Delivery of tissue treatment energy using optical imaging - Patents.com

Abstract

A system for imaging and treating tissue may include or employ an imaging sensor adapted to receive imaging information from an internal location of a human or animal subject, a tissue treatment output for applying tissue treatment to tissue at the internal location of the subject, and a controller circuit comprising signal processing circuitry configured to image-process the imaging information to determine structures or other characteristics at or near the internal location of the subject and adjust parameters or algorithms that control the tissue treatment output based at least in part on the imaging information.

Description

[Technical field]

[0001] Claiming priority This application claims priority to U.S. Provisional Application No. 63 / 155,444, filed March 2, 2021, U.S. Provisional Application No. 63 / 271,053, filed October 22, 2021, and U.S. Provisional Application No. 63 / 265,978, filed December 23, 2021, each of which is incorporated by reference in its entirety and the benefit of priority to each of them is claimed herein.

[0002] The present invention relates to the delivery of tissue treatment energy using optical imaging. [Background technology]

[0003] A variety of different types of energy, such as radio frequency (RF) or other electromagnetic energy, plasma energy, or ultrasonic energy, can be used alone or in combination with mechanical energy delivery (e.g., using a sharp cutting instrument) or manipulation (e.g., using forceps) to seal vessels, cut or cauterize tissue, ablate tissue, or coagulate tissue, among others. There are many types of monopolar or bipolar energy devices for various surgical purposes. In one example of an energy delivery device ("energy device"), forceps can be utilized in laparoscopic surgery and the like. The forceps can be used to control fine movements within the patient's body and can include a gripping assembly and / or a cutting assembly. Additionally, the forceps can utilize electrical energy in the gripping assembly. Electrosurgical sealing forceps can further include or utilize energy devices such as RF, ultrasonic, and microwave vascular sealing devices. The forceps can clamp the tissue, and the elastin or collagen in the clamped tissue can be dissolved by the energy device to seal the tissue. Summary of the Invention [Means for solving the problem]

[0004] The inventors have recognized that, among other things, tissue treatment procedures using the delivery of energy can benefit from improvements including shortening treatment times, reducing complications from treatment, and reducing the required skill level of the surgeon or other professional performing the procedure, such as helping to reduce or avoid the need for surgical "tricks" or techniques to accommodate changing conditions at the target site during treatment. For example, an approach to electrosurgery may include or use an electrosurgical device having a radio frequency (RF) or other electromagnetic energy delivery system with near-instantaneous feedback regarding one or more conditions at the target site (e.g., tissue impedance, phase angle of treatment power delivery, etc.) to adjust the electrosurgical treatment energy delivered to the tissue at the target site based on an electrical feedback signal representative of such condition or conditions at the target site. However, a single common RF electromagnetic energy waveform application process may be applied to different tissue types that may exhibit different characteristics during vessel sealing or during a particular treatment such as monopolar or bipolar cutting or any other tissue treatment. For example, the carotid and renal arteries have different vessel sealing characteristics, but in one approach, each such vessel is likely to be controlled by the same treatment energy waveform application process. Similar considerations may exist for other tissues that may be more specifically treated, such as fat or ligaments or other tissues. Thus, in some cases, feedback of electrical tissue properties (e.g., tissue impedance) from the target tissue site alone may not be sufficient to properly regulate energy treatment delivery or to properly distinguish between tissue types to allow for adjustments or modulation or other nuances in the way electrical energy is delivered to the target tissue site. For example, the inventors have recognized that, among other things, the carotid artery should have electrical treatment power applied relatively slowly, e.g., for sealing of the vessel, whereas the renal artery can tolerate faster power application without causing tissue "popping" problems.Such tissue popping is a phenomenon in which steam generated within tissue may exit, for example, from between the jaws of a forceps, at a rate that may cause damage to the vessel wall, including at one or more locations that may be remote from the target vessel sealing site, with the application of mechanical pressure and electrical energy together. Thus, feedback in addition to or other than electrical feedback may be used to help make different, e.g., more appropriately tailored, energy delivery decisions.

[0005] The electrosurgery approach can also help provide a target closing force delivered by the end effector regardless of the amount of tissue along the jaw contact area. For example, the electrosurgical system can help the user achieve a desired tissue pressure between the jaws during an electrosurgical treatment. The target closing force can be determined by the system based on visualization feedback. In one example, the system can determine a target tissue pressure based on a vessel type and then determine a target closing force required to deliver the target tissue pressure to the vessel. In another example, the target tissue pressure can be estimated based on a measured tissue parameter, such as a vessel diameter, and the system can then determine a target closing force required to deliver the target tissue pressure to the tissue. The system can include or use a processing unit configured to establish, adjust, or modulate the jaw closing force or energy waveform based on data collected from the sensor. The processing unit can use the data collected from the sensor, such as to help determine the target tissue pressure or target closing force. [Brief description of the drawings]

[0006] [Figure 1A] FIG. 1 is a side view of an example of an electrosurgical system. [Figure 1B] FIG. 2 illustrates an example of a jaw assembly in a first position. [Figure 1C] FIG. 2 illustrates an example of a jaw assembly in a second position. [Figure 1D]1 illustrates an example of an electrosurgical system for compressing a target object. [Figure 1E] FIG. 1 illustrates an example of an integrated camera for use with an end effector assembly. [Diagram 2] FIG. 1 illustrates an example of an electrosurgical system for use by a surgeon. [Diagram 3] 1 is a block diagram of an example machine in which one or more of the methods discussed herein may be implemented. [Figure 4] 1 is a flow chart of a method of using an example of an electrosurgical system including a jawed electrosurgical device. [Diagram 5] 1 is a flow chart of a method of using an example of a generic electrosurgical system. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] The present disclosure relates to devices and methods for treating tissues, such as, but not limited to, blood vessels. In an approach to tissue treatment, energy devices, such as radio frequency (RF), ultrasonic, and microwave vascular sealing devices, can be used as part of an electrosurgical sealing forceps. An end effector assembly, such as one that can include a forceps, can provide a desired jaw pressure, such as compressing tissue for electrosurgical sealing. The energy device can be used to apply energy to treat one or more tissue components, such as collagen or elastin of the tissue, and the heated or melted components can be used to seal the tissue. Certain electrosurgical sealing forceps can also include a cutting element, such as a fixed or movable cutting blade, for severing the sealed vessel as part of the end effector. Such devices can use a relatively high jaw closure force to compress vascular tissue for sealing. However, a problem with this approach is that if the tissue is not held at the proper target tissue pressure, the undesired jaw closure force can cause undesired tissue damage during the procedure. For example, if the area between the jaws is not filled with tissue, it can be difficult for the user to provide the proper target tissue pressure. In such cases, the closure force required from the end effector to provide the target tissue pressure may vary based on the amount of tissue in contact with the contact surface area of ​​the jaws. For example, at a constant closure force where the jaws compress a relatively small amount of tissue, a higher tissue pressure is provided than when the jaws compress a relatively large amount of tissue at the same constant closure force. Also, providing the desired tissue pressure may be difficult for a user due to varying tissue thickness, morphology, size, volume, density, composition, water content, etc. In general, a "target tissue pressure" may refer to a relatively small range of pressures that are acceptable for a procedure, and providing the target tissue pressure may be important, such as for a user to perform a successful procedure. If too little closure force is applied, the tissue may be compressed at too little tissue, and the resulting seals created may be too low in their "burst pressure" (e.g., blood pressure required to prevent the created seal from breaking).This is because the "glue" created from dissolved elements of the vessel, such as collagen, is not sufficiently pressed together. This insufficient pressurization of the adhesive can result in weakness at the pressurized joint interface, resulting in insufficient sealing. If too much closing force is applied, the tissue pressure is too high and irreparable tissue damage, such as tissue rupture and tearing, can occur. Also, if too much closing force is applied, the tissue pressure is too high and tissue popping can occur if steam generated in the tissue during the application of energy is trapped between the jaws at high pressure until the jaws are released after the procedure. At this point, the steam in the vessel wall can escape at high speed and damage the surrounding tissue. This can result in post-sealing weakness that can later rupture when the patient recovers from anesthesia or after surgery (as the patient's rising blood pressure reaches a certain blood pressure level in the vessel that causes failure at the vulnerable location of the "sealed" vessel).

