Cartilage diagnosis system and biological diagnosis system

Abstract

To make diagnosis of dynamic characteristics of a biological tissue using OCT more practical.SOLUTION: A cartilage diagnosis system 1 comprises: an optical unit 2 which includes an optical system that uses OCT; a probe 50 which includes a contact surface capable of applying deformation energy by contacting the surface of cartilage and an optical mechanism for guiding light from the optical unit 2 to the cartilage, and in which an optical axis is set such that light from the optical unit 2 is emitted in the normal direction of the contact surface; a manipulator 4 which holds the probe 50 and changes the position and the orientation of the contact surface by moving the probe 50; a control computation device which controls the manipulator 4 and calculates the diagnostic evaluation value of the cartilage tissue by processing an optical interference signal output from the optical unit 2 in response to the application of the deformation energy to the surface of the cartilage; and a display device 200 which displays the diagnostic evaluation value of the cartilage tissue.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a system for diagnosing mechanical properties based on viscoelasticity of a living body. [Background technology]

[0002] Cartilage plays an important role in absorbing load shock and improving joint gliding, but it is a tissue that has difficulty in self-healing due to the lack of blood circulation. Many elderly people develop osteoarthritis (OA) due to wear of the cartilage, and there is a need to establish diagnostic and treatment methods for this condition, especially in countries with an aging society. Cartilage tissue is composed of 80% water and 20% matrix (extracellular matrix), which contains collagen and proteoglycan. In particular, proteoglycan bound to collagen fibers is thought to be greatly involved in the excellent viscoelastic properties of cartilage, such as determining the flow properties inside the cartilage. OA develops due to the loss of the viscoelastic properties of the cartilage.

[0003] With the recent advancement of medical diagnostic technology, optical coherence tomography (hereinafter referred to as "OCT") has been developed. OCT allows for micro-tomographic visualization of the inside of biological tissue in a non-invasive and non-contact manner. In addition, the acquisition rate of two-dimensional OCT tomographic images is faster than the video rate, and they have high time resolution. Therefore, a method for using OCT to perform tomographic visualization of the mechanical properties of cartilage has also been proposed (Patent Document 1).

[0004] In the system of Patent Document 1, an OCT probe is inserted into the knee joint, and the tip of the probe is brought into contact with the surface of the cartilage. Then, a predetermined stress is applied to the cartilage to visualize the degree of deformation of the cartilage tissue in cross-section, making it possible to distinguish between normal cartilage and degenerated cartilage. More specifically, it becomes possible to distinguish between normal and degenerated parts of the cartilage. If it is possible to distinguish between normal and degenerated parts of the cartilage, it is possible to realize a treatment in which a part of the normal part is harvested and autologously cultured, and the cultured cartilage tissue is transplanted into the defective part of the cartilage. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Patent No. 6623163 Summary of the Invention [Problem to be solved by the invention]

[0006] Considering the culturing of such cartilage tissue, it is desirable to evaluate the normal part of the cartilage to be collected with high accuracy. In this regard, when applying the above-mentioned system to an actual medical site, the way in which the probe is applied to the cartilage surface may vary depending on the doctor who operates the probe. In addition, there is a concern that noise from the doctor's body movements may affect the OCT tomographic images, resulting in a decrease in the accuracy of the above-mentioned evaluation. Such problems are not limited to the degree of cartilage degeneration, but may also occur when diagnosing the mechanical properties based on the viscoelasticity of biological tissues, such as skin, cardiac muscle, and ossicles.

[0007] The present invention has been made in view of the above problems, and one of its objects is to make diagnosis of the mechanical properties of biological tissue using OCT more practical. [Means for solving the problem]

[0008] One aspect of the present invention is a system for diagnosing articular cartilage. The cartilage diagnostic system includes an optical unit including an optical system using optical coherence tomography, a probe having an abutment surface capable of abutting against the surface of the cartilage to impart deformation energy, and an optical mechanism for guiding light from the optical unit to the cartilage, the probe having an optical axis set so as to emit light from the optical unit in the normal direction of the abutment surface, a manipulator for holding the probe and moving the probe to change the position and orientation of the abutment surface, a control and arithmetic device for controlling the manipulator and calculating a diagnostic evaluation value of cartilage tissue by processing an optical interference signal output from the optical unit in response to the application of deformation energy to the surface of the cartilage, and a display device for displaying the diagnostic evaluation value of the cartilage tissue.

[0009] Another aspect of the present invention is a system for diagnosing mechanical properties based on the viscoelasticity of biological tissue. The biological diagnostic system includes an optical unit including an optical system using optical coherence tomography, a probe having an abutment surface capable of abutting against a surface of a target portion of a biological body to impart deformation energy, and an optical mechanism for guiding light from the optical unit to the biological tissue, the probe having an optical axis set so as to emit the light from the optical unit in a normal direction to the abutment surface, a manipulator for holding the probe and moving the probe to change the position and orientation of the abutment surface, a control and arithmetic device for controlling the manipulator and calculating a diagnostic evaluation value of the biological tissue by processing an optical interference signal output from the optical unit in response to the application of deformation energy to the surface of the target portion, and a display device for displaying the diagnostic evaluation value of the biological tissue. Effect of the Invention

[0010] According to the present invention, diagnosis of the mechanical properties of biological tissue using OCT can be made more practical. [Brief description of the drawings]

[0011] [Figure 1] 1 is a diagram illustrating a configuration of a cartilage diagnosis system according to an embodiment. [Diagram 2] FIG. 2 is a diagram illustrating a schematic configuration of a manipulator. [Diagram 3] FIG. 2 is a diagram illustrating a schematic configuration of a probe. [Figure 4] FIG. 2 is a functional block diagram of a control arithmetic device. [Diagram 5] FIG. 2 is a functional block diagram of a user terminal. [Figure 6] FIG. 13 is a diagram showing attitude control of a manipulator. [Figure 7] The machine learning model used to calculate the diagnostic evaluation value is shown. [Figure 8] FIG. 1 is a diagram showing a method of acquiring OCT sectional images during learning or diagnosis. [Figure 9]FIG. 11 is a diagram illustrating a specific example of a user interface of a user terminal. [Figure 10] 13 is a flowchart illustrating a general flow of cartilage diagnosis processing. [Figure 11] FIG. 13 is a diagram showing experimental results. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. In the following embodiment and its modified examples, substantially the same components are denoted by the same reference numerals, and the description thereof will be omitted as appropriate.

