Tilus surgical robot, instrument and bone tunnel planning control method

Abstract

The invention belongs to the field of talus operation equipment, and discloses a talus operation robot, an instrument and a bone tunnel planning control method. The drilling controller is in communication connection with the drilling mechanical arm; the drilling positioning unit is used for being clamped on the talus surgical part of the patient and providing a drilling angle for the drilling mechanical arm; the data acquisition unit is in communication connection with the drilling controller and is used for acquiring a drilling feedback signal in the talus operation process and uploading the drilling feedback signal to the drilling controller; the drilling controller is used for generating a drilling control instruction according to the drilling feedback signal and sending the drilling control instruction to the drilling mechanical arm, and the drilling mechanical arm dynamically adjusts the feeding speed and / or the rotating speed of drilling when responding to the drilling control instruction. According to the invention, the safety and reliability of the whole operation process can be ensured to the maximum extent, and the drilling precision is improved.

Description

Technical Field

[0001] The present invention belongs to the field of talus surgical equipment, and particularly relates to a talus surgical robot, an instrument and a bone tunnel planning and control method. Background Art

[0002] In orthopedic surgery, accurate bone tunnel planning and drilling operations are crucial for the success of the surgery, especially in the relatively rare case of talus fractures. Typical talus fractures are often accompanied by multiple injuries. Due to the unique anatomical structure, fragile blood supply, and multiple complex articular surfaces of the hindfoot of the talus, traumatic arthritis, hindfoot deformity, ischemic necrosis and other teratogenic complications are likely to occur after talus fractures. Therefore, for some simple, non-displaced talus fractures, good results can generally be achieved through conservative treatment. However, for relatively complex talus fractures with obvious displacement and severe damage to surrounding tissues, the surgery is often more difficult and requires precise reduction and fixation to restore the normal anatomical structure and function of the talus. The surgery in this case is more complex, challenging and risky. At the same time, fractures of the talar body are more difficult to handle than fractures of the talar neck.

[0003] Traditional bone tunnel planning methods mainly rely on doctors' experience and manual operations, and have problems such as low precision and complex operations. In recent years, although surgical robots have been applied to spinal or joint replacement, their rigid instruments are difficult to meet the requirements of the angled bone tunnel of the lateral malleolus (for example, the two bone tunnels need to precisely converge within 30°). Existing navigation systems mostly rely on a single imaging modality (such as CT) and cannot fuse soft tissue dynamic information, resulting in a deviation of the planning model from the actual anatomy > 2 mm. Therefore, existing technical solutions still have certain limitations, such as insufficient precision and poor operation flexibility, which greatly increase the risk of talus fracture surgery. Summary of the Invention

[0004] The purpose of the present invention is to provide a talus surgical robot, an instrument and a bone tunnel planning and control method to solve the problems of insufficient precision and poor operation flexibility in the prior art, which greatly increase the risk of talus fracture surgery.

[0005] To achieve the above purpose, the present invention adopts the following technical solutions: In the first aspect, the present invention provides a talus surgical robot, which includes: A drilling robotic arm; A drilling controller, communicatively connected to the drilling robotic arm; A drilling positioning unit, used for clamping on the talus surgical site of the patient and providing a drilling angle for the drilling robotic arm; A data acquisition unit, communicatively connected to the drilling controller, used for collecting drilling feedback signals during the talus surgery and uploading the drilling feedback signals to the drilling controller; The drilling controller is configured to: generate a drilling control instruction according to a drilling feedback signal, and send the drilling control instruction to a drilling robotic arm, and the drilling robotic arm dynamically adjusts the feed speed and / or the rotation speed of drilling in response to the drilling control instruction.