[0008] The inventors have recognized, among other things, that creating a tissue treatment system capable of determining and providing a "target" closure force delivered by the end effector regardless of the amount of tissue along the contact area of ​​the jaws can aid a user in achieving a target tissue pressure between the jaws during an energy delivery treatment, such as for vessel sealing. The target closure force can be determined and established or adjusted by the system, such as based on visualization feedback, such as using tissue images generated by an imaging sensor, such as a camera. In one example, the system can determine a target tissue pressure based on an identified tissue type, such as, for example, vessel type, and then use information from the image to help determine a target closure force suitable for delivering the target tissue pressure to a vessel or other tissue. In another example, the target tissue pressure can be estimated or calculated based on one or more measured tissue parameters, such as cross-sectional vessel dimensions, such as, but not limited to, vessel diameter, and the system can then determine the target closure force required to deliver the target tissue pressure to the tissue. Because vessels are natural and do not conform to an exact circular shape in cross section, the vessel diameter can include an approximated diameter, a measured diameter, an average diameter, a minimum diameter, a maximum diameter, or a calculated diameter obtained from one or more measurements. The one or more tissue parameters may additionally or alternatively correspond to one or more of thickness, morphology, size, volume, density, composition, water content, dynamic modulus, viscoelastic properties, electrical conductivity, impedance, color, or non-visible light reflection, scattering, or other optical response feedback, or other describable properties of tissue. Alternatively or additionally, the system may use the tissue parameters to establish, adjust, or modulate another parameter of the electrosurgical system, such as the amplitude, frequency, pulse width, current, phase angle, or other electrical property of the electrosurgical energy signal. The system may include or use a processing unit configured to establish, adjust, limit, or modulate one or both of the jaw closure force or the energy waveform, such as may be based on data collected from the imaging sensor and / or other sensors.The processing unit 122 can use the data collected from the sensors, such as to determine a target tissue pressure or a target closure force. Tailoring one or both of the force or electrical properties applied by the device to one or more tissue parameters can be beneficial as it can provide improved performance of the device across different tissues.

[0009] For example, a particular tissue or tissue type within the visualization field may be identified, such as by using one or more visualization or optical imaging techniques. Such techniques may include using image signal processing, such as with trained learning models, such as trained using one or more machine learning or other artificial intelligence (AI) techniques, to help identify one or more particular tissues, tissue types, or tissue structures, etc., at or near a target site, such as within an endoscopic, laparoscopic, or other patient-internal visualization field, or scene. Particular tissues may also be identified in visualization fields external to the patient. For example, this type of visualization or optical imaging may be configured to use visualization or optical imaging information to identify particular anatomical structures or tissues, or features thereof. When tissue is positioned or positioned relative to an end effector, etc., for vessel sealing or similar treatment, such as by electrical treatment or other therapeutic medical device, a particular treatment or treatment parameter setting (e.g., process, waveform, etc.) may be selected. A particular treatment or treatment parameter setting may be selected from a plurality of available treatments or treatment parameter settings, such as may be adjusted based at least in part on using visualization or optical imaging information. As described herein, visualization or optical imaging information may also be used in combination with other information, such as electrical information (e.g., tissue impedance). In one example, if the tissue type or characteristics cannot be determined (e.g., with a desired accuracy) using visualization or optical imaging techniques, a particular "compromise" vessel sealing (or other treatment) treatment or treatment waveform or parameters may be selected, such as based at least in part on suitability for all tissue types, or a variety of tissue types, or a subset of tissue types, or based at least in part on electrical tissue properties such as tissue impedance characteristics, phase angle of treatment power delivery, etc.

[0010] 1A, 1B, 1C, 1D, and 1E show an example of a portion of an electrosurgical system 100. The electrosurgical system 100 can include or use a medical device 102 having an end effector assembly 104. As described herein, the system 100 can be configured to deliver a conditioned energy output to a variety of devices. In an illustrative example, the system 100 can provide a conditioned waveform to one or more electrodes, such as an active electrode 111 (electrode 111 shown in FIG. 1D). Such a conditioned energy output can be used to treat tissue, such as to seal, cut, ablate, fulgurate, or desiccate, among other effects. In some examples, the end effector assembly 104 includes both an active electrode and a return electrode, although the device need not be a forceps or bipolar, as shown in the illustrative examples. The waveforms delivered by the processing unit 122 can be conditioned for monopolar and other types of devices as well. For example, the active electrode of the device can be used in conjunction with a remote return electrode, such as, but not limited to, a return electrode pad.

[0011] Additionally, the regulated energy output described herein may be used in a system that delivers any type of energy output compatible with a particular end effector or device. In just a few non-limiting examples provided for clarity, regulating the energy output may include regulating ultrasonic or radio frequency energy delivered to / by an ultrasonic forceps, or regulating thermal energy delivered to / by a forceps or other device for treatments such as cutting, sealing, coagulation, ablation, desiccation, fulguration, etc.

[0012] End effector assembly 104 may include or use a jaw assembly 106. Alternatively or additionally, end effector assembly 104 may include or use a surgical "J-hook" type electrode or other electrode type. Jaw assembly 106 may include a first jaw member 108 and a second jaw member 110. Second jaw member 110 may be pivotally coupled to first jaw member 108 about a pivot axis or other pivot point 112. One or more drivers 119 may be included or used within device 102 to be housed within handle 118 and mechanically coupled to end effector assembly 104. One or more drivers 119 may also be included at or near the distal end of assembly 104 to directly drive jaw assembly 106. Driver 119 may be any suitable driver associated with or coupled to an end effector in any arrangement, including, for example, robotic applications.

[0013] The electrosurgical system 100 may also include or use one or more on-board or separate sensors 114 for determining tissue characteristics. For example, as shown in FIG. 1A, the sensor 114 may be separate from the end effector assembly, extending from another shaft or mount of the device 102, or from another device, such as a robotic arm or videoscope. The sensor 114 may also be integrated at or near the end effector assembly 104. Force sensor types may include, for example, strain, piezoelectric, inductive, capacitive, magnetostrictive, hydraulic, or pneumatic, which may measure forces such as compression, strain, contact, displacement, bending, tension, shear, or torque. Such force sensors 114 (or electrical sensors 114) may be located within the end effector 104, for example, within one or more components of the jaw members 108, 110 or tissue sealing plate 109, which in this example may include the active electrode 111 and the return electrode 113. The force sensor 114 may be positioned at any other suitable location on the forceps for measuring the force or displacement of the jaws 108, 110. In one example, the sensor 114, such as a camera, may be located at or near the distal end of the end effector assembly, may be located on a jaw of the jaw assembly 106, or may be integrated with the end effector assembly 104, such as a J-hook (as shown in FIG. 1E).

[0014] As shown in FIG. 1B and FIG. 1C, the jaws 108 and 110 of the jaw assembly 106 may be movable between a first position a, in which the first and second jaw members 108 and 110 are spaced apart from one another, and a second position b, in which the first and second jaw members 108 and 110 are disposed closer to one another than the first position a. As shown in FIG. 1D, the end effector assembly 104 may compress a blood vessel or other target object 124, such as a body part, anatomical feature, tissue, vein, artery, or combination thereof, of a human or animal subject. In one example, the end effector assembly 104 may be used in the system 100 to compress one or more of a lymphatic vessel, a tissue pedicle, an artery, and a vein, such as having a diameter or similar cross-sectional dimension ranging from about 0.5 mm to about 7 mm. As used herein, a diameter of a vessel may refer to either a measured diameter or an average diameter along a length of the subject of the vessel of interest. In another example, the end effector assembly 104 may be used in the system 100 to compress lymphatic vessels or arteries having a diameter of greater than 7 mm or other similar cross-sectional dimensions. At least one of the first and second jaw members 108 and 110 may include an electrode that may be adapted to be electrically connected to a source of electrosurgical energy to provide a current that may be passed through the electrode of the end effector assembly 104. For example, a treatment current may flow from the first jaw member 108 to the second jaw member 110 when tissue is located within the jaws, and the treatment current may coagulate blood, cauterize, cut, or a combination thereof. The end effector assembly 104 generally may include one or more working assemblies, such as a pair of jaws, and controls sufficient to operate the one or more working assemblies.The end effector assembly 104 can include components used to perform the enumerated functions, and can include an elongated or other shaft 116 (e.g., a tubular member, hollow tube, or tube assembly), a hand piece 118, one or more operable mechanisms, such as an actuator 120, used to manipulate the shaft 116 or to actuate the end effector assembly 104, or combinations thereof. The hand piece 118 can be an assembly of parts or housing structures that can form a hollow hand piece 118 structure. In one example, the shaft 116 and end effector assembly 104 can be included or attached to the end of a robotic arm to allow for stabilization, positioning, manipulation, and operation of the robot instead of being held in the hand of a user gripping the hand piece 118.