[0013] FIG. 1 is a diagram showing the configuration of a cartilage diagnosis system according to an embodiment. The cartilage diagnostic system 1 displays to the user an evaluation value (referred to as a "diagnostic evaluation value") for diagnosing the degree of degeneration of cartilage tissue based on an OCT tomographic image of articular cartilage in a manner that allows the user to visually confirm the evaluation value.

[0014] In this embodiment, a treatment is assumed in which a part of the cartilage of a patient who has developed OA is harvested, autologously cultured, and transplanted into the cartilage defect. A doctor (operator) cuts open the patient's knee joint to expose the cartilage and identifies the normal part. A part of the normal part is harvested and cultured, and the cultured cartilage tissue is transplanted into the cartilage defect.

[0015] The surgeon is a user of this system and holds a user terminal 200 for displaying the diagnostic evaluation value during surgery. In this embodiment, the user terminal 200 is an AR (Augmented Reality) goggles (head mounted display). When the surgeon wears the AR goggles and projects cartilage on the screen, the diagnostic evaluation value is superimposed on the image of the cartilage. This makes it possible to distinguish between normal and degenerated areas in the cartilage, and allows cartilage tissue from normal areas to be easily harvested.

[0016] The diagnostic evaluation value is calculated (estimated) by a machine learning model (mathematical model) generated in advance. That is, a diagnostic evaluation value indicating the degree of degeneration of the cartilage is output by inputting time-series data of OCT tomographic images of the cartilage into the machine learning model. A heat map (hereinafter also referred to as a "degeneration degree map") that associates this diagnostic evaluation value with the position of the cartilage surface is generated and displayed superimposed on the image of the cartilage surface. These details will be described below.

[0017] The cartilage diagnostic system 1 includes an optical unit 2, a manipulator 4, and a control and arithmetic device 6. The control and arithmetic device 6 is connected to a user terminal 200 wirelessly or by wire so as to be able to communicate with the user terminal 200. In this embodiment, the user terminal 200 has a display unit 202 and functions as a "display device" that displays the diagnostic evaluation value, but the control and arithmetic device 6 may also be connected to a display device (display) separate from the user terminal 200 to display the diagnostic evaluation value.

[0018] A diagnostic probe 50 is attached to the tip of the manipulator 4. The optical unit 2 includes a light source 10, an object arm 12, a reference arm 14, an optical mechanism 16, and a light detection device 18. The probe 50 is connected to the optical unit 2 and constitutes one end of the object arm 12.

[0019] The optical elements of the optical unit 2 are connected to each other by optical fibers. In the illustrated example, an optical system based on a Mach-Zehnder interferometer is shown, but a Michelson interferometer or other optical system can also be used. In this embodiment, SS-OCT (Swept Source OCT) using a wavelength scanning laser as a light source is used as the OCT. The light source 10 in this embodiment is a wavelength swept light source with a central wavelength of 1310 (nm) and a wavelength sweep range of 106 (nm), and the depth spatial resolution is about 6 μm.

[0020] In addition, in a modified example, TD-OCT (Time Domain OCT), SD-OCT (Spectral Domain OCT), or other OCT can also be used. Unlike TD-OCT, SS-OCT does not require mechanical optical delay scanning such as reference mirror scanning, and is therefore preferable in that it can provide high time resolution and high position detection accuracy.

[0021] Light (laser) emitted from the light source 10 is split by the coupler 20 (beam splitter), one of which is guided to the object arm 12 to become object light, and the other is guided to the reference arm 14 to become reference light. The object light from the object arm 12 is guided to the probe 50 via the circulator 22 and irradiated onto the cartilage, which is the subject of diagnosis. This object light is reflected as backscattered light on the surface and cross section of the cartilage, taken into the probe 50, and guided to the coupler 24 via the circulator 22.

[0022] The optical fiber 25 extending from the circulator 22 is inserted into the probe 50. A small-diameter insertion section 52 is provided at the tip of the probe 50, and the insertion section 52 can be placed inside a living body (the patient's knee K). The knee joint may be incised to expose the cartilage, and the insertion section 52 may be inserted (placed) in the incision. The probe 50 is provided with an optical mechanism 54 (functioning as a "first optical mechanism") for directing light from the optical unit 2 to the cartilage of the knee K, and an indenter 56 (contact surface) for contacting the cartilage to transmit a predetermined load (stress). The optical mechanism 54 irradiates light toward the cartilage and directs the reflected light to the object arm 12.

[0023] On the other hand, the reference light from the reference arm 14 is guided to the optical mechanism 16 (functioning as a "second optical mechanism") via a circulator 26. This reference light passes through a collimator lens 28 and is focused on a reflecting mirror 32 by a focusing lens 30. This reference light is reflected by the reflecting mirror 32 and returns to the circulator 26, and is guided to the coupler 24. That is, the object light and the reference light are multiplexed (superimposed) by the coupler 24, and the interference light is detected by the photodetector 18. The photodetector 18 detects this as an optical interference signal (a signal indicating the intensity of the interference light).

[0024] The light detection device 18 includes a photodetector 34, a filter 36, and an amplifier 38. The interference light obtained by passing through the coupler 24 is detected as an optical interference signal by the photodetector 34. This optical interference signal has noise removed or reduced by the filter 36, and is input to the control and arithmetic device 6 via the amplifier 38 and the A / D converter 40.

[0025] The control and arithmetic device 6 controls the entire optical system, controls the manipulator 4, and performs arithmetic processing for image output by OCT. Command signals from the control and arithmetic device 6 are input to the optical unit 2, the optical mechanism 54 of the probe 50, and each device of the manipulator 4 via the D / A converter 42. The control and arithmetic device 6 processes the optical interference signal input to the photodetector 18 and obtains a tomographic image (brightness information) of the cartilage by OCT. Then, based on the tomographic image data, the control and arithmetic device 6 calculates a diagnostic evaluation value representing the degree of degeneration of the cartilage tissue by a method described later. The control and arithmetic device 6 generates a two-dimensional or three-dimensional map image (degeneration degree map) in which the calculated diagnostic evaluation value is associated with the position of the cartilage surface, and transmits the degeneration degree map in response to a request from the user terminal 200.

[0026] In this embodiment, the user terminal 200 is an AR goggle having a calculation function, a display function, and a communication function, and the display unit 202 is configured as a head-mounted display. In a modified example, a smartphone, a tablet computer, a desktop PC, a laptop PC, or other user terminal may be adopted. The user terminal 200 receives the degeneration degree map transmitted from the control and calculation device 6, and displays it as a diagnostic evaluation value superimposed on the image of the cartilage surface displayed on the screen of the display unit 202 (described in detail later).

