative of various events, and in particular improper staple formations. The patterns are identified based on outliers, which are differences in amplitude between peaks 480a and valleys 480b that exceed a preset threshold. Once the outliers are identified, a qualitative assessment of the staple formation can be performed by measuring the distances between the peaks 480a and valleys 480b as shown in FIG. 14 (e.g., see differences A and E), and comparing them to the distances associated with optimal staple formation (e.g., see difference B). Outliers are indicative of sub-optimal stapler formation which can contribute to improper anastomosis and surgical complications. Once the outliers are detected, their physical position can be easily identified, and the controller 147 may output an alert (e.g., on the display 146) at step 411, which may instruct the user to examine the precise location.

[0062] The motor 152a is controlled based on the identified pattern. If the pattern indicative of the approaching end stop is identified, the motor 152a is signaled to stop before the sled 350 impacts the mechanical limit at time sample no. 5740 thereby preventing twitching ofthe end effector 26. If the pattern indicative of the missing sled 350 is identified, the motor 152a is signaled to stop before continuing operation.

[0063] While the disclosure is presented with respect to surgical instruments including handle assemblies, it should be understood the principles of the disclosure may also be applied to robotic surgical systems that do not technically include a handle assemblies. It will be understood that various modifications may be made to the embodiments of the presently disclosed staplers. Therefore, the above description should not be construed as limiting, but merely as exemplifications of embodiments. Those skilled in the art will envision other modifications within the scope and spirit of the present disclosure.

[0064] In one or more examples, the described techniques may be implemented in hardware, software, firmware, or any combination thereof. If implemented in software, the functions may be stored as one or more instructions or code on a computer-readable medium and executed by a hardware-based processing unit. Computer-readable media may include non- transitory computer-readable media, which corresponds to a tangible medium such as data storage media (e.g., RAM, ROM, EEPROM, flash memory, or any other medium that can be used to store desired program code in the form of instructions or data structures and that can be accessed by a computer).

[0065] Instructions may be executed by one or more processors, such as one or more digital signal processors (DSPs), general purpose microprocessors, application specific integrated circuits (ASICs), field programmable logic arrays (FPGAs), or other equivalent integrated or discrete logic circuitry. Accordingly, the term “processor” as used herein may refer to any of the foregoing structure or any other physical structure suitable for implementation of the described techniques. Also, the techniques could be fully implemented in one or more circuits or logic elements.

Claims

WHAT IS CLAIMED IS:

1. A powered surgical stapler comprising: an end effector including: a first jaw having a stapler cartridge storing a plurality of staples; a sled disposed in the stapler cartridge and movable to eject the plurality of staples; a second jaw having an anvil configured to deform the plurality of staples ejected by the sled; a drive beam coupled to the end effector and configured to move the sled through the stapler cartridge; a motor coupled to the drive beam and configured to move the drive beam; a sensor configured to measure at least one property of the motor; and a controller configured to: generate measured signal data based on the at least one property of the motor and time; process the measured signal data to generate processed data; identify a pattern in the processed data; and control the motor in response to the pattern in the processed data.

2. The powered surgical stapler according to claim 1, wherein the at least one property of the motor is selected from the group consisting of current draw, force, and torque.

3. The powered surgical stapler according to claim 1, wherein the measured signal data is a plot of the at least one property over time.

4. The powered surgical stapler according to claim 3, wherein the controller is further configured to identify a region of interest in the plot.

5. The powered surgical stapler according to claim 4, wherein the controller is further configured to generate template signal data based on the region of interest.

6. The powered surgical stapler according to claim 5, wherein the controller is further configured to correlate the template signal data to the measured signal data to generate correlated signal data.

7. The powered surgical stapler according to claim 6, wherein the controller is further configured to identify a pattern in the correlated signal data corresponding to an approaching end stop for the sled.

8. The powered surgical stapler according to claim 6, wherein the controller is further configured to identify a pattern in the correlated signal data corresponding to the sled being missing from the stapler cartridge.

9. The powered surgical stapler according to claim 3, wherein the controller is further configured to generate wavelet signal data using a wavelet transform function on the plot.

10. The powered surgical stapler according to claim 9, wherein the controller is further configured to identify a pattern in the wavelet signal data corresponding to an approaching end stop for the sled.

11. The powered surgical stapler according to claim 9, wherein the controller is further configured to identify a pattern in the wavelet signal data corresponding to improper staple formation of at least one staple of the plurality of staples.

12. The powered surgical stapler according to claim 11, wherein the controller is further configured to output a prompt informing of the improper staple formation.

13. The powered surgical stapler according to claim 12, further comprising a display screen configured to display the prompt.

14. A method for controlling a powered surgical stapler, the method comprising: activating a motor to advance a drive beam coupled to an end effector, the end effector including: a first jaw having a stapler cartridge storing a plurality of staples; a sled disposed in the stapler cartridge and movable by the drive beam to eject the plurality of staples; and a second jaw having an anvil configured to deform the plurality of staples ejected by the sled; measuring, at a sensor coupled to the motor, at least one property of the motor; generating, at a controller, measured signal data based on the at least one property of the motor and time; processing, at the controller, the measured signal data to generate processed data; identifying, at the controller, a pattern in the processed data; and controlling, by the controller, the motor in response to the pattern in the processed data.

15. The method according to claim 14, wherein processing the measured signal data further includes: identifying a region of interest in the measured signal data; generating template signal data based on the region of interest; and correlating the template signal data to the measured signal data to generate correlated signal data.

16. The method according to claim 15, wherein identifying the pattern further includes identifying a pattern in the correlated signal data corresponding to at least one of an approaching end stop for the sled or the sled being missing from the stapler cartridge.

17. The method according to claim 14, wherein processing the measured signal data further includes generating wavelet signal data using a wavelet transform function on the measured signal data.

18. The method according to claim 17, wherein identifying the pattern further includes identifying a pattern in the wavelet signal data corresponding to an approaching end stop for the sled.

19. The method according to claim 17, wherein identifying the pattern further includes identifying a pattern in the wavelet signal data corresponding to improper staple formation of at least one staple of the plurality of staples.

20. The method according to claim 19, further comprising: outputting a prompt informing of the improper staple formation on a display screen.

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