Soft tissue intelligent recognition method and device, and soft tissue cutting and sealing device

Abstract

The application discloses a soft tissue intelligent recognition method and device and a soft tissue cutting and sealing device.The ultrasonic knife trigger controls the jaw to clamp the soft tissue, and an initial current I0 is released at the same time.The jaw clamps the tissue, and the trigger stroke and the current passing through the soft tissue at different moments are collected in the process that the soft tissue is extruded and deformed.According to the Kelvin model, the elasticity modulus E and the viscosity coefficient η of the soft tissue are inversely solved.A stroke sensor is installed on the trigger to capture the rotation angle of the trigger.A monitoring button is designed to manually control the micro current to be turned on immediately after the operation starts, and the micro current can also be automatically turned on as soon as the handle trigger is started.According to the linear relationship between the current and the stress of the soft tissue, the elasticity modulus and the viscosity coefficient are inversely solved based on the Darwin model, and then the tissue type is recognized, so that the operation gear of the ultrasonic knife does not need to be selected artificially, and the safety and efficiency of the operation are improved.

Description

Technical Field

[0001] This invention relates to the field of ultrasonic scalpels, and particularly to a method, apparatus, and soft tissue cutting and sealing device for intelligent identification of soft tissue. Background Technology

[0002] Currently, soft tissue cutting and coagulation rely on doctors actively identifying whether the tissue is a blood vessel. For example, if the target soft tissue is a blood vessel, the ultrasonic scalpel is set to level three; if cutting other soft tissues, level five is used. Existing technology mainly relies on the doctor's visual recognition for selective work. However, in some environments, blood vessels and other tissues are obscured and difficult to identify. If a doctor uses a level five ultrasonic scalpel to rupture a blood vessel, it can lead to bleeding or even blood loss in the patient. In the context of intelligent medical robots, there is a greater need for an intelligent method that eliminates the need for selecting levels to adapt to the requirements of unmanned operation. Summary of the Invention

[0003] The purpose of this invention is to provide a method, device, and cutting and sealing device for intelligent soft tissue identification. Based on the Kelvin model, the elastic modulus and viscosity coefficient of the tissue can be identified to determine the type of tissue. Then, based on the tissue type, the cutting or sealing level can be intelligently selected to meet the needs of unmanned operation.

[0004] The technical solution of this invention is:

[0005] The soft tissue intelligent recognition method includes the following steps:

[0006] (1) The ultrasonic scalpel trigger controls the jaws to clamp the soft tissue while simultaneously releasing the initial current I0;

[0007] (2) During the process of clamping the tissue with clamps and compressing and deforming the soft tissue, t1, t2, ... t are collected respectively. n Trigger travel at specific moments S1, S2, ... S n And the currents I1, I2, ... I through soft tissue n ;

[0008] (3) Let σ = mI Δ = m(I t - I t-1 ), γ=nS Δ =n(S) t -S t-1 According to the Kelvin model:

[0009] ,

[0010] The elastic modulus E and viscosity coefficient η of the soft tissue are obtained by inverse solving, where σ is stress, γ is strain, t is time, and m and n are constants determined by the mechanical system.

[0011] (4) Identify the tissue type based on the elastic modulus E and viscosity coefficient η of the soft tissue.

[0012] Preferably, the method for reversing the elastic modulus E and viscosity coefficient η of the soft tissue in step (3) is as follows:

[0013] (3-1) According to γ=nS Δ =n(S) t -S t-1 ), at any time t i have:

[0014] ;

[0015] (3-2) According to σ=mI Δ = m(I t - I t-1 ) and the Kelvin model, at any time t in the entire process i have:

[0016] m( ) = E·n( )+η·n ;

[0017] (3-3) During the entire process of soft tissue compression and deformation, t n At any given moment, there are n-1 equations. Solving any two of these equations simultaneously will yield E and η.