[0015] In one example, the sensor 114 can include a camera capable of illuminating and capturing a tissue image, such as using a focal plane array (FPA) imaging sensor array of pixels. For example, as shown in FIG. 1E, the sensor 114 can be a camera disposed at the distal end of the end effector assembly 104. One or more cameras 114 can be incorporated at or near the jaw assembly 106 or the J-hook (as shown). One or more light sources 115 can be included in the system 100, such as near the camera 114, for illumination of the surgical site, etc. If the camera 114 is incorporated in the device 102, operation of the device can position both the camera 114 and the tissue simultaneously or in parallel. The tissue image can include one or more device-recognizable features of the tissue, such as by applying a trained learning model or other image signal processing to the image to recognize one or more features in the image. The sensor 114 may also include an electrical sensor or electrode to provide electrical characterization of tissue compressed between the first jaw member 108 and the second jaw member 110 during, after, or alternating with the application of electrosurgical energy to the tissue. The sensor 114 may also involve a fiber optic bundle or other illumination optics to transmit light from an external light source to an internal target, and may include one or more of a spectroscopic or analytical imaging sensor, a hyperspectral imaging sensor, a colorimetric imaging sensor, a video camera sensor, an infrared imaging sensor, an ultrasonic imaging sensor, a 3D imaging sensor, a LIDAR imaging sensor, an optical coherence tomography (OCT) imaging sensor, a focal plane array (FPA) imaging sensor, or a fluorescent or other shifting wavelength response imaging sensor. The sensor 114 may include a force sensor, such as embedded or otherwise included within the jaw assembly 106, to measure the displacement of the clamping force by the tissue. The force sensor included within the sensor 114 may include, for example, a strain gauge, a load cell, a piezoresistive sensor, an inductive sensor, a capacitive sensor, or a magnetostrictive sensor.The sensor 114 may include an electrical sensor for measuring one or more electrical properties of tissue, such as, for example, resistance, capacitance, or inductance.

[0016] The sensor 114 may be communicatively coupled to a processing unit 122. The processing unit 122 may receive data from the sensor 114 and based thereon may use the data to determine compressive force parameters, such as a target clamping force for the jaw assembly 106. In one example, the processing unit 122 may generate a tissue image of tissue compressed between the first jaw member 108 and the second jaw member 110. The tissue image may include one or more device-recognizable features of the tissue. The processing unit 122 may be configured to recognize or identify various types or characteristics of tissue or vessels, such as a carotid artery, a femoral artery, a renal artery, a pulmonary artery, or other types of specific arteries, veins, or lymphatic vessels. In one example, the processing unit 122 may use trained learning models, algorithmic signal processing approaches, or both to recognize or identify one or more device-recognizable features of the tissue image, such as one or more shapes, patterns, colors, etc. These features may be cross-referenced with a database of image feature data, in one example. Processing unit 122 can further manipulate the image, the features, or both, such as changing from color to grayscale or monochrome, or segmenting or outlining certain features, to help improve feature recognition. In one example, depth of field analysis circuitry can be included or used by processing unit 122 to help obtain a representation of the 3D features of the tissue.

[0017] The processing unit 122 may also include, use, or execute a machine learning model trained to recognize or identify various types of vessels using the specimen image. The processing unit 122 may include or use one or more algorithms capable of extracting data from the specimen image, such as data by compression, filtering, edge detection, corner detection, blob detection, ridge detection, Hough transform, image segmentation, optical flow, genetic algorithm (GA), or other techniques to algorithmically analyze the specimen image. The one or more algorithms may be capable of digital image processing to extract relevant data from the specimen image to enable recognition of relevant vessel features. The machine learning model may include or use training data received as input, such as classified training data from a human user. The model may include or use one or more prediction engines. The prediction engine may include or use several engine parameters, such as data source, algorithm, configuration inputs, or other features of the engine under consideration. The prediction engine may include data source parameters, such as user-inputted symptom data. The model may include multiple algorithms and source code for generating prediction engine variants by using training data to help algorithmically arrive at a label that represents an accurate identification of the vessel. The model may replace a prediction engine variant with a new prediction engine variant based on the training data received as input, the performance of the prediction engine variant, or both. The prediction variant may be selected by an operator, such as a human user, or may be automatically generated. The engine parameter selection may be tagged or played back by the prediction engine to evaluate and tune the prediction engine. In some examples, an operator may manually determine one or more new engine variants to troubleshoot, tune, or otherwise override the prediction engine.The prediction engine may include or use the training data locally, such as exclusively receiving training data for multiple specimen images generated by a single user. Alternatively or additionally, the prediction engine may include or use the training data globally, such as collectively receiving training data for specimen images generated by multiple users. The prediction engine may interact with one or more servers, which may be capable of data storage, local data communication, global data communication, or any combination thereof. The prediction engine may interact with a website for global data communication, etc.

[0018] The machine learning model may be an artificial neural network in some implementations. An artificial neural network is artificial in the sense that it is a computational entity inspired by biological neural networks but modified for implementation by a computing device. Artificial neural networks are used to model complex relationships between inputs and outputs or to find patterns in data where dependencies between inputs and outputs cannot be easily ascertained. A neural network typically includes an input layer, one or more intermediate ("hidden") layers, and an output layer, each layer including several nodes. The number of nodes may vary between layers. A neural network is considered "deep" if it includes two or more hidden layers. The nodes in each layer connect to some or all nodes in subsequent layers, and the weights of these connections are typically learned from data during a training process, for example through backpropagation, where network parameters are adjusted to produce the expected output given the corresponding input in the labeled training data. An artificial neural network is thus an adaptive system 100 configured to change its structure (e.g., connection configurations and / or weights) based on information flowing through the network during training, and the hidden layer weights can be viewed as encoding meaningful patterns in the data.

[0019] A fully connected neural network is one in which each node in the input layer is connected to each node in the subsequent layer (the first hidden layer), which is in turn connected to each node in the subsequent hidden layer, and so on until each node in the final hidden layer is connected to each node in the output layer.

[0020] In one example, the machine learning model may include or use a convolutional neural network (CNN). A CNN is a type of artificial neural network, and like the artificial neural networks described above, a CNN is composed of nodes and has learnable weights. However, a layer of a CNN may have nodes arranged in three dimensions: width, height, and depth, corresponding to a 2×2 array of pixel values ​​in each video frame (e.g., width and height) and the number of video frames in the sequence (e.g., depth). A node in a layer may be locally connected only to a small region of the previous layer in width and height, called a receptive field. The weights of a hidden layer may take the form of a convolution filter applied to the receptive field. In some examples, the convolution filter may be two-dimensional, and thus convolution with the same filter may be repeated for each frame (or convolutional transform of an image) in the input volume, or for a specified subset of frames. In other examples, the convolution filter may be three-dimensional, and thus extend throughout the entire depth of the nodes in the input volume. Nodes in each convolutional layer of a CNN can share weights such that a convolutional filter of a given layer is replicated across the width and height of the input volume (e.g., across the entire frame), reducing the overall number of trainable weights and increasing the applicability of the CNN to datasets other than the training data. Values ​​of a layer may be pooled (e.g., values ​​representing a particular pixel are passed forward and other values ​​are discarded) to reduce the number of calculations in subsequent layers, and further along the depth of the CNN, a pooling mask may reintroduce any discarded values ​​to return the number of data points to the previous size. Several layers may be stacked, optionally some fully connected, to form a CNN architecture. The machine learning model may also be at least one of a support vector machine (SVM), K-nearest neighbors (KNN), an artificial neural network (ANN), or an ensemble model combining SVM and ANN.