[0006] Preferably, the drilling feedback signal includes: a pressure signal, a temperature signal, and an acoustic wave signal; The data acquisition unit includes: a pressure sensor, a temperature sensor, and an acoustic sensor, and the pressure sensor, the temperature sensor, and the acoustic sensor are all communicatively connected to the drilling controller; The pressure sensor is configured to collect a pressure signal applied by the drilling robotic arm to the talus surgical site during the talus surgery; The temperature sensor is configured to collect a temperature signal of the talus surgical site; The acoustic sensor is configured to collect an acoustic wave signal generated by the drilling robotic arm during drilling.

[0007] In a second aspect, the present invention provides a talus surgical instrument, which serves as a drilling positioning unit of the above-mentioned talus surgical robot, and the talus surgical instrument includes: two metal tubes and an arc telescopic rod, and the two metal tubes are respectively installed at both ends of the arc telescopic rod; A connecting rod is slidably connected to the arc telescopic rod, the axis of the connecting rod is perpendicular to the plane formed by the two metal tubes and the arc telescopic rod, and an orthopedic clamp is installed on the connecting rod.

[0008] Preferably, the arc telescopic rod includes: an arc sleeve, an arc rod is inserted into the arc sleeve, and a first locking screw is provided on the arc sleeve for locking the arc rod in the arc sleeve; A sliding sleeve is sleeved on the arc rod, the connecting rod is fixedly connected to the sliding sleeve, and a second locking screw is further provided on the sliding sleeve for locking the sliding sleeve on the arc rod.

[0009] In a third aspect, the present invention further provides a bone tunnel planning control method, and the method includes: Obtain multi-modal images of the talus surgical site of a patient, and generate a three-dimensional finite element model of the talus surgical site based on the multi-modal images; Based on the three-dimensional finite element model, plan the bone tunnel entrance position, the bone tunnel exit position, and the angulation of the talus surgical site, and install the drilling positioning unit according to the bone tunnel entrance position, the bone tunnel exit position, and the angulation; Obtain a drilling feedback signal during the drilling operation applied to the talus surgical site; Generate a drilling control instruction based on the drilling feedback signal, and send the drilling control instruction to a drilling robotic arm, and the drilling robotic arm dynamically adjusts the feed speed and / or the rotation speed of drilling in response to the drilling control instruction.

[0010] Preferably, the drilling feedback signal includes: a pressure signal, and the pressure signal includes: the axial force and lateral force of the drill bit; Generating a drilling control instruction based on the drilling feedback signal, including: Constructing a relationship model between the axial force of the drill bit and the bone tissue hardness based on a three-dimensional finite element model; Inputting the axial force in the drilling feedback signal into the relationship model to obtain the local hardness of the patient's bone tissue; Determining the patient's bone density and the hardness difference in adjacent regions based on the local hardness of the patient's bone tissue; When the hardness difference in adjacent regions reaches a difference threshold, generating a first control instruction for adjusting the rotation speed of the drilling; when the patient's bone density reaches a mutation threshold, generating a second control instruction for adjusting the feed speed of the drilling.

[0011] Preferably, the method further includes: Judging whether the lateral force reaches a preset lateral force threshold, and if so, generating a third control instruction for controlling the drilling robot arm to stop and generating a prompt for re-planning the drilling path.

[0012] Preferably, the drilling feedback signal further includes: a temperature signal and an acoustic wave signal; Generating a drilling control instruction based on the drilling feedback signal further includes: Performing feature extraction on the acoustic wave signal to obtain the frequency domain energy; Judging whether the frequency domain energy reaches a preset energy and the temperature signal reaches a preset temperature, and if so, generating a fourth control instruction for reducing the rotation speed of the drilling to a preset rotation speed.

[0013] Preferably, the method further includes: Predicting the stress distribution in front of the drill bit of the drilling robot arm based on a three-dimensional finite element model; Generating an optimization control instruction based on the stress distribution in front of the drill bit of the drilling robot arm, and the optimization control instruction is used to optimize the feed speed of the drilling.