[0018] Soft tissue intelligent recognition device, including:

[0019] The ultrasonic scalpel trigger controls the jaws to grip soft tissue while simultaneously releasing an initial current I0;

[0020] The travel sensor collects data at t1, t2, ... t during the deformation of soft tissue by the jaws of the clamp. n Trigger travel at specific moments S1, S2, ... S n ;

[0021] The current acquisition module collects data at t1, t2, ... t during the deformation of soft tissue by the clamp jaws. n Currents I1, I2, ... I passing through the soft tissue at all times n ;

[0022] Calculation unit, let σ=mI Δ = m(I t - I t-1 ), γ=nS Δ =n(S) t -S t-1 According to the Kelvin model:

[0023] ,

[0024] The elastic modulus E and viscosity coefficient η of the soft tissue are obtained by inverse solving, where σ is stress, γ is strain, t is time, and m and n are constants determined by the mechanical system.

[0025] The identification module identifies the tissue type based on the elastic modulus E and viscosity coefficient η of the soft tissue.

[0026] Preferably, the process by which the calculation unit inversely solves for the elastic modulus E and viscosity coefficient η of the soft tissue is as follows:

[0027] According to γ=nS Δ =n(S) t -S t-1 ), at any time t i have:

[0028] ;

[0029] According to σ=mI Δ = m(I t - I t-1 ) and the Kelvin model, at any time t in the entire process i have:

[0030] m( ) = E·n( )+η·n ;

[0031] During the entire process of soft tissue compression and deformation, t n At any given moment, there are n-1 equations. Solving any two of these equations simultaneously will yield E and η.

[0032] Preferably, t1, t2, ... t n The time interval between moments is one unit, or in the evolution algorithm, the time interval can be any number of units.

[0033] Preferably, the initial current I0 is set by a program, and I1~I n It is captured by circuit feedback.

[0034] A computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the soft tissue intelligent recognition method.

[0035] A soft tissue cutting and sealing device, comprising:

[0036] The monitoring button is activated when the main unit enters the surgical state, thereby starting the initial current.

[0037] A soft tissue intelligent recognition device is used to identify soft tissue types.

[0038] Ultrasonic scalpel: Select the cutting or coagulation setting based on the type of soft tissue.

[0039] Preferably, when the monitoring button is turned on, it is activated by a separate button, or it is activated while the ultrasonic scalpel trigger is closed.

[0040] Preferably, the monitoring button is a physical object or a signal in the control system.

[0041] Preferably, during the closing process of the ultrasonic scalpel trigger of the soft tissue intelligent recognition device, three states occur:

[0042] Phase 1: The jaws do not clamp the tissue, the tissue does not strain, and the current does not change.

[0043] Second stage: The jaws begin to clamp the tissue, the tissue is squeezed and deformed, and the current becomes larger and larger;

[0044] Third stage: After the tissue is squeezed and deformed, the trigger continues to move for a certain distance to completely clamp the tissue, and the current increases slightly;

[0045] The process of identifying soft tissue types is in the second stage of ultrasonic scalpel trigger closure.

[0046] The advantages of this invention are:

[0047] The soft tissue intelligent identification method, device, and soft tissue cutting and sealing device of the present invention, based on the linear relationship between soft tissue current and stress and the Kelvin model, inversely solves the elastic modulus and viscosity coefficient, thereby identifying the tissue type. This eliminates the need for manual selection of the ultrasonic scalpel surgical setting, improving the safety and efficiency of the surgery. Attached Figure Description

[0048] The present invention will be further described below with reference to the accompanying drawings and embodiments:

[0049] Figure 1 This is a schematic diagram of the Kelvin model;

[0050] Figure 2 A flowchart of a soft tissue intelligent recognition method;

[0051] Figure 3 This diagram illustrates the three states of soft tissue during the closure of the ultrasonic scalpel trigger:

[0052] Figure 4 A block diagram of a soft tissue intelligent recognition device;

[0053] Figure 5 A block diagram of a soft tissue cutting and closure device. Detailed Implementation

[0054] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] Every type of soft tissue has its specific characteristics. As a viscoelastic material, the characteristics of soft tissue are determined by its elastic modulus E and viscosity coefficient n. For example... Figure 1 As shown, according to the Kelvin model, under external force clamping, its mathematical relationship is:

[0056] ;

[0057] In the formula, σ is stress, E is elastic modulus, η is viscosity coefficient, γ is strain, and t is time.