[0021] In one example, the processing unit 122 can use data from the sensor 114 to measure or estimate the diameter of a vessel sensed by the sensor, such as a vessel within the field of view of the sensor or in contact with the sensor, a vessel located between the first jaw member 108 and the second jaw member 110, or a vessel compressed between the first jaw member 108 and the second jaw member 110. For example, device-recognizable features from the specimen image can be used by the processing unit 122 to measure or estimate the diameter of the vessel. Also, the pivot angle of the first jaw member 108 and the second jaw member 110 can be measured and used to help estimate the diameter of the vessel. Also, the processing unit 122 can use data from the sensor 114 to measure or estimate the amount of tissue compressed between the jaws. The processing unit 122 can use data from the sensor 114 to determine the contact area of ​​the tissue compressed between the first jaw member 108 or the second jaw member 110. As described herein, a vessel diameter or other cross-sectional outer or inner dimension can include an approximated diameter, a measured diameter, an average diameter, a minimum diameter, a maximum diameter, or a calculated diameter obtained from one or more measurements.

[0022] The processing unit 122 can determine a target tissue pressure based on, for example, the identified vessel type, the identified vessel location, or the identified vessel diameter. The processing unit 122 can determine a target closing force based on, for example, the amount of tissue located between the jaws and compressed between the jaws, the contact area of ​​the tissue compressed between the jaws, the identified vessel type, and the identified vessel location, or the identified vessel diameter sensed by a sensor. The processing unit 122 can help deliver the determined target closing force based on the end effector assembly 104. The end effector can receive input from the processing unit 122 to apply the target closing force by actuation of one or more actuators, for example, motors, micromotors, etc. In another example, the robotic unit can receive input from the processing unit 122 to apply the target closing force. In one example, the target closing force or target tissue pressure can be determined by the processing unit 122 and can serve as a threshold input for user feedback. For example, a user of the end effector assembly 104 may receive visual, audible, tactile, or other feedback from the processing unit 122 upon meeting or exceeding a determined target closure force or a determined target tissue pressure, and the user may receive visual, audible, tactile, or other feedback upon approaching a determined target closure force or a determined target tissue pressure.

[0023] In another example, the processing unit 122 can use either of these two to determine a target tissue pressure or target closure force and establish, adjust, or modulate an energy waveform, for example, to help compensate for too much tissue pressure or closure force. Alternatively, or in addition to adjusting the target clamping force, the magnitude of the electrosurgical energy can be established or adjusted to mitigate potential side effects of the treatment on the patient, such as overheating or "popping" of the tissue. For example, the electrosurgical system 100 can be configured to use visualization or optical imaging information to identify a particular anatomical structure, or tissue, or features thereof. When tissue is positioned or positioned for vessel sealing or similar treatment, such as with electrical treatment or other therapeutic medical devices, a particular treatment or treatment parameter setting (e.g., process, waveform, etc.) can be selected from a plurality of available treatments or treatment parameter settings, such as may be adjusted based at least in part on using visualization or optical imaging information that may also be used in combination with other information, such as electrical information (e.g., tissue impedance). In one example, if the tissue type or characteristics cannot be determined (e.g., with a desired accuracy) using visualization or optical imaging techniques, a particular "compromise" vessel sealing (or other therapeutic) treatment or treatment waveform or parameters may be selected, such as based at least in part on suitability for all or a variety of tissue types, or based at least in part on electrical tissue characteristics such as tissue impedance characteristics, phase angle of therapeutic power delivery, etc. If the tissue type or characteristics cannot be satisfactorily determined by the processing unit 122, a warning may be output to notify the user of such event, to notify the user that a less-than-tuned output is being provided, or to alert the user to reposition the end effector relative to the tissue, such as to perform additional sensing.

[0024] Feedback or other sensed input signals based on visualization or optical imaging information can serve as a "predictive" selection of therapeutic energy delivery waveforms, treatments, or treatment parameters, which can optionally be further combined with one or more other feedback or other sensed information, such as based on tissue impedance, energy delivery phase angle, or other features that can be monitored during tissue modification or other treatment delivery and used as feedback signals for signal processing and controlling adjustments, timing, or alterations of treatment delivery.

[0025] Additionally or alternatively, the waveform, or other treatment parameter, or feature, or treatment may be selected based at least in part on information derived from visualization or optical imaging other than information specific to the target site to be treated. For example, if the target site to be treated is close to an organ at risk (OAR) or heat sensitive area (such as the colon), the waveform, or other treatment parameter, or feature, or treatment may be selected or adjusted based at least in part on such information to help create the smallest possible thermal margin or to be within a thermal budget specific to such a situation. The presence or proximity of such organ at risk or other heat sensitive area may be determined using visualization or optical imaging information, such that one or more image processing techniques (e.g., segmentation, atlas, trained statistical learning models) can be used, and the internal imaging information from the first sensor may be combined with external video or still camera information from a second sensor to provide an indication of the patient's position, orientation, or movement, which may be image processed for use in helping determine proximity to one or more such other anatomical areas of interest for delivery of treatment or avoidance of treatment. The visualization or optical imaging information may additionally or alternatively be used to detect one or more structures that should not be modified by the treatment, so as to automatically stop application of the treatment energy (or provide a user warning) before reaching a thermal margin or limit range of one or more such structures that should not be altered by the delivery of the treatment energy. If the delivery of the treatment energy is affected by one or more other components in the system 100 (e.g., electrical treatment delivered using forceps to crush a vessel closed during sealing), one or more control or other parameters (e.g., gripping force) associated with such other components may be at least one of specified, adjusted, warned (e.g., using audible, tactile, or visual feedback to a user), limited, or controlled (e.g., using a robotic forceps controller circuit) based at least in part on such visualization or optical imaging information.

[0026] For example, different tissues perform differently under compression, such as with forceps during vessel sealing: in some instances, a vessel has no added benefit to thermal margin or burst pressure from being compressed to a lateral dimension less than 40% of its static dimension when uncompressed, whereas other tissues require a greater amount of compression to reduce thermal margin.

[0027] In addition, vessel sealing or other electrical treatment waveforms may also be selected according to other considerations derived from an understanding of the visualization or optical image. Some areas, such as tissue close to another organ, for example the colon, may benefit from treatments that use as little thermal margin as possible during the activation and sealing process. However, thermal margins may be traded off with the rate of activation. Slower activation rates may be used to reduce or minimize thermal margins, such as by delivering only a small amount of energy before the steam condensation period of the cycle begins. Such treatment timing, rate, and energy considerations may also be controlled to be based at least in part on visualization or optical imaging information.

[0028] There are many different tissue materials that a user may wish to activate and cut. Such tissues may include fat, mesentery, ligaments, etc. Such tissues may be activated using a different treatment output modality than that used for vessel sealing. Such various treatment output modalities may be selected, established, or adapted based at least in part on visualization or optical imaging information, such as by using imaging processing and trained machine learning or trained statistical models to identify treatment target or non-target tissue types or characteristics prior to or during treatment administration, including in combination with one or more other factors such as electrical properties of the tissue or treatment delivery (e.g., tissue impedance or treatment power phase angle).

[0029] Thus, the present technique is not limited to use in vascular sealing devices, but may also be applied elsewhere, including, for example, monopolar and bipolar cutting devices, such as those intended to hemostatically cut tissue. For example, the present technique may be used not only to visually locate vessels on the surface of tissue, but also to use optical imaging techniques (e.g., involving absorption, scattering, etc.) to help identify vessels or other structures below the visually observable surface of such tissue.

[0030] For example, while a surgeon or other user is cutting across variable sections of tissue material having different fat levels as well as vascularity or differences in vascularity, appropriately adjusted treatment settings can be selected before tissue treatment begins. This can advantageously help to provide a more rapid response during treatment (by the user or by an automated treatment system controller using during treatment feedback signals such as tissue impedance, power delivery phase angle, etc.) than would otherwise be the case when reacting to certain situations, such as heavy bleeding, for example.

[0031] Additionally, the techniques may be used to detect structures that should not be modified or affected by the treatment and help stop the application of energy when the device is placed near a thermal margin distance from the structure to prevent it from being affected or overly affected by the treatment. For example, the ureter can be damaged during a gynecologic procedure that involves the application of electrical or other energy to nearby tissue. However, the techniques may include modifying the output energy, such as turning off energy delivery, warning the user, or requesting user confirmation to override such system control, upon visualization or optical imaging information indicating that a structure to be protected is nearby.