[0014] Preferably, the multi-modal image includes: an MRI image, a CT image, and an X-ray image; generating a three-dimensional finite element model of the talus surgical site based on the multi-modal image, including: Extracting the bone structure in the CT image and the X-ray image based on a threshold segmentation algorithm to obtain a bone model; Extracting the ligament contour in the MRI image based on a deep learning algorithm; Mapping the ligament contour in the MRI image to the bone model based on a mutual information registration algorithm to obtain a composite three-dimensional model; Optimize the resolution of the composite three-dimensional model based on the adversarial generative network to obtain an optimized composite three-dimensional model, and use the optimized composite three-dimensional model as the three-dimensional finite element model.

[0015] Beneficial effects: 1. The present invention uses the talus surgical instrument as the drilling positioning unit of the talus surgical robot. The talus surgical instrument mainly consists of two metal tubes, an arc-shaped telescopic rod, a connecting rod, and an orthopedic clamp. By adjusting the arc length of the arc-shaped telescopic rod, the angle of the two metal tubes can be adjusted. After the angle adjustment is completed, the ends of the metal tubes and the ends of the orthopedic clamp form a three-point positioning, which can stably position the surgical site and facilitate the drill of the talus surgical robot to accurately drill the surgical site; 2. The present invention can realize the accurate analysis and visual reconstruction of medical images by obtaining multi-modal images of the talus surgical site of the patient and generating a three-dimensional finite element model of the talus surgical site, providing high-precision visual support for clinical diagnosis and surgical planning; 3. The present invention collects the drilling feedback signal during the talus surgery through the data acquisition unit, combines it with the three-dimensional finite element model of the patient, and monitors and corrects the surgical process in real time, which can ensure the safety and reliability of the entire surgical process to the greatest extent and improve the drilling accuracy. Description of the drawings

[0016] The drawings are used to provide a further understanding of the embodiments of the present invention, and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the embodiments of the present invention, but do not constitute a limitation to the embodiments of the present invention. In the drawings: Figure 1 is a schematic diagram of the overall structure of the talus surgical instrument provided by an embodiment of the present invention; Figure 2 is a schematic diagram of the positioning of the talus surgical instrument provided by an embodiment of the present invention; Figure 3 is a block diagram of the talus surgical robot provided by an embodiment of the present invention; Figure 4 is a flowchart of the bone tunnel planning control method provided by an embodiment of the present invention.

[0017] Description of the reference numerals: 1. Metal tube; 2. Arc-shaped telescopic rod; 3. Connecting rod; 4. Orthopedic clamp; 5. First locking screw; 6. Sliding sleeve; 7. Second locking screw; 8. Sleeve; 9. Mounting sleeve; 10. Third locking screw. Detailed implementation manners

[0018] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the present invention in combination with the accompanying drawings and the descriptions of the embodiments or the prior art. Obviously, the following descriptions of the structures of the accompanying drawings are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings. It should be noted here that the descriptions of these embodiments are used to help understand the present invention, but do not constitute a limitation to the present invention.

[0019] Embodiment 1 Figure 1 is a schematic diagram of the overall structure of a talus surgical instrument provided by an embodiment of the present invention. As Figure 1 shown, this embodiment provides a talus surgical instrument, which includes: two metal tubes 1 and an arc telescopic rod 2. The two metal tubes 1 are respectively installed at both ends of the arc telescopic rod 2; a sleeve 8 can be provided at both ends of the arc telescopic rod 2, and the two metal tubes 1 are inserted into the sleeve 8, and the sleeve 8 is used to fix the metal tubes 1. The connection and fixation method between the sleeve and the metal tube 1 can be a threaded connection or fixed by using a locking screw; among them, the metal tube 1 is made of medical titanium alloy (Ti-6Al-4V), with an inner diameter of 2.0 mm and an outer diameter of 3.5 mm.