[0058] According to the Kelvin model, it is only necessary to identify the elastic modulus E and viscosity coefficient η of the tissue to determine what kind of tissue it is.

[0059] Example 1

[0060] like Figure 2 As shown, based on the Kelvin model, this invention proposes a soft tissue intelligent recognition method, including the following steps:

[0061] (1) The ultrasonic scalpel trigger controls the jaws to clamp the soft tissue while simultaneously releasing the initial current I0;

[0062] (2) During the closing of the ultrasonic scalpel trigger, the jaws are rotated by the handle, such as Figure 3 As shown, three states occur:

[0063] Phase 1: The jaws do not clamp the tissue, the tissue does not strain, and the current does not change.

[0064] Second stage: The jaws begin to clamp the tissue, the tissue is squeezed and deformed, and the current becomes larger and larger;

[0065] Third stage: After the tissue is squeezed and deformed, the trigger continues to move for a certain distance to completely clamp the tissue, and the current increases slightly;

[0066] Figure 3 The current curve in the display is replaced by a straight line, and the display may show jitter.

[0067] The process of identifying soft tissue types occurs during the second stage of ultrasonic scalpel trigger closure. During the compression and deformation of the soft tissue, t1, t2, ... t are collected respectively. n Trigger travel at specific moments S1, S2, ... S n And the currents I1, I2, ... I through soft tissue n ;

[0068] (3) Let σ = mI Δ = m(I t - I t-1 ), γ=nS Δ =n(S) t -S t-1 According to the Kelvin model:

[0069] ,

[0070] The elastic modulus E and viscosity coefficient η of the soft tissue are obtained by inverse solving, where σ is stress, γ is strain, t is time, and m and n are constants determined by the mechanical system. Specific inverse solving methods include:

[0071] (3-1) According to γ=nS Δ =n(S) t -S t-1 ), at any time t i have:

[0072] ;

[0073] (3-2) According to σ=mI Δ = m(I t - I t-1 ) and the Kelvin model, at any time t in the entire process i have:

[0074] m( - ) = E·n( - )+η·n ;

[0075] (3-3) During the entire process of soft tissue compression and deformation, t n At any given moment, there are n-1 equations. Solving any two of these equations simultaneously will yield E and η. For example:

[0076] m( - ) = E·n( - )+η·n· ;

[0077] m( - ) = E·n( - )+η·n· ;

[0078] In this system of equations, the only unknowns are E and η; n-1 equations can form n-2 systems of equations, which can be solved inversely to obtain n-2 sets of E and η. They cannot be completely identical, but this is more helpful in analyzing what kind of organization this is.

[0079] (4) Identify the tissue type based on the elastic modulus E and viscosity coefficient η of the soft tissue.

[0080] In this embodiment, t1, t2, ... t n The time interval between moments is one unit, or in the evolution algorithm, the time interval can be any number of units.

[0081] Example 2

[0082] This embodiment proposes a soft tissue intelligent recognition device, such as... Figure 4 As shown, it includes:

[0083] The ultrasonic scalpel trigger controls the jaws to grip soft tissue while simultaneously releasing an initial current I0;

[0084] A travel sensor, mounted on the ultrasonic scalpel trigger, collects data at t1, t2, ... t during the deformation of soft tissue by the jaws. n Trigger travel at specific moments S1, S2, ... S n ;

[0085] The current acquisition module collects data at t1, t2, ... t during the deformation of soft tissue by the clamp jaws. n Currents I1, I2, ... I passing through the soft tissue at all times n The initial current I0 is set by the program, and I1~I n The current is captured by the circuit feedback of the current acquisition module;

[0086] Calculation unit, let σ=mI Δ = m(I t - I t-1 ), γ=nS Δ =n(S) t -S t-1 According to the Kelvin model:

[0087] ,

[0088] The elastic modulus E and viscosity coefficient η of the soft tissue are obtained by inverse solving, where σ is stress, γ is strain, t is time, and m and n are constants determined by the mechanical system.