[0032] Thus, the techniques can include "predictive" or a priori selection of energy delivery (e.g., selecting or adjusting an algorithm or a more simplified waveform, etc.) that can be combined with one or more feedback signals (e.g., tissue resistance, tissue reactance, tissue impedance, phase angle, etc.) used during tissue modification. The techniques can be used with electrical energy treatments, monopolar, bipolar, RF, ultrasound, laser, microwave, resistive, among others.

[0033] The system may include or be coupled to an endoscope or other visualization device capable of providing illumination to the tissue site within the patient, and a photodetector or multi-pixel imaging array to convert response light from the tissue site within the patient for optical imaging. The system may further include electrical signal processing circuitry. This may include imaging processing circuitry that may be used to process the converted signal into an electrical representation of an optical image. The represented image may be used by the signal processing circuitry, such as for training (or use with trained) neural networks or other machine learning or statistical learning models such as those described herein. In such methods, image recognition techniques may be used to help recognize tissue types, tissue materials, tissue features, or tissue structures. Such visualization or optical imaging information may then be used to adjust one or more parameters of the treatment delivery, such as to better treat the area of ​​interest or to help avoid affecting one or more other sensitive nearby areas or structures. Such a priori visualization or optical imaging information may be used in combination with ongoing information acquired during treatment delivery regarding tissue features or treatment features, etc., to further regulate, limit, or control the treatment delivery. Examples herein can also include providing a user of the end effector assembly 104 with the ability to receive visual, tactile, or audible feedback from the processing unit 122 to help prompt or suggest to the operator to cease or shorten the energy application period based on the amount of tissue pressure or closure force, such as after energy application and appropriate tissue pressure or closure force, and to inform the user that too much tissue pressure or closure force is being applied, such as in terms of a particular sensed tissue type or tissue parameters observed within or grasped by the end effector.

[0034] The processing unit 122 may include or use a processing circuit. The processing unit 122 is not limited to a single unit or circuit. The processing unit 122 may include multiple processing sources, some of which may be physically separated from the device or located remotely from the device. In one example, the processing unit 122 may include or use a memory circuit that includes instructions that, when executed by the processing circuit of the processing unit 122, cause the processing unit 122 to perform an inspection operation using the sensor 114. The processing circuit may include or use an image processing circuit configured to determine a contact area of ​​tissue compressed between the first jaw member 108 and the second jaw member 110 using the tissue image and a trained machine learning model. The processing circuit may include or use an image processing circuit configured to determine a contact area of ​​tissue compressed between the first jaw member 108 and the second jaw member 110 using the tissue image and an automated algorithm. The processing circuitry may include or may use image processing circuitry configured to use the tissue images and the trained machine learning model to determine a cross-sectional diameter of at least one vessel contained within tissue compressed between the first jaw member 108 and the second jaw member 110. The processing circuitry may include or may use image processing circuitry configured to use the tissue images and the automated algorithm to determine a cross-sectional diameter of at least one vessel contained within tissue compressed between the first jaw member 108 and the second jaw member 110.

[0035] FIG. 2 illustrates an example of an electrosurgical system in use by a surgeon. The system may include or use one or more sensors 114 configured to inspect tissue compressed between the first jaw member 108 and the second jaw member 110. The sensor 114 may inspect tissue at any location adjacent to the end effector assembly 104 or located between the first jaw member 108 and the second jaw member 110 within the field of view of the sensor before or after the tissue is compressed by the end effector assembly 104 or without final compression by it. For example, the tissue image may be a pre-compression image used by the processing unit 122 to help determine a target clamping force for the jaw assembly. The tissue image may also be an image of compressed tissue between the first jaw member 108 and the second jaw member 110 and used by the processing unit 122 to help determine, modulate or adjust a parameter of the jaw assembly, such as a target clamping force applied to the compressed tissue.

[0036] Also, mechanical or electrical properties of tissue may be measured by one or more sensors 114 to help establish, adjust, or modulate parameters of the jaw assembly. In one example, the electrosurgical system may include or use spectroscopy or ionization circuitry that may be used to help determine the composition of the tissue. Optical sensing circuitry may also be used, for example, at one or more IR wavelengths or in conjunction with dyes or fluorescence, to help visualize the tissue to the sensor 114.

[0037] As previously mentioned, the sensor 114 may be a camera capable of capturing tissue images, where the tissue images may include device-recognizable features of the tissue. For example, the camera may provide a real-time, in-line, or other simultaneous video surveillance feed to the surgeon, and frames of the feed may be used to capture device-recognizable features of the tissue. The video surveillance feed may be viewed on the display 126 during the procedure. The video surveillance feed may include or use one or more visual, tactile, or audible indicators to provide feedback to the surgeon during the procedure. The indicators may provide feedback to the surgeon to assist the surgeon in manually applying a target clamping force using the end effector assembly 104. Additionally or alternatively, the device-recognizable features may be used by the processing unit 122 to assist in automatically applying a target clamping force using the end effector assembly 104.

[0038] In one example, data from the sensor 114 may be used by the seal assessment circuitry to ensure a desired seal of the tissue after treatment. For example, data from the sensor 114, such as oximeter data or vascular color data, may be used to indicate the presence of hemoglobin in the tissue. In one example, the seal assessment circuitry may include or use a processor to automatically consider one or more characteristics corresponding to a desired seal, the characteristics being determined from the data. For example, a surgeon or operator may be alerted by the system when a desired tissue characteristic is not detected after a treatment session, if a characteristic may be determined. Also, a surgeon or operator may be alerted by the system after a treatment session if one or more undesirable tissue characteristics are detected.

[0039] FIG. 3 shows a block diagram of one example of a machine 300 in which one or more of the methods discussed herein may be implemented. In one or more examples, one electrosurgical system 100 may be implemented by the machine 300. In alternative examples, the machine 300 may operate as a standalone device or may be connected (e.g., networked) to other machines. In one or more examples, the electrosurgical system 100 may include one or more of the items of the machine 300. In a networked deployment, the machine 300 may operate in the capacity of a server or a client machine in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), tablet PC, set-top box (STB), personal digital assistant (PDA), mobile phone, web appliance, network router, switch or bridge, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by the machine. Additionally, although only a single machine is shown, the term "machine" shall be taken to include any collection of machines that, independently or jointly, execute a set of instructions to perform any one or more of the methodologies discussed herein.

[0040] The exemplary machine 300 includes a processor 302 (e.g., a CPU, GPU, ASIC, circuitry, or combination thereof, such as one or more transistors, resistors, capacitors, inductors, diodes, logic gates, multiplexers, buffers, modulators, demodulators, radios (e.g., transmitting or receiving radios, or transceivers), sensors 321 (e.g., converters that convert one form of energy (e.g., light, heat, electrical, mechanical, or other energy) into another form of energy), main memory 304, and static memory 306, which communicate with each other via a bus 308. The machine 300 (e.g., a computer system) may further include a video display unit 310 (e.g., a liquid crystal display (LCD) or a cathode ray tube (CRT)). The machine 300 also includes an alphanumeric input device 312 (e.g., a keyboard), a user interface (UI) navigation device 314 (e.g., a mouse), a disk drive or mass storage unit 316, a signal generating device 318 (e.g., a speaker), and a network interface device 320.

[0041] The disk drive or mass storage unit 316 includes a machine-readable medium 322 on which is stored one or more sets of instructions and data structures (e.g., software) 324 that embody or are used by one or more of the methodologies or functions described herein. The instructions 324 may also reside, completely or at least partially, within the main memory 304 or within the processor 302 during their execution by the machine 300, with the main memory 304 and the processor 302 also constituting machine-readable media.

[0042] The illustrated machine 300 includes an output controller 328. The output controller 328 manages the flow of data to and from the machine 300. The output controller 328 is often called a device controller, and the software that interacts directly with the output controller 328 is called a device driver.