[0020] A connecting rod 3 is slidably connected to the arc telescopic rod 2. The axis of the connecting rod 3 is perpendicular to the plane formed by the two metal tubes 1 and the arc telescopic rod 2. An orthopedic clamp 4 is installed on the connecting rod 3, and the orthopedic clamp 4 is fixed to the distal end of the fibula to provide stability during the operation; for the connection method between the orthopedic clamp 4 and the connecting rod 3, an adjustable connection method can also be adopted. For example, an installation sleeve 9 is provided at one end of the orthopedic clamp 4, and the connecting rod 3 is inserted into the installation sleeve 9, and the installation sleeve 9 and the connecting rod 3 can also be fixed by using a third locking screw 10.

[0021] In this embodiment, the arc telescopic rod 2 includes: an arc sleeve, an arc rod is inserted into the arc sleeve, and a first locking screw 5 for locking the arc rod in the arc sleeve is provided on the arc sleeve. A slide rail is provided in the arc sleeve, and a corresponding chute is provided on the arc rod. After the arc rod is inserted into the arc sleeve, it can slide in the arc sleeve to realize the angle adjustment between the two metal tubes 1. The allowable intraoperative adjustment angle range is 10°-45°; therefore, the talus surgical instrument of the present invention enables the doctor to flexibly adjust the relative position and angle between the metal tubes 1 according to needs during the operation, so as to achieve the best surgical effect. After the adjustment is completed, it can be fixed by using a locking screw to ensure the stability during the operation.

[0022] A sliding sleeve 6 is sleeved on the arc-shaped rod, the connecting rod 3 is fixedly connected to the sliding sleeve 6, and a second locking screw 7 for locking the sliding sleeve 6 on the arc-shaped rod is further arranged on the sliding sleeve 6; at this time, the position of the orthopedic clamp 4 can be adjusted to realize the clamping and fixing of fibulas with different shapes.

[0023] In this embodiment, a separate pressure sensor can be installed at the end of the other end of the orthopedic clamp 4 to measure the clamping force between the orthopedic clamp 4 and the fibula in real time, so as to avoid bone damage.

[0024] In this embodiment, when installing the talus surgical instrument, an optical marker with a diameter of 2 mm is provided at the end of the metal tube 1, which is matched with the reflective sphere array of the optical navigation system; the positioning error of the optical navigation system in this embodiment < 0.3 mm, and the refresh frequency ≥ 20 Hz, which is used to track the spatial position of the optical marker at the end of the hollow metal tube 1 in real time to judge whether the installation position of the metal tube 1 is accurate. If the position of the end of the metal tube 1 on the talus is not at the drilling position, the installation position and angle of the metal tube 1 need to be adjusted until the end of the metal tube 1 is aligned with the drilling position.

[0025] The installation and positioning schematic diagram of the talus surgical instrument is as Figure 2 shown. Therefore, the present invention can ensure that the surgical instrument can stably act on the lower end of the fibula, preventing surgical errors caused by instrument shaking.

[0026] Embodiment 2 Figure 3 is a block diagram of a talus surgical robot provided by an embodiment of the present invention. As Figure 3 shown, this embodiment provides a talus surgical robot, which includes: a drilling robotic arm, a drilling controller, a drilling positioning unit, and a data acquisition unit. Among them, the drilling robotic arm can adopt a general-purpose robotic arm on the market, and its specific structure is not described in detail in this embodiment. The drilling positioning unit adopts the talus surgical instrument in Embodiment 1. After the installation of the talus surgical instrument is completed, the drill bit of the drilling robotic arm is placed into the metal tube, so as to realize precise positioning, drilling, fixation, material implantation and other surgical operations; the drilling controller can adopt a microprocessor or a processor of the STM32 series.

[0027] The drilling controller is communicatively connected to the drilling robotic arm and is used to control the feeding speed and rotation speed of the drilling robotic arm during drilling.

[0028] The drilling positioning unit is used to clamp onto the talus surgical site of the patient and provide a drilling angle for the drilling robotic arm. In this embodiment, by adjusting the arc length of the arc-shaped telescopic rod of the talus surgical instrument, the angles of the two metal tubes are further adjusted. After the angle adjustment is completed, the ends of the metal tubes and the ends of the orthopedic clamp form a three-point positioning, which can stably position the surgical site and facilitate the drill bit of the talus surgical robot to accurately drill the surgical site.