[0027] A camera 46 is also connected to the control and arithmetic device 6 for detecting the position of the probe 50 in a treatment room (operating room) in real time. The camera 46 is a depth camera having a depth sensor, and can measure the distance to an object (subject) as three-dimensional coordinates. In this embodiment, multiple cameras 46 are provided for position detection, but depending on the environment of the treatment room (the arrangement of equipment), only one cartilage diagnostic system may be provided. The control and arithmetic device 6 calculates the position of the probe 50 by coordinate conversion based on the three-dimensional coordinates measured by the multiple cameras 46.

[0028] FIG. 2 is a diagram illustrating a schematic configuration of the manipulator 4. As shown in FIG. The manipulator 4 is a multi-joint robot with six degrees of freedom, and is capable of translation (x, y, z) in three axial directions in the global coordinate system, and rotation (Rx, Ry, Rz) around each axis. Note that in this embodiment, a known manipulator used for industrial or medical purposes is adopted as the manipulator 4, and therefore detailed description thereof will be omitted.

[0029] A probe 50 is detachably attached to the tip of the manipulator 4. The position and orientation of the probe 50 can be controlled by driving the manipulator 4. A depth camera 70 is attached integrally to the probe 50. The depth camera 70 functions as a "depth sensor" for detecting the surface shape of the cartilage. By attaching the probe 50 to the tip of the manipulator 4 in this way and providing the depth camera 70 on the probe 50, the depth sensor can be brought close to the cartilage. As a result, it becomes easier to detect the surface shape of the cartilage.

[0030] FIG. 3 is a diagram illustrating a schematic configuration of the probe 50. As shown in FIG. The probe 50 has a small-diameter cylindrical insertion section 52 attached to the tip of a hollow main body 51. The axis of the insertion section 52 is configured to coincide with the optical axis. The insertion section 52 is inserted into the incised joint of the knee K. The lower half of the main body 51 is a small-diameter section 53 whose outer diameter is slightly larger than that of the insertion section 52, improving the visibility of the insertion section 52 when the probe 50 is photographed, etc.

[0031] An indenter 56 capable of coming into contact with the cartilage J is coaxially disposed at the tip of the insertion section 52. The indenter 56 is a light-transmitting member, and its tip surface serves as an abutment surface 58 capable of coming into contact with the surface of the cartilage J. In this embodiment, the indenter 56 is made of glass, but plastic or other materials may be selected as long as the indenter 56 has sufficient strength to not break or deform when pressed against the cartilage J and is capable of transmitting object light.

[0032] A collimator lens 60 is provided so as to penetrate the side of the main body 51, and a galvanometer mirror 61 is disposed inside the main body 51. When acquiring a three-dimensional OCT tomographic image, a plurality of galvanometer mirrors 61 are provided. An achromatic lens 62 (objective lens) is disposed at the boundary between the small diameter portion 53 and the insertion portion 52. An optical fiber 25 is connected to the collimator lens 60. The collimator lens 60, the galvanometer mirror 61, the achromatic lens 62, and the indenter 56 constitute the optical mechanism 54. The optical axis of the probe 50 is set so as to emit light from the optical unit 2 in the normal direction of the contact surface 58.

[0033] The manipulator 4 moves the probe 50, thereby changing the position and orientation of the contact surface 58 relative to the cartilage surface. The manipulator 4 is controlled to press the contact surface 58 against the surface of the cartilage J, thereby applying deformation energy. The deformation energy changes over time. By processing the optical interference signal (time-series data) output from the optical unit 2 at this time, the diagnostic evaluation value of the cartilage tissue (degree of degeneration of the cartilage tissue) can be calculated.

[0034] The light guided to the probe 50 by the optical fiber 25 is emitted via the optical mechanism 54 and irradiated onto the cartilage J. As a result, light reflected on the surface or inside of the cartilage J is taken in by the probe 50 and guided to the object arm 12 of the optical unit 2 via the optical fiber 25. By driving the galvanometer mirror 61, it is also possible to irradiate the cartilage J with light while scanning it in the x-axis direction or y-axis direction perpendicular to the optical axis direction (z-axis direction). As a result, a two-dimensional or three-dimensional OCT tomographic image can be acquired.

[0035] The viscoelasticity of the cartilage tissue changes according to the degree of degeneration of each part of the cartilage J, and as a result, the mechanical properties of the cartilage tissue change. The control and calculation device 6 calculates the mechanical properties of the cartilage tissue as a "diagnostic evaluation value." By acquiring two-dimensional or three-dimensional OCT tomographic images, the diagnostic evaluation value of the cartilage J can also be displayed as a two-dimensional or three-dimensional image (degeneration degree map).

[0036] FIG. 4 is a functional block diagram of the control and arithmetic device 6. Each component of the control and arithmetic device 6 is realized by hardware including arithmetic units such as a CPU and various co-processors, storage devices such as memory and storage, and wired or wireless communication lines connecting them, and software stored in the storage devices and supplying processing instructions to the arithmetic units. The computer programs may be composed of device drivers, an operating system, various application programs located at higher layers than those, and libraries that provide common functions to these programs. Each block described below shows a functional block rather than a hardware-based configuration.

[0037] The control and arithmetic device 6 includes an input / output interface unit 110, a communication unit 112, a data processing unit 114, and a data storage unit 116. The input / output interface unit 110 is responsible for processing related to the input / output interface, including data exchange with an external device or external equipment. The communication unit 112 is responsible for communication processing with an external device such as a user terminal 200. The data processing unit 114 executes various processes based on data acquired by the input / output interface unit 110, data acquired by the communication unit 112, and data stored in the data storage unit 116. The data processing unit 114 also functions as an interface for the input / output interface unit 110, the communication unit 112, and the data storage unit 116. The data storage unit 116 stores various programs and setting data.

[0038] The input / output interface unit 110 includes an input unit 120 and an output unit 122. The input unit 120 includes an input information acquisition unit 124 and a detection information acquisition unit 126. The input information acquisition unit 124 acquires input information based on the optical interference signal output from the optical unit 2. The "input information" here is an input value input to the machine learning model. In this embodiment, an OCT tomographic image of cartilage obtained by processing the optical interference signal is used as the input value, the details of which will be described later.

[0039] The detection information acquisition unit 126 acquires each of the detection information from the camera 46 and the depth camera 70. Based on each of the detection information from the camera 46 and the depth camera 70, the relative positional relationship between the probe 50 attached to the tip of the manipulator 4 and the cartilage surface can be acquired. The position information (coordinates) of the probe 50 can be acquired from the rotation information (rotation information around each axis) of the motor constituting each joint of the manipulator 4. In addition, shape information of the cartilage surface can be acquired based on the detection information from the depth camera 70. The detection information acquisition unit 126 further acquires information for identifying the detailed shape of the cartilage surface based on the optical interference signal output from the optical unit 2. The details of these will be described later. The output unit 122 outputs control commands to the optical unit 2 and each functional unit (device including a control circuit and a drive circuit) of the manipulator 4.