[0043] While the machine-readable medium 322 is shown in one example as being a single medium, the term "machine-readable medium" may include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) that store one or more instructions or data structures. The term "machine-readable medium" should also be interpreted to include any tangible medium that can store, encode, or carry instructions for execution by a machine and cause the machine to perform any one or more of the methodologies of the present disclosure, or that can store, encode, or carry data structures used by or associated with such instructions. Thus, the term "machine-readable medium" should be interpreted to include, but is not limited to, solid-state memory, as well as optical and magnetic media. Particular examples of machine-readable media include non-volatile memory, including, by way of example, semiconductor memory devices, e.g., erasable programmable read-only memory (EPROM), EEPROM, and flash memory devices, magnetic disks such as internal hard disks and removable disks, magneto-optical disks, and CD-ROM and DVD-ROM disks.

[0044] The instructions 324 may further be transmitted or received over a communications network 326 using a transmission medium. The instructions 324 may be transmitted using the network interface device 320 and any one of several well-known transfer protocols (e.g., HTTP). Examples of communications networks include LANs, WANs, the Internet, cellular networks, plain old telephone (POTS) networks, and wireless data networks (e.g., WiFi and WiMax networks). The term "transmission medium" shall be taken to include any intangible medium capable of storing, encoding, or carrying instructions for execution by a machine, including digital or analog communications signals, or other intangible media that facilitate communication of such software.

[0045] FIG. 4 is a flow chart of a method of using an example of an electrosurgical system including a jawed electrosurgical device. In one example, a method 400 of treating tissue may be performed using one of several electrosurgical systems described herein. At 402, first and second jaw members of an end effector assembly of a medical device may be articulated between a first position and a second position. At 404, tissue may be compressed between the first and second jaw members of the end effector assembly. At 406, at least one of the first and second jaw members may be energized using an electrosurgical energy source. At 408, the compressed tissue may be inspected using a sensor. At 410, a target clamping force of the end effector assembly may be determined using image data from the sensor.

[0046] FIG. 5 is a flow chart of a method of using an example of a general electrosurgical system. In an example, a method 500 of treating tissue may be performed using one of several electrosurgical systems as described herein. At 502, imaging information may be received from a location internal to a human or animal subject, such as via an imaging sensor. Imaging information may also be received from a location external to the human or animal subject. At 504, a structure or other characteristic at or near an internal or external location of the subject may be determined based on the imaging information. At 506, a parameter or algorithm, such as for controlling a tissue treatment output, may be adjusted based at least in part on the imaging information. At 508, a tissue treatment may be applied to tissue at the internal or external location of the subject, such as via a tissue treatment output. For example, adjusting a parameter or algorithm may include adjusting a target clamping force of a jaw assembly of an end effector, such as for sealing, cauterizing, or cutting, based at least in part on the imaging information. Adjusting a parameter or algorithm may also include adjusting an energy waveform, such as for sealing, cauterizing, or cutting, based at least in part on the imaging information. For example, adjusting the parameters or algorithms may include or use adjusting the electrosurgical energy signal based at least in part on the imaging information. In some examples, adjusting the energy waveform may include adjusting the energy waveform for any type of energy-based tissue treatment, including devices for ablation, desiccation, sectioning, or ultrasonic therapy. Adjusting the energy waveform is not limited to forceps-type devices with jaws or bipolar functionality. For example, adjusting the energy waveform based on imaging information may be applied to any electrosurgical device, including, but not limited to, any other bipolar or monopolar device, such as a spatula, Bovie, scalpel, etc.

[0047] The systems described herein can adjust the output delivered to the treatment device based on one or more inputs received from one or more sensors (e.g., image information from an image sensor, force information from a force sensor, electrical information from an electrical sensor), and can adjust one or more of the energy-related output and the force-related output. In the illustrative example provided, the energy output is shown as radio frequency energy delivered to the electrodes of the forceps, but can also include ultrasonic, microwave, electromagnetic, resistive, thermal, laser energy in forceps or other types of treatment devices. In the illustrative example, the force can be applied to a mechanism including compression, displacement, pressure, such as that caused by the closing of jaws in forceps, by movement by a user in a manual device, or by a drive in a robotic device.

[0048] Notes and Examples The following non-limiting examples detail particular aspects of the present subject matter that, among other things, solve the problems and provide the benefits discussed herein.

[0049] Embodiment 1 is a system for imaging and treating tissue, the system comprising an imaging sensor adapted to receive imaging information from an internal location of a human or animal subject, a tissue treatment output for applying tissue treatment to tissue at the internal location of the subject, and a controller circuit comprising signal processing circuitry configured to image process the imaging information to determine structures or other characteristics at or near the internal location of the subject and adjust parameters or algorithms that control the tissue treatment output based at least in part on the imaging information.

[0050] In embodiment 2, the subject matter of embodiment 1, wherein the imaging sensor includes a camera.

[0051] In embodiment 3, the subject matter of embodiment 2, wherein the controller circuit is configured to generate a tissue image of the tissue at an internal location of the subject.

[0052] In embodiment 4, the subject matter of any of embodiments 1 to 3 further comprises an end effector including a tissue treatment output.

[0053] In aspect 5, the subject matter of aspect 4, wherein the end effector includes a forceps having a jaw assembly including a first jaw member and a second jaw member pivotally coupled to the first jaw member, the jaw assembly being movable between a first position in which the first and second jaw members are spaced apart from one another and a second position in which the first and second jaw members are disposed closer to one another than the first position, and at least one of the first and second jaw members includes a tissue treatment output.

[0054] In embodiment 6, the subject matter of embodiment 5, wherein the parameters include a target clamping force of the jaw assembly, and the controller circuit is configured to establish or adjust the target clamping force using the imaging information.

[0055] In aspect 7, the subject matter of any of aspects 5 to 6, wherein the controller circuit is configured to provide at least one of a control signal to the end effector assembly, or a user alert to a user controlling the end effector assembly, based at least in part on the imaging information.

[0056] In aspect 8, the subject matter of any of aspects 5 to 7, wherein the controller circuit is configured to recognize one or more features from a tissue image of tissue compressed between the first and second jaw members, the recognized one or more features of the tissue including a width of the tissue compressed between the first and second jaw members, the width of the tissue being a measurement of the tissue contacting the jaw members between respective proximal and respective distal portions of the jaw members.

[0057] In aspect 9, the subject matter of any of aspects 5 to 8, wherein the controller circuit is configured to recognize one or more features from a tissue image of tissue compressed between the first and second jaw members, and the recognized one or more features of the tissue include an indication of an amount of tissue compressed between the first and second jaw members.

[0058] In aspect 10, the subject matter of any of aspects 5 to 9, wherein the controller circuit is configured to recognize one or more features from a tissue image of tissue compressed between the first and second jaw members, and the recognized one or more features of the tissue include an indication of an approximate diameter of a vessel compressed between the first and second jaw members.

[0059] In aspect 11, the subject matter of any of aspects 5 to 10, wherein the controller circuit includes an image processing circuit configured to use the tissue image to determine a contact area of ​​tissue compressed between the first jaw member and the second jaw member.

[0060] In aspect 12, the subject matter of any of aspects 1 to 11, wherein the parameters include energy characteristics of the tissue treatment, and the controller circuit is configured to modulate the energy characteristics of the tissue treatment using the imaging information.

[0061] In aspect 13, the subject matter of any of aspects 1 to 12, wherein the controller circuit is configured to modulate a parameter, including modulating the electrosurgical energy signal based at least in part on imaging information, including imaging information from a location of the treatment target structure.

[0062] In aspect 14, the subject matter of any of aspects 1 to 13, wherein the controller circuit is configured to recognize one or more features from imaging information including a tissue image of tissue at an internal location of the subject, and establish or adjust the electrosurgical energy signal delivered by the tissue treatment output based at least in part on the recognized one or more features from the imaging information including the tissue image of the tissue.

[0063] Embodiment 15 is a system for treating tissue, the system comprising: an imaging sensor adapted to receive imaging information from an internal location of a human or animal subject; a tissue treatment output for applying a tissue treatment to tissue at the internal location of the subject; and a controller circuit comprising signal processing circuitry configured to image process the imaging information to determine structures or other characteristics at or near the internal location of the subject and adjust a tissue treatment compressive force parameter that controls the tissue treatment output based at least in part on the imaging information.

[0064] In embodiment 16, the imaging sensor comprises a camera.

[0065] In aspect 17, the subject matter of any of aspects 15 to 16 includes an end effector including a jaw assembly, and the compressive force parameter includes a target clamping force of the jaw assembly.