[0029] The data acquisition unit is communicatively connected to the drilling controller and is used to collect the drilling feedback signals during the talus surgery process and upload the drilling feedback signals to the drilling controller; the drilling controller is used to: generate a drilling control instruction according to the drilling feedback signals and send the drilling control instruction to the drilling robotic arm, and the drilling robotic arm dynamically adjusts the feeding speed and / or the rotation speed of the drilling when responding to the drilling control instruction.

[0030] As a further optimization of this embodiment, the drilling feedback signals include: pressure signals, temperature signals, and acoustic signals.

[0031] The data acquisition unit includes: a pressure sensor, a temperature sensor, and an acoustic sensor, and the pressure sensor, the temperature sensor, and the acoustic sensor are all communicatively connected to the drilling controller; The pressure sensor is used to collect the pressure signals exerted by the drilling robotic arm on the talus surgical site during the talus surgery process. The pressure signals exerted on the talus surgical site mainly include: axial force and lateral force; The temperature sensor is used to collect the temperature signals of the talus surgical site; The acoustic sensor is used to collect the acoustic signals generated by the drilling robotic arm during drilling.

[0032] Therefore, during the drilling surgery process of the present invention, by real-time monitoring and analyzing the pressure signals, temperature signals, and acoustic signals, the drilling speed and path are dynamically corrected to ensure that the bone tunnel alignment error ≤ 1.0 mm and the angle deviation ≤ 1.5°, and avoid overheating damage to tissues, solve the problems of unstable bone tunnel positioning and high operation risk in talus surgery, significantly improve the surgical accuracy and safety, and are applicable to the clinical treatment of repair of anterior talofibular ligament injury, talus fracture, etc.

[0033] Embodiment Three Figure 4 It is a flowchart of a bone tunnel planning control method provided by an embodiment of the present invention. As Figure 2 shown, this embodiment provides a bone tunnel planning control method, which is implemented based on the drilling controller of the talus surgical robot in Embodiment Two, that is, this method runs on the drilling controller. The method includes: Step S10: Obtain multi-modal images of the talus surgical site of the patient, and generate a three-dimensional finite element model of the talus surgical site based on the multi-modal images.

[0034] In this embodiment, the multi-modal images include: MRI (Nuclear Magnetic Resonance Imaging) images, CT (Computed Tomography) images, and X-ray images. Among them, MRI images can be obtained by detecting the patient with a nuclear magnetic resonance instrument to collect MRI images, and the nuclear magnetic resonance instrument transmits the collected MRI images to the drilling controller through a data transmission protocol; similarly, CT images and X-ray images are also collected using a CT scanner and an X-ray scanner, and then the collected CT images and X-ray images are sent to the drilling controller.

[0035] Therefore, generating a three-dimensional finite element model of the talus surgical site based on the multi-modal images includes: Step S101: Based on the threshold segmentation algorithm, extract the bone structures in the CT images and X-ray images to obtain a bone model; among them, the threshold segmentation algorithm can adopt algorithms such as U-Net algorithm and Mask R-CNN algorithm to perform pixel-level classification on the CT images and X-ray images; U-Net shows efficient feature learning ability in medical image segmentation due to its encoder-decoder structure and skip connection characteristics; while Mask R-CNN is suitable for instance segmentation in complex scenarios through the dual mechanisms of object detection and mask generation.

[0036] Step S102: Based on the deep learning algorithm, extract the ligament contours in the MRI images; the deep learning algorithm can also adopt algorithms such as U-Net algorithm and Mask R-CNN algorithm.