[0040] The communication unit 112 includes a transmission unit 130 and a reception unit 132. The transmission unit 130 transmits various data to an external device such as a user terminal 200. The reception unit 132 receives various data and commands from the external device.

[0041] The data processing unit 114 includes a control unit 140, a calculation unit 142, a learning unit 144, and an output generation unit 146. The control unit 140 includes an OCT driving unit 150 and a robot control unit 152. The OCT driving unit 150 controls the driving of each device in the optical unit 2. The robot control unit 152 controls the driving of the manipulator 4.

[0042] The robot control unit 152 includes a movement control unit 154, a first attitude control unit 156, and a second attitude control unit 158. When the mode is switched to a diagnosis mode for diagnosing the cartilage, the movement control unit 154 drives the manipulator 4 from the standby position based on the detection information of the camera 46, and moves the probe 50 toward the cartilage. When the probe 50 approaches the cartilage to a predetermined distance, the first attitude control unit 156 drives the manipulator 4 based on the detection information of the depth camera 70, and moves the probe 50 even closer to the cartilage within a range in which the cartilage is within the viewing angle (angle of view) of the depth camera 70. Then, the attitude of the probe 50 is controlled so that the normal direction (optical axis direction) of the contact surface of the probe 50 approaches the normal direction of the cartilage surface (first attitude control).

[0043] The second attitude control unit 158 ​​moves the probe 50 close to the cartilage after the first attitude control, and finely adjusts the attitude of the probe 50 based on the detailed shape of the cartilage surface detected by OCT. This causes the normal direction of the contact surface of the probe 50 to coincide with the normal direction of the cartilage surface (second attitude control). This type of stepwise attitude control can improve the accuracy of the diagnostic evaluation value, and details will be described later. When the diagnosis mode ends, the movement control unit 154 retreats the manipulator 4 to the standby position.

[0044] The calculation unit 142 includes a position calculation unit 160, a shape calculation unit 162, and an evaluation value calculation unit 164. The position calculation unit 160 calculates the position of the probe 50 in global coordinates (particularly the position of the contact surface 58) based on the detection information of each camera and OCT and the drive information (motor rotation information) of the manipulator 4. The shape calculation unit 162 calculates a rough shape of the cartilage surface based on the detection information of the depth camera 70, and further calculates a detailed shape of the cartilage surface based on the detection information of the OCT. The evaluation value calculation unit 164 calculates a diagnostic evaluation value representing the degree of degeneration of the cartilage by setting the OCT tomographic image as an input value for the above-mentioned machine learning model.

[0045] The learning unit 144 executes a learning process for the machine learning model so that the diagnostic evaluation value can be calculated. When generating the machine learning model, the learning unit 144 adjusts the machine learning model by using input information acquired about the cartilage to be learned as an input value and using a setting value (label) of the diagnostic evaluation value of the cartilage to be learned as an output value (described in detail later).

[0046] When instructed by the user terminal 200, the output generation unit 146 generates a degeneration degree map (a two-dimensional or three-dimensional image representing the diagnostic evaluation value) to be output to the user terminal 200 based on the calculated diagnostic evaluation value.

[0047] The data storage unit 116 stores the machine learning model generated by the data processing unit 114 and the diagnostic evaluation value that is the output result of the machine learning model. The data storage unit 116 includes a memory that functions as a working area when the data processing unit 114 performs calculation processing.

[0048] FIG. 5 is a functional block diagram of the user terminal 200. Each component of the user terminal 200 is also realized by hardware including computing units such as a CPU and various co-processors, storage devices such as memory and storage, and wired or wireless communication lines connecting them, and software stored in the storage devices and supplying processing instructions to the computing units. Each block described below represents a functional block, not a hardware configuration.

[0049] The user terminal 200 includes a user interface processing unit 210, a communication unit 212, a data processing unit 214, and a data storage unit 216. The user interface processing unit 210 accepts operation input from the user via a predetermined input device, and is responsible for processing related to the user interface, such as image display and audio output.

[0050] In this embodiment, the user terminal 200 is an AR goggle including a head mounted display, and is capable of grasping the contents (e.g., the position) that the user points with his / her finger or the like using a camera (so-called hand tracking). The user can select various operations by aligning the operation button displayed on the head mounted display with the position of the finger displayed on the head mounted display. In addition, in the diagnosis mode, the manipulator 4 can be driven to move the tip of the probe 50 to the specified position by specifying the position of the background image displayed on the head mounted display with the finger.

[0051] The communication unit 212 is responsible for communication processing with the control arithmetic device 6 and the like. The data storage unit 216 stores various data. The data processing unit 214 executes various processes based on input from the user interface processing unit 210, data received by the communication unit 212, and data stored in the data storage unit 216. The data processing unit 214 also functions as an interface between the communication unit 212, the user interface processing unit 210, and the data storage unit 216.

[0052] The user interface processing unit 210 includes an input unit 220, an output unit 222, and an imaging unit 224. The input unit 220 accepts operational input from a user. The output unit 222 outputs various information such as images and sounds to the user. The images are displayed on the display unit 202 (head mounted display: see FIG. 1). The imaging unit 224 acquires real images captured by a camera mounted on the AR goggles. The output unit 222 causes the display unit 202 to display an AR image in which a degeneration degree map (virtual image) is synthesized with the real image.

[0053] The communication unit 212 includes a transmission unit 230 and a reception unit 232. The transmission unit 230 transmits data such as position information of the user terminal 200 to the control arithmetic device 6. The reception unit 232 receives data such as a degeneration degree map from the control arithmetic device 6. The transmission unit 230 can also transfer a real image or an AR image generated by the user terminal 200 to the control arithmetic device 6.

[0054] The data processing unit 214 includes an instruction input analysis unit 240, a position information calculation unit 242, a display processing unit 244, and an output generation unit 246. The instruction input analysis unit 240 analyzes the contents of the user's operation input (i.e., the instruction contents) based on the display information on the display screen of the display unit 202 and the contents (e.g., the position) of the user's instruction on the display screen with a finger or the like. The position information calculation unit 242 calculates the position information (including the position and orientation) of the user terminal 200 in the processing chamber based on the actual image acquired by the imaging unit 224.