[0089] The identification module identifies the tissue type based on the elastic modulus E and viscosity coefficient η of the soft tissue.

[0090] The process of calculating the elastic modulus E and viscosity coefficient η of soft tissue by inverse calculation unit is as follows:

[0091] According to γ=nS Δ =n(S) t -S t-1 ), at any time t i have:

[0092] ;

[0093] According to σ=mI Δ = m(I t - I t-1 ) and the Kelvin model, at any time t in the entire process i have:

[0094] m( ) = E·n( )+η·n ;

[0095] During the entire process of soft tissue compression and deformation, t n At any given moment, there are n-1 equations. Solving any two of these equations simultaneously will yield E and η.

[0096] In this embodiment, t1, t2, ... t n The time interval between moments is one unit, or in the evolution algorithm, the time interval can be any number of units.

[0097] Example 3

[0098] The present invention also proposes a computer-readable medium having a computer program stored thereon, which, when executed by a processor, implements the steps of the soft tissue intelligent recognition method described in Embodiment 1.

[0099] Example 4

[0100] like Figure 4 As shown, the present invention also proposes a soft tissue cutting and sealing device, comprising:

[0101] The monitoring button is activated when the main unit enters the surgical state, thereby initiating the initial current. Activation of the monitoring button can be accomplished by a separate button or by activating it in conjunction with the closing of the ultrasonic scalpel trigger. After activation, the system releases a small current to monitor the entire clamping process. Since the current and stress σ have a linear relationship, a numerical curve of σ (current versus stress) over time is formed, allowing for the fitting of a stress-time function. The Darwinian model, based on linear differential equations, can then solve for the strain-stress functional relationship. The monitoring button is either a physical object or a signal within the control system.

[0102] The soft tissue intelligent recognition device, as described in Example 2, identifies the type of soft tissue.

[0103] During the closing process of the ultrasonic scalpel trigger of the soft tissue intelligent recognition device, three states occur:

[0104] Phase 1: The jaws do not clamp the tissue, the tissue does not strain, and the current does not change.

[0105] Second stage: The jaws begin to clamp the tissue, the tissue is squeezed and deformed, and the current becomes larger and larger;

[0106] Third stage: After the tissue is squeezed and deformed, the trigger continues to move for a certain distance to completely clamp the tissue, and the current increases slightly;

[0107] The second stage is directly related to tissue strain, where the ultrasonic scalpel trigger stroke has a linear relationship with tissue deformation. Therefore, this relationship can be used to determine the relationship between strain and time, thereby fitting a function of strain over time. Thus, the process of identifying soft tissue types occurs during the second stage of ultrasonic scalpel trigger closure.

[0108] Ultrasonic scalpel: Select the cutting or coagulation setting based on the type of soft tissue.

[0109] The entire surgical procedure is as follows:

[0110] (1) The surgery begins, and a microcurrent is turned on;

[0111] (2) During the ultrasonic scalpel trigger closing process, three current segments and trigger stroke were collected;

[0112] (3) Identify the tissue based on the second current and the corresponding stroke;

[0113] (4) Press the high current excitation button;

[0114] (5) The system automatically identifies cutting or sealing;

[0115] (6) Complete the surgery.

[0116] The above embodiments are only for illustrating the technical concept and features of the present invention, and are intended to enable those skilled in the art to understand the content of the present invention and implement it accordingly. They should not be construed as limiting the scope of protection of the present invention. All modifications made according to the spirit and essence of the main technical solution of the present invention should be covered within the scope of protection of the present invention.

Claims

1. A soft tissue intelligent recognition device, characterized in that, include: The ultrasonic scalpel trigger controls the jaws to grip soft tissue while simultaneously releasing an initial current I0; The travel sensor collects data at t1, t2, ... t during the deformation of soft tissue by the jaws of the clamp. n Trigger travel at specific moments S1, S2, ... S n ; The current acquisition module collects data at t1, t2, ... t during the deformation of soft tissue by the clamp jaws. n Currents I1, I2, ... I passing through the soft tissue at all times n ; Calculation unit, let σ=mI Δ = m(I t - I t-1 ), γ=nS Δ =n(S) t -S t-1 According to the Kelvin model: ， The elastic modulus E and viscosity coefficient η of the soft tissue are obtained by inverse solving, where σ is stress, γ is strain, t is time, and m and n are constants determined by the mechanical system. The identification module identifies the tissue type based on the elastic modulus E and viscosity coefficient η of the soft tissue.