[0037] Step S103: Map the ligament contours in the MRI images to the bone model based on the mutual information registration algorithm to obtain a composite three-dimensional model. At this time, the composite three-dimensional model is a comprehensive anatomical model including bones, tendons, ligaments, and blood vessels, which can provide high-precision visualization support for clinical diagnosis and surgical planning; among them, the mutual information (MI) registration algorithm is a technology widely used in the field of medical image processing, especially when aligning images of different modalities (such as MRI and CT).

[0038] Step S104: Optimize the resolution of the composite three-dimensional model based on the generative adversarial network to obtain an optimized composite three-dimensional model, and use the optimized composite three-dimensional model as the three-dimensional finite element model; the generative adversarial network in this embodiment includes a generator and a discriminator. Input the CT image, MRI image, and X-ray image into the generator of the U-Net architecture to generate a synthetic image highly consistent with the real CT image, and perform adversarial training in combination with a convolutional neural network (CNN) discriminator to accurately map the ligament contour in the MRI image onto the bone model, thereby optimizing the resolution of the bone-soft tissue interface of the composite three-dimensional model until it is below 0.2 mm.

[0039] In this embodiment, the U-Net network is trained using preoperative MRI annotation data to output the probability heat map of the footprint area of the anterior talofibular ligament, and its biomechanical rationality is verified through morphological post-processing and finite element mechanical simulation. Subsequently, based on the Monte Carlo algorithm, 10,000 groups of bone path parameters are randomly generated within the preoperatively planned drilling range, and the stress distribution and temperature risk are predicted by combining finite element simulation. Dynamically fuse the intraoperative sensor feedback (such as the temperature overrun penalty term), and select the top 5% of the paths with the highest scores using a comprehensive evaluation function (including path deviation, temperature risk, and soft tissue injury probability). Finally, ensure that the angle between the bone tunnel axis and the long axis of the talus is ≤5°, the depth error is ≤1 mm, and avoid the footprint area and the vascular dense area to achieve the global optimal solution of safety and accuracy.

[0040] Step S20: Based on the three-dimensional finite element model, plan the bone tunnel entrance position, bone tunnel exit position, and angulation angle of the talus surgical site, and install the drilling positioning unit according to the bone tunnel entrance position, bone tunnel exit position, and angulation angle.

[0041] In this embodiment, the finite element analysis software is used to perform mechanical simulation on the three-dimensional finite element model. Input parameters such as the elastic modulus and yield strength of the talus to simulate the stress distribution and deformation characteristics during bone tunnel drilling, which can simulate the stress concentration and bone tissue deformation generated during the drilling process, help identify areas that may cause risks such as fractures, vascular injuries, and nerve injuries. When determining the optimal bone tunnel position (including parameters such as the bone tunnel entrance position, bone tunnel exit position, and angulation angle) subsequently, the surgical risk can be reduced and the surgical accuracy can be improved by avoiding these high-risk areas.

[0042] For the method of determining the optimal bone tunnel position, the generative adversarial network (GAN) can be used to optimize the similarity evaluation between multi-modal images, automatically identify the footprint area of the anterior talofibular ligament, and screen the optimal bone tunnel position within the drillable range of the talus through the Monte Carlo algorithm to ensure that the angle between the bone tunnel axis and the long axis of the talus is ≤5°, and the depth error is ≤1 mm.

[0043] After determining the optimal bone tunnel position, it is positioned and installed through the talus surgical instrument in the first embodiment. After installing the talus surgical instrument, the drilling robotic arm of the talus surgical robot is placed into the metal tube, and drilling operation is performed on the optimal bone tunnel position.

[0044] Step S30: Obtain the drilling feedback signal during the drilling operation on the talus surgical site; the pressure signal, temperature signal, and acoustic signal in this embodiment.

[0045] Among them, the pressure signal is collected by a pressure sensor, and the pressure sensor uploads the collected pressure signal to the drilling controller; the pressure sensor adopts a six-axis force sensor with a measuring range of 0 - 50 N and an accuracy of ±0.1 N, which is used to monitor the axial force (Fz) and lateral forces (Fx, Fy) of the drill bit. At this time, the pressure signal includes the axial force and lateral forces.