[0055] In order to grasp the contents (e.g., the position) pointed by the user with his / her finger, that is, to realize hand tracking, a process of converting the local coordinates of the user terminal 200 into the global coordinates of the manipulator 4 (robot) is sequentially performed. The converted position information is transmitted to the control arithmetic device 6.

[0056] The display processing unit 244 superimposes and aligns the degeneration degree map with the image of the cartilage included in the actual image based on the position information of the user terminal 200 and the degeneration degree map (e.g., a two-dimensional image of the diagnostic evaluation value). The output generation unit 246 generates information (e.g., position information, AR image, etc.) to be output to the control arithmetic device 6 upon request from the control arithmetic device 6.

[0057] The data storage unit 216 stores the diagnostic evaluation value (degeneration degree map) received from the control arithmetic device 6, the AR image generated by the data processing unit 214, etc. The data storage unit 216 includes a memory that functions as a working area when the data processing unit 214 performs arithmetic processing.

[0058] FIG. 6 is a diagram showing the attitude control of the manipulator 4. As shown in FIG. As a prerequisite for calculating the diagnostic evaluation value of cartilage using this system, it is necessary to generate a mathematical model (machine learning model) that learns the correspondence between the dynamic interference time series signal obtained by OCT and the diagnostic evaluation value (elasticity coefficient, viscosity coefficient, etc.) that represents the degree of degeneration of cartilage tissue. When obtaining this dynamic interference time series signal, a compressive load (deformation energy) is applied to the cartilage J, and the time change in the physical behavior of the cartilage tissue (time series data) is obtained. From this physical behavior, the viscoelastic properties of the cartilage can be understood and the degree of degeneration can be estimated.

[0059] The diagnostic evaluation value is calculated based on a comparison with a diagnostic reference value (a reference diagnostic evaluation value) previously obtained by a similar method. For this reason, the conditions such as the method of applying a compressive load to the cartilage J and the method of emitting the OCT light must be constant. For this reason, it is desirable to perform the above-mentioned posture control with high accuracy.

[0060] Therefore, the control and arithmetic device 6 drives the manipulator 4 to bring the probe 50 closer to the cartilage J, and executes the above-mentioned attitude control. First, the movement control unit 154 drives the manipulator 4 based on the detection information of the camera 46 to move the probe 50 toward the cartilage. In this embodiment, as shown in the figure, two cameras 46 (46a, 46b) are installed on both sides of the position where the patient's knee K is placed in the treatment room OR. Note that multiple cameras 46 (three or more) that can capture images of the position where the patient's knee K is placed from multiple directions may be installed. By capturing images of the probe 50 with these cameras 46, its position can be calculated.

[0061] Next, the above-mentioned stepwise attitude control is executed. Prior to the application of a compressive load to the cartilage J by the probe 50, the control and calculation device 6 controls the attitude of the manipulator 4 so that the normal direction of the contact surface 58 of the probe 50 approaches the normal direction of the surface of the cartilage J based on the surface shape of the cartilage J detected by the depth camera 70. The normal direction of the contact surface 58 is also the optical axis direction of the probe 50. By aligning the optical axis direction of the OCT with the normal direction of the cartilage surface, the accuracy of the calculation process based on the optical interference signal can be improved. In other words, the diagnostic evaluation value of the cartilage tissue can be calculated with high accuracy.

[0062] Specifically, in order to adjust the angle of the probe 50 with respect to the surface of the cartilage J, the first attitude control unit 156 executes a first attitude control for adjusting the angle with a predetermined accuracy. Then, the second attitude control unit 158 ​​executes a second attitude control for adjusting the angle with even higher accuracy. As the surface shape of the cartilage J, the shape detected by the depth camera 70 is used in the first attitude control, and the shape detected by the OCT is used in the second attitude control. The depth camera 70 has a larger viewing angle than the OCT, but also a larger focal length, so that the detection accuracy decreases as it approaches the cartilage J, and in some cases, the cartilage surface may deviate from the viewing angle. On the other hand, the OCT has a small viewing angle (size of the detectable area) and focal length, so shape detection is not possible unless it is close to the cartilage surface, but the depth resolution can be increased to several μm when it approaches the cartilage surface, which is advantageous for detailed detection of the surface shape. For this reason, the appropriate ranges of both for shape detection are used.

[0063] In the first attitude control, the attitude of the manipulator 4 is adjusted based on the surface shape of the cartilage J detected by the depth camera 70 while the probe 50 is brought close to the cartilage J to a first distance. In the subsequent second attitude control, the surface shape of the cartilage J can be detected in two or three dimensions based on an optical interference signal obtained without applying a compressive load (deformation energy) while the probe 50 is brought close to the cartilage J to a second distance smaller than the first distance. Then, the attitude of the manipulator 4 is finely adjusted based on the calculated surface shape.

[0064] In each posture control, the unit normal vector a→ of the cartilage surface is calculated from the surface shape of the cartilage J, and the unit direction vector b→ of the contact surface 58 of the probe 50 is calculated from the joint angle of the manipulator 4. The unit direction vector b→ of the contact surface 58 is aligned with the optical axis of the laser emitted from the contact surface 58. Here, the incident angle θ of the laser to the cartilage surface is defined by the following formula (1).

number

[0065] Each attitude control unit of the control and calculation device 6 determines the target incidence angle θ of the laser onto the cartilage. 0 = 0, and set the incident angle θ to the target incident angle θ 0 The manipulator 4 is driven to control the attitude of the probe 50 so as to bring the probe 50 closer to the cartilage surface. In this embodiment, the three-axis attitude control of the manipulator 4 is performed by PID control. By such stepwise attitude control, the probe 50 can be stably brought closer to the cartilage surface, and the angle of the probe 50 can be accurately adjusted.

[0066] FIG. 7 shows the machine learning model used to calculate the diagnostic evaluation value. The control and calculation device 6 estimates the degree of degeneration (diagnostic evaluation value) of the cartilage by the machine learning model M based on the OCT tomographic image (time series data) input from the optical unit 2 with the application of a compressive load to the cartilage. Specifically, learning is performed by the learning unit 144. In the learning stage, a user provides the OCT tomographic image (time series data with the application of a load) of a denatured cartilage sample and the degree of degeneration (diagnostic evaluation value) of the cartilage sample as teacher data, and prepares a machine learning model M that has been trained. This cartilage sample may be a cartilage sample cut from normal cartilage that has been artificially denatured by performing a predetermined treatment (collagenase enzyme treatment, etc.). Specifically, a naturally occurring denatured cartilage sample obtained by biopsy of human cartilage is used, and the diagnostic evaluation value can be set in correspondence with a doctor's findings or a viscoelastic coefficient.