[0066] Aspect 18 is a method for treating tissue that includes receiving imaging information from an internal location of a human or animal subject via an imaging sensor; determining a structure or other characteristic at or near the internal location of the subject based on the imaging information; adjusting a parameter or algorithm based at least in part on the imaging information, the parameter or algorithm controlling the tissue treatment output; and applying a tissue treatment to the tissue at the internal location of the subject via the tissue treatment output.

[0067] In embodiment 19, the subject matter of embodiment 18, wherein the imaging sensor includes a camera.

[0068] In aspect 20, the subject matter of any of aspects 18 to 19, wherein adjusting the parameters or algorithms includes adjusting a target clamping force of a jaw assembly of the end effector for at least one of sealing, cauterizing, or cutting based at least in part on the imaging information.

[0069] In aspect 21, the subject matter of any of aspects 18 to 20, wherein adjusting the parameters or algorithms includes adjusting the electrosurgical energy signal based at least in part on the imaging information.

[0070] Embodiment 22 is a system comprising a visualization or optical imaging component adapted to receive visualization or optical imaging information from an internal location of a human or animal subject, a tissue treatment output for applying energy to tissue at the internal location of the subject, and a controller circuit comprising signal processing circuitry configured to image process the visualization or optical imaging information to train or use a trained model for image recognition to determine structures or other characteristics at or near the internal location of the subject, and adjust parameters or algorithms that control the tissue treatment output based at least in part on the visualization or optical imaging information.

[0071] In aspect 23, the subject matter of aspect 22, further comprising an electrical tissue property sensor arranged to sense electrical properties of tissue located at or near an internal location of the subject, and the controller circuitry configured to control tissue treatment output based at least in part on information regarding the sensed electrical properties.

[0072] In aspect 24, the subject matter of any of aspects 22 to 23, wherein the tissue treatment output further includes a mechanical device for assisting in applying energy to tissue at an internal location of the subject, and the controller circuit is further configured to provide at least one of a control signal to the mechanical device or a user alert to a user controlling the mechanical device based at least in part on the visualization or optical imaging information.

[0073] In embodiment 25, the subject matter of any of embodiments 22-24, wherein the controller circuit is configured to control tissue treatment output based at least in part on visualization or optical imaging information from the location of the treatment target structure.

[0074] In embodiment 26, the subject matter of any of embodiments 22 to 25, wherein the controller circuit is configured to control tissue treatment output based at least in part on visualization or optical imaging information from the location of the treatment avoidance structure.

[0075] Example 27 is a method of using the system of example 22 to control therapy delivery or provide user alerts.

[0076] Example 28 is a device-readable medium comprising instructions for performing the method of example 27.

[0077] Example 29 is a device-readable medium including a trained neural network, machine learning, or other model for performing the method of example 27, where the model uses visualization or optical imaging information.

[0078] Embodiment 30 is a system for treating tissue comprising: an end effector assembly of a medical device, the end effector assembly comprising a jaw assembly including a first jaw member and a second jaw member pivotally coupled to the first jaw member, the jaw assembly being movable between a first position in which the first and second jaw members are spaced apart from one another and a second position in which the first and second jaw members are disposed closer to one another, at least one of the first and second jaw members adapted to connect to a source of electrosurgical energy; a sensor configured to inspect tissue; and a processing unit configured to receive data from the sensor and modulate parameters of the jaw assembly using the data.

[0079] In embodiment 31, the parameter of the jaw assembly is a target clamping force.

[0080] In embodiment 32, the subject matter of any of embodiments 30 to 31, wherein the parameter of the jaw assembly is a frequency or current of electrosurgical energy from the electrosurgical energy source.

[0081] In embodiment 33, the subject matter of any of embodiments 30-32, wherein the tissue is compressed between the first and second jaw members for examination.

[0082] In embodiment 34, the subject matter of any of embodiments 30 to 33, wherein the sensor is a camera.

[0083] In aspect 35, the subject matter of aspect 34, wherein the processing unit is configured to generate a tissue image of the tissue compressed between the first jaw member and the second jaw member, the tissue image including device-recognizable features of the tissue.

[0084] In embodiment 36, the subject matter of embodiment 35, wherein the device-recognizable characteristic of the tissue includes an amount of tissue compressed between the first jaw member and the second jaw member.

[0085] In aspect 37, the subject matter of any of aspects 35 to 36, wherein the device-recognizable characteristics of the tissue include a width of the tissue compressed between the first jaw member and the second jaw member, the width of the tissue being a measurement of the tissue contacting the jaw members between the proximal ends and distal ends of each of the jaw members.

[0086] In embodiment 38, the subject matter of any of embodiments 35 to 37, wherein the device-recognizable feature of the tissue includes an approximated diameter of a vessel compressed between the first jaw member and the second jaw member.

[0087] In embodiment 39, the subject matter of any of embodiments 35 to 38, wherein the processing unit includes a memory circuit including instructions that, when executed by the at least one processor circuit, cause the processing unit to perform an inspection operation using the sensor.

[0088] In aspect 40, the subject matter of aspect 39, wherein at least one processor circuit includes an image processing circuit configured to determine a contact area of ​​tissue compressed between the first jaw member and the second jaw member using the tissue image and a trained machine learning model.

[0089] In aspect 41, the subject matter of any of aspects 39 to 40, wherein at least one processor circuit includes an image processing circuit configured to determine a contact area of ​​tissue compressed between the first jaw member and the second jaw member using tissue images and an automated algorithm.

[0090] In aspect 42, the subject matter of any of aspects 39 to 41, wherein at least one processor circuit includes an image processing circuit configured to use the tissue image and a trained machine learning model to determine a cross-sectional diameter of at least one vessel contained within the tissue compressed between the first jaw members.

[0091] In aspect 43, the subject matter of any of aspects 39 to 42, wherein at least one processor circuit includes an image processing circuit configured to determine a cross-sectional diameter of at least one vessel contained within the tissue compressed between the first jaw members using the tissue image and an automated algorithm.

[0092] Example 44 is a system for treating tissue comprising an end effector assembly of a medical device, the end effector assembly including a jaw assembly including a first jaw member and a second jaw member pivotally coupled to the first jaw member, the jaw assembly being movable between a first position in which the first and second jaw members are spaced apart from one another and a second position in which the first and second jaw members are disposed closer to one another, at least one of the first and second jaw members adapted to connect to a source of electrosurgical energy; a sensor configured to examine tissue compressed between the first and second jaw members; and a processing unit configured to receive image data from the sensor and use the data to determine a target clamping force for the jaw assembly.

[0093] Example 45 is a method for treating tissue including articulating an end effector assembly of a medical device between a first position in which first and second jaw members of the assembly are spaced apart from one another and a second position in which the first and second jaw members are disposed closer to one another; compressing tissue between the first and second jaw members of the end effector assembly; energizing at least one of the first and second jaw members using an electrosurgical energy source; inspecting the compressed tissue using a sensor; and determining a target clamping force for the end effector assembly using image data from the sensor.

[0094] In embodiment 46, the subject matter of embodiment 45, wherein the sensor is a camera.

[0095] In aspect 47, the subject matter of aspect 46, wherein the step of determining a target clamping force of the end effector assembly using image data from the sensor includes the step of generating a tissue image including device-recognizable features of the tissue.

[0096] In embodiment 48, the subject matter of embodiment 47, wherein the device-recognizable characteristic of the tissue includes an amount of tissue compressed between the first jaw member and the second jaw member.

[0097] In aspect 49, the subject matter of aspect 48, wherein the device-recognizable characteristics of the tissue include a width of the tissue compressed between the first jaw member and the second jaw member, the width of the tissue being a measurement of the tissue contacting the jaw members between the proximal ends and distal ends of each of the jaw members.

[0098] Embodiment 50 is at least one machine-readable medium comprising instructions that, when executed by a processing circuit, cause the processing circuit to perform operations to implement any of embodiments 1-49.

[0099] Aspect 51 is an apparatus comprising means for implementing any of aspects 1 to 49.

[0100] Example 52 is a system that implements any of examples 1 to 49.

[0101] Example 53 is a method of implementing any of examples 1 to 49.