[0046] Among them, the temperature signal is collected by a temperature sensor, and the temperature sensor uploads the collected temperature signal to the drilling controller; the temperature sensor adopts an infrared thermal imager (resolution 0.1°C, measuring range 0 - 100 °C), which is used to monitor the temperature change in the drilling area in real time. When the temperature in the drilling area exceeds 42°C, the cooling system is triggered (saline perfusion rate 10 mL / min).

[0047] Among them, the acoustic signal is collected by an acoustic sensor, and the acoustic sensor uploads the collected acoustic signal to the drilling controller; the acoustic sensor adopts a piezoelectric microphone (frequency response range 20 Hz - 20 kHz) to capture the acoustic signal during the drilling process, and identifies the characteristic frequency of bone tunnel penetration (typical value: 2 - 5 kHz) through short-time Fourier transform (STFT).

[0048] Step S40: Generate a drilling control instruction based on the drilling feedback signal, and send the drilling control instruction to the drilling robotic arm. The drilling robotic arm dynamically adjusts the feed speed and / or rotation speed of the drilling in response to the drilling control instruction, and the rotation speed is the drill speed of the drill bit.

[0049] As a further optimization of this embodiment, generating a drilling control instruction based on the drilling feedback signal includes: Step a10: Construct a relationship model between the axial force of the drill bit and the bone tissue hardness based on the three-dimensional finite element model.

[0050] In this embodiment, the pressure sensor monitors the axial force (range 0 - 200 N) and torque (0 - 5 Nm), eliminates noise through Kalman filtering, and then uses the drilling mechanics theory to establish a relationship model between the axial force of the drill bit and the bone tissue hardness. The expression of the relationship model is: (1); In formula (1),A is the cross-sectional area of the drill bit (m²), k is the material characteristic coefficient (determined through in vitro bone sample calibration experiments), H is the bone tissue hardness (Pa), Fz is the axial force of the drill bit (N), v is the feed rate of the drill bit (mm / s), n is the rotational speed of the drill head (revolutions per minute, rpm).

[0051] Step a20: Input the axial force in the drilling feedback signal into the relational model to obtain the local bone tissue hardness of the patient; Further introduce a Kalman filter to denoise the original axial force and eliminate the interference caused by drill bit vibration or local non-uniformity of bone tissue. Subsequently, input the preprocessed axial force data into a three-dimensional finite element model, and combine the bone density distribution (gray value, HU) reconstructed from the preoperative CT image to establish the mapping relationship between bone density ( ρ ), and bone tissue hardness ( H ), as follows: (2); In formula (2), α,β is the regression coefficient, obtained by machine learning fitting of the bone sample database.

[0052] Step a30: Based on the local bone tissue hardness of the patient, determine the bone density of the patient and the hardness difference in adjacent regions.

[0053] Step a40: When the hardness difference in adjacent regions reaches the difference threshold, generate a first control instruction for adjusting the rotational speed of the drilling; when the bone density of the patient reaches the mutation threshold, generate a second control instruction for adjusting the feed rate of the drilling.

[0054] During the drilling process, calculate the local bone hardness in real time according to formula (1). When it is detected that the hardness difference in adjacent regions > 10% (such as transitioning from cancellous bone to cortical bone), automatically trigger the drill speed adjustment (200 → 400 rpm) to match the hardness change.

[0055] As a further optimization of this embodiment, the method further includes: determining whether the lateral force reaches a preset lateral force threshold. If so, generate a third control instruction for controlling the drilling robotic arm to stop and generate a prompt for re-planning the drilling path; for example: when the lateral force (Fx or Fy) detected by the six-axis force sensor > 5N, start the path re-planning based on the three-dimensional finite element model, and the error compensation accuracy ≤ 0.5 mm.