[0067] Collagenase enzyme treatment causes the collagen fibers inside the cartilage tissue to break down. The accompanying release of proteoglycan causes the tissue fluid flow and tissue displacement under compressive load to change depending on the enzyme treatment time. It is known that the treatment time with collagenase enzyme is related to the viscoelastic properties of cartilage tissue. Since the degree of denaturation of the cartilage sample can be varied depending on the treatment time, it is possible to label the cartilage sample with a denaturation degree of 0 to 4 (denaturation degree: 0 is normal cartilage, the value increases as the degree of denaturation increases) in accordance with the treatment time.

[0068] The machine learning model M uses, for example, a convolutional neural network (CNN) and may have an intermediate layer (convolutional layer, pooling layer, fully connected layer) between the input layer and the output layer. The user prepares the machine learning model M by learning the OCT tomographic images as input data and the degree of degeneration (diagnostic evaluation value) as output data using these as training data. The learning unit 144 adjusts the internal parameters (intermediate layer parameters, weighting) of the machine learning model M that has been trained using the training data for a large number of cartilage samples.

[0069] After learning, the evaluation value calculation unit 164 acquires OCT tomographic images (time-series data accompanying the application of a compressive load) of the patient's cartilage, which is the actual diagnosis target, and provides the data as input data to the machine learning model M. The evaluation value calculation unit 164 calculates the degree of degeneration (diagnostic evaluation value) from the output of the machine learning model M based on this input data. According to this embodiment, the diagnostic evaluation value can be easily determined by reading the detection data by OCT (time-series data of the OCT tomographic images) into the machine learning model M.

[0070] FIG. 8 is a diagram showing a method of acquiring OCT sectional images during learning or diagnosis. In the process of acquiring an OCT tomographic image, a diagnostic area DA is set in the cartilage J of the knee joint (FIG. 8(A)), and the diagnostic area DA is divided into a plurality of detection areas IAn (n is a natural number) in a mesh pattern (FIG. 8(B)). In the illustrated example, a square area (e.g., an area of ​​3×3 mm) in the xy direction is set as the detection area IAn, and an OCT tomographic image (one-dimensional image) in the normal direction (z direction) of the cartilage surface is acquired at its center position On. In other words, the diagnosis result of the detection area IAn is represented by the diagnosis result at the center position On. In a modified example, a three-dimensional OCT tomographic image may be acquired as time-series data within the detection area IAn while a compressive load is applied, and the degree of degeneration may be estimated from each one-dimensional tomographic signal constituting the three-dimensional OCT tomographic image, and averaged within the detection area IAn.

[0071] This tomographic distribution is acquired as time-series data that displays images obtained only by scanning the cartilage in the depth direction (z direction) (i.e., one-dimensional images in one axis direction) over time. Once the data of the detection area IAn has been acquired, the probe 50 is moved two-dimensionally in the x and y directions in sequence to acquire OCT tomographic images (time-series data) of each detection area (FIG. 8(C)). As shown in the figure, the detection area IAn should be sufficiently large compared to the size of the contact surface 58 of the probe 50 (e.g., 1×1 mm).

[0072] In this embodiment, in order to prioritize shortening the processing time, a one-dimensional image is acquired as an OCT tomographic image in this manner. Therefore, driving of the galvanometer mirror 61 (see FIG. 3) is not particularly necessary in controlling the probe 50. In a modified example, the galvanometer mirror 61 may be driven to perform scanning in directions (x and y directions) perpendicular to the depth direction in the image acquisition process, thereby displaying a two-dimensional image or a three-dimensional image obtained.

[0073] However, in order to obtain a diagnostic evaluation value, it is necessary to detect the dynamic behavior of the cartilage tissue, that is, it is necessary to obtain the viscoelastic properties of the cartilage tissue. For this reason, time-series data (time-varying data) of the OCT tomographic images during the process of applying a compressive load in the z direction by the probe 50 is necessary. In this embodiment, the load time (e.g., 100 ms) for applying the compressive load is set. When performing real-time diagnosis in a medical setting, it is preferable to display a one-dimensional image as in this embodiment.

[0074] In the post-learning diagnosis mode shown in FIG. 7, the evaluation value calculation unit 164 sets the time series data of OCT tomographic images (one-dimensional images in one axis direction) acquired with the application of a compressive load as input values.

[0075] FIG. 9 is a diagram showing a specific example of a user interface of the user terminal 200. As shown in FIG. After the machine learning model is generated as described above, the user can perform a treatment on the cartilage (collect cartilage tissue from a normal site) based on the diagnostic evaluation by the machine learning model. The user can check the diagnostic evaluation value of the cartilage J by wearing the user terminal 200 in the treatment room.

[0076] At this time, the control and arithmetic device 6 performs image processing on the calculated diagnostic evaluation value in the form of a degeneration degree map, and transmits the image to the user terminal 200. The user terminal 200 displays the degeneration degree map superimposed on an image of the cartilage surface displayed on a screen 250 (head-mounted display). In the illustrated example, the display is color-coded according to the degree of degeneration. The smaller the degree of degeneration, the lighter the color, and the greater the degree of degeneration, the darker the color, so that the user can distinguish between normal and degenerated parts of the cartilage J by looking at the screen 250. This makes it easy to collect part of the normal part for autologous culture.

[0077] The display format of the diagnostic evaluation value can be appropriately set in consideration of the user's visibility. In this embodiment, the degeneration degree is color-coded according to a certain range, but it may be distinguished by using different patterns, etc. Alternatively, the diagnostic evaluation value may be converted into a numerical value and the numerical value itself may be displayed, or the numerical range (which numerical range the value belongs to) may be displayed.

[0078] Next, a specific process flow executed by the control and arithmetic device 6 will be described with reference to each function shown in FIG. 4 and FIG. FIG. 10 is a flowchart illustrating the outline of the flow of the cartilage diagnosis process. In the diagnosis mode, the user wears the user terminal 200 (AR goggles) and first specifies a target diagnosis part based on the image displayed on the screen of the terminal (see FIG. 6). Here, the target diagnosis part is the cartilage J of the knee K. As described above, the user specifies the diagnosis target area DA of the cartilage J included in the image on the screen with a finger. The position information calculation unit 242 calculates the position information specified by the user. This position information is transmitted from the user terminal 200 to the control and arithmetic device 6.

[0079] In the control and arithmetic device 6, the position calculation unit 160 identifies the diagnosis position based on the position information received from the user terminal 200 (S10). The movement control unit 154 drives the manipulator 4 to move the probe 50 toward the cartilage J at the diagnosis position (S12).