[0102] The above detailed description includes references to the accompanying drawings, which form a part of the detailed description. The drawings show, by way of illustration, specific embodiments in which the invention may be practiced. These embodiments are also referred to herein as "examples." Such examples may include elements in addition to those shown or described. However, the inventors also contemplate examples in which only those elements shown or described are provided. Furthermore, the inventors also contemplate examples that use any combination or permutation of those illustrated or described elements (or one or more aspects thereof) with respect to the particular example (or one or more aspects thereof) or any other example (or one or more aspects thereof) shown or described herein.

[0103] In the event of a conflict in usage between this document and any document incorporated by reference, the usage in this document shall prevail. In this document, the terms "including" and "in which" are used as the plain English equivalents of the respective terms "comprising" and "wherein." Also, in the following claims, the terms "including" and "comprising" are open-ended, i.e., a system, device, article, composition, formulation, or process that includes elements in addition to those listed after such term in the claim will still be considered within the scope of that claim.

[0104] In this document, the terms "a" or "an" are used to include one or more, as is common in patent documents, regardless of any other instances or usages of "at least one" or "one or more." In this document, the term "or" is used to refer to a non-exclusive or, such as "A or B" including "A but not B," "B but not A," and "A and B," unless otherwise indicated. In this document, the terms "including" and "in which" are used as the plain English equivalents of the respective terms "comprising" and "wherein." Also, in the following claims, the terms "comprising" and "comprising" are open-ended, i.e., a system, device, article, composition, formulation, or process that includes elements in addition to the elements listed after such terms in the claim will still be considered within the scope of the claim. Moreover, in the following claims, terms such as "first," "second," and "third" are used merely as labels and are not intended to impose numerical requirements on their objects.

[0105] The above description is intended to be illustrative and not restrictive. For example, the examples described above (or one or more aspects thereof) may be used in combination with each other. Other embodiments may be used by those of ordinary skill in the art who have reviewed the above description. The Abstract is provided to enable the reader to quickly ascertain the nature of the technical disclosure. The Abstract is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. Also, in the above Detailed Description, various features may be grouped together to streamline the disclosure. This should not be construed as intending that an unclaimed disclosed feature is essential to any claim. Rather, inventive subject matter may lie in less than all features of a particular disclosed embodiment. Accordingly, the following claims are hereby contemplated as each claim standing alone as a separate embodiment in itself, and as an example or embodiment, and such embodiments may be combined with each other in various combinations or permutations. The scope of the present invention should be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. [Explanation of symbols]

[0106] 100 Electrosurgical system, system, adaptive system 102 Medical devices, devices 104 End effector assembly, assembly 106 Jaw Assembly 108 first jaw member, jaw member, jaw 109 Tissue sealing plate 110 second jaw member, jaw member, jaw 111 active electrode, electrode 112 Pivot point 114 Sensors, force sensors, electric sensors, cameras 115 Light source 116 Shaft 118 Handle, handpiece 119 Drive unit 120 Actuator 122 Processing Unit 124 Blood vessels or other target objects 300 Machines 302 Processor 304 Main Memory 306 Static Memory 308 Bus 310 Video Display Unit 312 Alphanumeric Input Device 314 User Interface (UI) Navigation Devices 316 Disk drive or mass storage unit 318 Signal Generating Devices 320 Network Interface Device 321 Sensors 322 Machine-Readable Media 324 Instructions and data structures (e.g. software), instructions 326 Communication Network 328 Output Controller

Claims

1. 1. A system for imaging and treating tissue, comprising: an imaging sensor adapted to receive imaging information from a location within a human or animal subject; an end effector including a tissue treatment output for applying tissue treatment to tissue at the location within the human or animal subject, the end effector comprising: A first jaw member; a second jaw member pivotally coupled to the first jaw member; a jaw assembly comprising: the jaw assembly is movable between a first position in which the first and second jaw members are spaced apart from one another and a second position in which the first and second jaw members are disposed closer to one another than in the first position; an end effector, at least one of the first and second jaw members including the tissue treatment output; a controller circuit including signal processing circuitry configured to image-process the imaging information to determine structures or other characteristics at or near the location within the human or animal subject, and to adjust parameters or algorithms that control the tissue treatment output based at least in part on the imaging information; A system comprising:

2. The system of claim 1 , wherein the imaging sensor comprises a camera.

3. The system of claim 2 , wherein the controller circuitry is configured to generate a tissue image of the tissue at the location within the human or animal subject.

4. the parameters include a target clamping force for the jaw assembly; The system of claim 1 , wherein the controller circuit is configured to use the imaging information to establish or adjust the target clamping force.

5. 13. The system of claim 1, wherein the controller circuit is configured to provide at least one of a control signal to the jaw assembly or a user alert to a user controlling the jaw assembly based at least in part on the imaging information.

6. The controller circuitry comprises: configured to recognize one or more features from a tissue image of the tissue compressed between the first and second jaw members; 2. The system of claim 1, wherein the recognized one or more characteristics of the tissue include a width of tissue compressed between the first and second jaw members, the width of tissue being a measurement of tissue contacting the first and second jaw members between respective proximal and distal portions of the first and second jaw members.

7. The controller circuitry comprises: configured to recognize one or more features from a tissue image of the tissue compressed between the first and second jaw members; The system of claim 1 , wherein the recognized one or more characteristics of the tissue include an indication of an amount of tissue compressed between the first jaw member and the second jaw member.

8. The controller circuitry comprises: configured to recognize one or more features from a tissue image of the tissue compressed between the first and second jaw members; The system of claim 1 , wherein the recognized one or more characteristics of the tissue include an indication of an approximate diameter of a vessel compressed between the first jaw member and the second jaw member.

9. 2. The system of claim 1, wherein the controller circuit includes image processing circuitry configured to use the imaging information to determine a contact area of ​​the tissue compressed between the first jaw member and the second jaw member.

10. the parameters include an energy characteristic of the tissue treatment; The system of claim 1 , wherein the controller circuitry is configured to use the imaging information to modulate the energy characteristics of the tissue treatment.

11. The system of claim 1, wherein the controller circuit is configured to modulate the parameters, including modulating the electrosurgical energy signal based at least in part on the imaging information, including imaging information from a location of a treatment target structure.

12. The controller circuitry comprises: Recognizing one or more features from the imaging information including a tissue image of the tissue at the location within the human or animal subject; The system of claim 1, configured to establish or adjust an electrosurgical energy signal delivered by the tissue treatment output based at least in part on the recognized one or more features from the imaging information including the tissue image of the tissue.

13. 1. A system for treating tissue, comprising: an imaging sensor adapted to receive imaging information from a location within a human or animal subject; a tissue treatment output for applying tissue treatment to tissue at said location within said human or animal subject; a controller circuit including signal processing circuitry configured to image-process the imaging information to determine structures or other characteristics at or near the location within the human or animal subject, and to adjust tissue treatment compressive force parameters that control the tissue treatment output based at least in part on the imaging information; A system comprising:

14. The system of claim 13 , wherein the imaging sensor comprises a camera.

15. The system of claim 13 , comprising an end effector including a jaw assembly, and wherein the tissue treatment compressive force parameters include a target clamping force of the jaw assembly.

16. A system comprising: a visualization or optical imaging component adapted to receive visualization or optical imaging information from a location within a human or animal subject; a tissue treatment output for applying energy to tissue at an internal location of the human or animal subject; a controller circuit comprising signal processing circuitry configured to image-process the visualization or optical imaging information to train or use a trained model for image recognition to determine structures or other characteristics at or near the location within the human or animal subject, and to adjust parameters or algorithms that control the tissue treatment output based at least in part on the visualization or optical imaging information; an electrical tissue property sensor arranged to sense an electrical property of tissue located at or near said location within said human or animal subject; wherein the controller circuitry is configured to control the tissue treatment output based at least in part on information related to the sensed electrical characteristic.

17. 17. The system of claim 16, wherein the tissue treatment output further includes a mechanical device to assist in applying the energy to the tissue at the location within the human or animal subject, and the controller circuitry is further configured to provide at least one of a control signal to the mechanical device or a user alert to a user controlling the mechanical device based at least in part on the visualization or optical imaging information.

18. 17. The system of claim 16, wherein the controller circuitry is configured to control the tissue treatment output based at least in part on visualization or optical imaging information from a location of a treatment target structure.

19. 17. The system of claim 16, wherein the controller circuitry is configured to control the tissue treatment output based at least in part on visualization or optical imaging information from a location of a treatment avoidance structure.

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