2. The soft tissue intelligent recognition device according to claim 1, characterized in that, The process of calculating the elastic modulus E and viscosity coefficient η of soft tissue by inverse calculation unit is as follows: According to γ=nS Δ =n(S) t -S t-1 ), at any time t i have: ； According to σ=mI Δ = m(I t - I t-1 ) and the Kelvin model, at any time t in the entire process i have: m( )=E·n( )+η·n 4 During the entire process of soft tissue compression and deformation, t n At any given moment, there are n-1 equations. Solving any two of these equations simultaneously will yield E and η.

3. The soft tissue intelligent recognition device according to claim 2, characterized in that, The t1, t2, ... t n The time interval between moments is one unit, or in the evolution algorithm, the time interval can be any number of units.

4. The soft tissue intelligent recognition device according to claim 1, characterized in that, The initial current I0 is set by the program, I1~I n It is captured by circuit feedback.

5. A computer-readable medium having a computer program stored thereon, characterized in that, The computer program achieves intelligent soft tissue recognition when executed by a processor. The soft tissue intelligent recognition method includes the following steps: (1) The ultrasonic scalpel trigger controls the jaws to clamp the soft tissue while simultaneously releasing the initial current I0; (2) During the process of clamping the tissue with clamps and compressing and deforming the soft tissue, t1, t2, ... t are collected respectively. n Trigger travel at specific moments S1, S2, ... S n And the currents I1, I2, ... I through soft tissue n ; (3) Let σ = mI Δ = m(I t - I t-1 ), γ=nS Δ =n(S) t -S t-1 According to the Kelvin model: ， The elastic modulus E and viscosity coefficient η of the soft tissue are obtained by inverse solving, where σ is stress, γ is strain, t is time, and m and n are constants determined by the mechanical system. (4) Identify the tissue type based on the elastic modulus E and viscosity coefficient η of the soft tissue.

6. The soft tissue intelligent recognition method according to claim 5, characterized in that, The method for reversing the elastic modulus E and viscosity coefficient η of the soft tissue in step (3) is as follows: (3-1) According to γ=nS Δ =n(S) t -S t-1 ), at any time t i have: ； (3-2) According to σ=mI Δ = m(I t - I t-1 ) and the Kelvin model, at any time t in the entire process i have: m( )=E·n( )+η·n 4 (3-3) During the entire process of soft tissue compression and deformation, t n At any given moment, there are n-1 equations. Solving any two of these equations simultaneously will yield E and η.

7. A soft tissue cutting and sealing device, characterized in that, include: The monitoring button is activated when the main unit enters the surgical state, thereby starting the initial current. A soft tissue intelligent recognition device, using the soft tissue intelligent recognition device according to any one of claims 1-4, identifies the type of soft tissue.

8. The soft tissue cutting and sealing device according to claim 7, characterized in that, When the monitoring button is turned on, it can be activated by a separate button or by following the activation of the ultrasonic scalpel trigger.

9. The soft tissue cutting and sealing device according to claim 8, characterized in that, The monitoring button is either a physical object or a signal in a control system.

10. The soft tissue cutting and sealing device according to claim 9, characterized in that, During the closing process of the ultrasonic scalpel trigger of the soft tissue intelligent recognition device, three states occur: Phase 1: The jaws do not clamp the tissue, the tissue does not strain, and the current does not change. Second stage: The jaws begin to clamp the tissue, the tissue is squeezed and deformed, and the current becomes larger and larger; Third stage: After the tissue is squeezed and deformed, the trigger continues to move for a certain distance to completely clamp the tissue, and the current increases slightly; The process of identifying soft tissue types is in the second stage of ultrasonic scalpel trigger closure.

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