[0080] When the probe 50 approaches the cartilage J to a first distance (for example, 20 to 30 cm from the cartilage J), ​​the first attitude control unit 156 executes the first attitude control (S14). As described above, the attitude of the manipulator 4 is adjusted based on the surface shape of the cartilage J detected by the depth camera 70.

[0081] Next, the second attitude control unit 158 ​​executes the second attitude control (S16). As described above, with the probe 50 brought close to the cartilage J to a second distance (for example, 1 to 2 mm from the cartilage J), ​​the attitude of the manipulator 4 is finely adjusted based on the surface shape of the cartilage J calculated by OCT. In this way, the optical axis direction and the direction in which the compressive load is applied are aligned with the normal direction of the cartilage surface.

[0082] Next, the movement control unit 154 drives the manipulator 4 in the optical axis direction, brings the probe 50 into contact with the surface of the cartilage J, and applies a preset compressive load (S18). The evaluation value calculation unit 164 performs a diagnosis (AI (Artificial Intelligence) diagnosis) using a machine learning model while applying this load (S20). The output generation unit 146 generates a degeneration degree map based on the diagnosis evaluation value, which is the diagnosis result, and transmits it to the user terminal 200. When the diagnosis of one detection area IAn is completed, the movement control unit 154 raster drives the probe 50 (S22). That is, the contact surface 58 of the probe 50 is moved to the adjacent detection area.

[0083] Such processing (S14 to S22) is repeated until diagnosis of all detection regions in the diagnosis target region DA is completed (N in S24). In this embodiment, the degeneration degree map is successively updated during the AI ​​diagnosis process and displayed on the user terminal 200.

[0084] When the diagnosis is completed (Y in S24), the movement control unit 154 drives the manipulator 4 to move the probe 50 to the standby position (S26). At this time, the first attitude control unit 156 returns the attitude of the manipulator 4 to the standby attitude, and the series of processes is completed.

[0085] Next, the effectiveness of the above-mentioned diagnostic technique will be described. In this embodiment, as described above, a tomographic image of cartilage tissue is obtained using OCT, and the time-series data of the OCT tomographic image is given as an input value to a machine learning model, whereby a diagnostic evaluation value is output and visually displayed to a user. Here, the results of an experiment conducted to verify the effectiveness of the diagnostic evaluation are described.

[0086] In this experiment, a normal cartilage tissue sample was taken from the cartilage of the lateral femoral condyle of a pig's knee joint to prepare a test specimen, which was then subjected to an enzymatic treatment to prepare a simulated denatured cartilage (OA test specimen in which the degree of degeneration was artificially controlled to simulate cartilage). Collagenase, which breaks down collagen fibers that maintain the elasticity of articular cartilage, was used for this enzymatic treatment. Specifically, normal cartilage was placed in a solution of collagenase dissolved in phosphate buffered saline (pH 7.4), and the solution was kept at 37°C and left for a specified period of time to obtain a simulated denatured cartilage.

[0087] In this experiment, in addition to normal cartilage that was not treated with collagenase enzyme (treatment time: 0), simulated degenerated cartilage was prepared with treatment times of 0.5 hours, 1 hour, and 2 hours. Then, using cartilage diagnostic system 1, diagnostic evaluation values ​​were calculated for these normal cartilage and simulated degenerated cartilage, and the correspondence between them was confirmed.

[0088] Figure 11 shows the experimental results. The horizontal axis shows collagenase enzyme treatment time, and the vertical axis shows the estimated results of the degree of degeneration based on the diagnostic evaluation value output by the machine learning model. The results show that the degree of degeneration of normal cartilage after 0 hours of collagenase enzyme treatment is equivalent to 0%, and that of degenerated cartilage after 2 hours of collagenase enzyme treatment is equivalent to 100%.

[0089] The experimental results show that the estimation results by the machine learning model and the actual degree of denaturation (collagenase enzyme treatment time) match with high accuracy. Note that, since the degree of denaturation that doctors can identify in actual medical practice is considered to be equivalent to a collagenase enzyme treatment time of 10 hours or more, it can be seen that this embodiment can identify even degeneration that cannot be artificially identified. For this reason, it is preferable in that cartilage tissue with a higher degree of normality can be harvested when performing autologous cartilage culture.

[0090] As described above, according to this embodiment, the probe 50 is held by the robot manipulator 4, not by a user such as a doctor, and the position and orientation of the contact surface 58 are controlled, so that the user's body movement noise does not affect the acquisition of OCT tomographic images. In other words, the patient's body movement and the doctor's technique rarely affect the diagnosis. In addition, the stepwise posture control by the manipulator 4 allows the contact surface 58 of the probe 50 to be accurately applied to the cartilage surface in the normal direction, and the reproducibility of the load of the compressive load is high. Therefore, a highly accurate diagnostic evaluation value can be obtained. Therefore, cartilage diagnosis using OCT can be made more practical in actual clinical settings.

[0091] In this embodiment, it is sufficient to obtain an OCT tomographic image (one-dimensional tomographic distribution) in the depth z direction. By using a machine learning model, it is not necessary to use a complex algorithm, such as performing tomographic measurement of the strain rate associated with the deformation of cartilage tissue, when diagnosing cartilage. Therefore, diagnostic images can be displayed in a short time, enabling real-time diagnosis in the medical field. Diagnostic evaluation can be performed in parallel with surgery by the surgeon.

[0092] As the OCT uses SS-OCT with a wavelength scanning laser as the light source, there are no mechanical artifacts such as the need to drive a reference mirror, and it has high time resolution and high positional accuracy. This makes it possible to realize a high-precision system at low cost.

[0093] Although a preferred embodiment of the present invention has been described above, it goes without saying that the present invention is not limited to that specific embodiment, and various modifications are possible within the scope of the technical concept of the present invention.

[0094] [Variations] In the above embodiment, an example was shown in which an OCT tomographic image (image data) obtained by processing an optical interference signal is set as an input value in the generation (learning) of a machine learning model and in use after learning. In a modified example, the spectrum (power spectrum) of the optical interference signal may be set as an input value. That is, tomographic information such as a tomographic signal (image) of brightness information (morphological distribution), its spectral tomographic signal, or a Doppler tomographic signal (image) may be acquired as input data.

[0095] In the above embodiment, the mechanical properties of the cartilage tissue are calculated as the "diagnostic evaluation value." This "diagnostic evaluation value" may be the degree of degeneration of the cartilage tissue itself, or a value expressing the viscoelasticity of the cartilage tissue (e.g., viscosity coefficient, elasticity coefficient, etc.). This is because there is a correlation between the degree of degeneration of the cartilage tissue and the viscoelasticity of the cartilage tissue, and it expresses the mechanical properties of the cartilage tissue. The viscosity coefficient or elasticity coefficient may be provided as an output value (denaturation degree label) used during learning.

[0096] In the above embodiment, the method of applying deformation energy (load) is to apply a compressive load that imparts a constant strain rate to the cartilage surface. In a modified example, a load application method based on a stress relaxation method in which a constant strain is applied and then the cartilage comes to rest may be used. Alternatively, a load application method based on a dynamic viscoelasticity method in which dynamic strain is applied to the target area may be used. Alternatively, a load application method based on a creep method in which a constant amount of stress is applied to the cartilage surface may be used.

[0097] Although not mentioned in the above embodiment, a sensor such as a load cell may be attached to the probe 50 itself so that the load applied to the cartilage can be measured. This makes it possible to display the degree of stress relaxation and to display the diagnostic evaluation value of the cartilage tissue from multiple angles.

[0098] In the above embodiment, an example is shown in which a degeneration degree map is superimposed on an actual image of cartilage using AR (Augmented Reality), but the diagnostic evaluation value of cartilage tissue may also be displayed on a user terminal using VR (Virtual Reality), MR (Mixed Reality), or other XR (Extended Reality / Cross Reality).

[0099] In the above embodiment, a system for diagnosing cartilage tissue as a biological tissue has been exemplified, but a biological diagnostic system may be constructed for diagnosing biological tissue of a relatively soft part (biological tissue having viscoelasticity) such as skin, cardiac muscle, ossicles, etc. The biological diagnostic system calculates a diagnostic evaluation value of the biological tissue by processing an optical interference signal output from an optical unit while applying deformation energy by bringing the tip of a probe into contact with the surface of a target part of a living body.

[0100] The present invention is not limited to the above-mentioned embodiment and modified examples, and the components can be modified without departing from the gist of the present invention. Various inventions can be formed by appropriately combining multiple components disclosed in the above-mentioned embodiment and modified examples. In addition, some components can be deleted from all the components shown in the above-mentioned embodiment and modified examples. [Explanation of symbols]

[0101] 1 Cartilage diagnostic system, 2 Optical unit, 4 Manipulator, 6 Control and calculation device, 10 Light source, 12 Object arm, 14 Reference arm, 16 Optical mechanism, 18 Light detection device, 25 Optical fiber, 28 Collimator lens, 30 Condenser lens, 32 Reflector, 46 Camera, 50 Probe, 52 Insertion part, 54 Optical mechanism, 56 Indenter, 58 Contact surface, 60 Collimator lens, 61 Galvanometer mirror, 62 Achromatic lens, 70 Depth camera, 124 Input information acquisition part, 144 Learning part, 156 First attitude control part, 158 Second attitude control part, 164 Evaluation value calculation part, 200 User terminal, 202 Display part, 224 Imaging part, 250 Screen, DA Diagnosis target area, IAn Detection area, J Cartilage, K Knee, M Machine learning model, OR Treatment room.

Claims

1. A cartilage diagnostic system for diagnosing articular cartilage, comprising: an optical unit including an optical system using optical coherence tomography; a probe having a contact surface capable of contacting a surface of cartilage to impart deformation energy thereto, and an optical mechanism for directing light from the optical unit to the cartilage, the optical axis of the probe being set so as to emit the light from the optical unit in a normal direction to the contact surface; a manipulator that holds the probe and moves the probe to change the position and orientation of the contact surface; a control and arithmetic unit that controls the manipulator and processes an optical interference signal output from the optical unit in response to application of deformation energy to the surface of the cartilage to calculate a diagnostic evaluation value of the cartilage tissue; A display device that displays the diagnostic evaluation value of the cartilage tissue; A cartilage diagnostic system comprising:

2. the probe is provided with a depth sensor for detecting a surface shape of the cartilage; The cartilage diagnostic system according to claim 1, characterized in that, prior to the application of deformation energy to the cartilage by the probe, the control and arithmetic device controls the attitude of the manipulator so as to bring the normal direction of the contact surface closer to the normal direction of the surface of the cartilage based on the surface shape detected by the depth sensor.

3. The control and arithmetic device includes: a first attitude control for adjusting an attitude of the manipulator based on a surface shape of the cartilage detected by the depth sensor while the probe is brought close to the cartilage to a first distance; a second attitude control in which, after the first attitude control, the probe is brought close to the cartilage to a second distance smaller than the first distance, a surface shape of the cartilage is calculated based on an optical interference signal obtained without applying the deformation energy, and an attitude of the manipulator is finely adjusted based on the calculated surface shape; The cartilage diagnostic system according to claim 2, wherein the above steps are carried out in stages.

4. The control and arithmetic device includes: an input information acquisition unit that acquires input information based on the optical interference signal; an evaluation value calculation unit that calculates the diagnostic evaluation value by setting the input information as an input value for a mathematical model that calculates the diagnostic evaluation value; 3. The cartilage diagnostic system according to claim 1, further comprising:

5. The cartilage diagnostic system according to claim 4, characterized in that the control and arithmetic device is provided with a learning unit that adjusts the mathematical model by using input information acquired about the cartilage to be learned as an input value and using a set value of the diagnostic evaluation value of the cartilage to be learned as an output value.

6. 5. The cartilage diagnostic system according to claim 4, wherein the evaluation value calculation unit sets time-series data of the input information accompanying the application of the deformation energy as the input value.

7. 6. The cartilage diagnostic system according to claim 5, wherein the evaluation value calculation section sets, as the input value, tomographic information of the cartilage obtained by processing the optical interference signal.

8. 6. The cartilage diagnostic system according to claim 5, wherein the evaluation value calculation section sets the spectrum of the optical interference signal as the input value.

9. A biodiagnostic system for diagnosing mechanical properties based on viscoelasticity of biological tissue, comprising: an optical unit including an optical system using optical coherence tomography; a probe having a contact surface capable of contacting a surface of a target site of a living body to impart deformation energy thereto, and an optical mechanism for guiding light from the optical unit to living tissue, the optical axis of the probe being set so as to emit the light from the optical unit in a normal direction to the contact surface; a manipulator that holds the probe and moves the probe to change the position and orientation of the contact surface; a control and arithmetic unit that controls the manipulator and processes an optical interference signal output from the optical unit in response to application of deformation energy to the surface of the target region to calculate a diagnostic evaluation value of the biological tissue; a display device that displays the diagnostic evaluation value of the biological tissue; A biological diagnostic system comprising:

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