[0056] As a further optimization of this embodiment, generating a drilling control instruction based on the drilling feedback signal further includes: Step b10: Extract features from the acoustic wave signal to obtain the frequency-domain energy; Step b20: Determine whether the frequency-domain energy reaches a preset energy and the temperature signal reaches a preset temperature. If so, generate a fourth control instruction for reducing the rotational speed of the drill to a preset rotational speed. For example, when the frequency-domain energy of the acoustic wave signal > 50 dB and the temperature > 42 °C, trigger the drill speed to be reduced to 50% of the set value.

[0057] In this embodiment, the identification of bone canal penetration mainly relies on the characteristic frequency components of the acoustic wave signal in the 2 - 5 kHz frequency band. Specifically, when the drill bit penetrates the bone layer, due to the sudden reduction of bone tissue resistance and structural fracture, a significant transient energy peak will be generated in this frequency band in the acoustic wave signal. More specifically, in terms of signal processing, the short-time Fourier transform (STFT) divides the acoustic wave signal according to a time window (such as a 50 ms window), and extracts the energy distribution within each window through spectral analysis. When the energy density in the 2 - 5 kHz frequency band is significantly higher than the background noise (signal-to-noise ratio > 10 dB) and shows a steep rising edge, it is determined as a penetration event; through multi-source verification, combined with the sudden drop of the axial force (Fz) detected by the six-axis force sensor (such as from 20 N to below 5 N) and the local temperature stability monitored by the temperature sensor (< 42 °C), false judgments caused by local uneven bone density or drill bit wear can be excluded to ensure the reliability of the characteristic frequency.

[0058] Therefore, during the operation, the data acquisition unit real-time collects the sensor parameters in the operation area, including pressure changes, temperature changes, and acoustic wave changes, etc. The pressure sensor monitors the six-axis interaction force between the front end of the bone drill and the talus tissue, and these data are used to simulate the deformation characteristics of the bone tissue in the three-dimensional finite element model, so as to realize the real-time monitoring and correction of the operation process. The temperature sensor monitors the temperature change of the lateral malleolus during the drilling process to prevent overheating from causing necrosis of the lateral malleolus tissue and ensure the safety of the operation. With the joint support of the above multi-source information, the safety and reliability of the whole process can be ensured to the greatest extent.

[0059] As a further optimization of this embodiment, the method further includes: Step c10: Predict the stress distribution in front of the drill bit of the drilling robotic arm based on the three-dimensional finite element model; Step c20: Generate an optimization control instruction based on the stress distribution in front of the drill bit of the drilling robotic arm, and the optimization control instruction is used to optimize the feed speed of the drill.

[0060] In this embodiment, the stress distribution in front of the drill bit is predicted through three-dimensional finite element model simulation, and the feed rate is dynamically optimized (0.1 → 0.05 mm / s) to avoid drill bit deviation or bone fracture caused by excessive hardness. Finally, the model is verified by an intraoperative acoustic sensor (hardness mutation accompanied by a characteristic frequency shift > 2 kHz), forming a closed-loop feedback of "mechanical measurement - density mapping - dynamic control" to achieve real-time modeling and adaptive drilling of bone tissue hardness.

[0061] As a further optimization of this embodiment, after the operation, a postoperative three-dimensional finite element model is generated by combining the intraoperative data and postoperative imaging data, and postoperative evaluation is performed. By calculating the actual position, angle, and depth of the drilled hole and comparing them with the preoperative plan, the accuracy of the operation is evaluated. At the same time, the system automatically generates a postoperative report, including parameters such as the three-dimensional coordinates of the bone channel, peak temperature, and mechanical curve, compares them with the preoperative plan data, calculates the surgical deviation rate, and provides a reference for the doctor's subsequent treatment.

[0062] The above are only embodiments of the present application and are not used to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. An ankle surgery robot, characterized in that, The robot includes: A drilling robotic arm; A drilling controller communicatively connected to the drilling robotic arm; A drilling positioning unit configured to clamp onto the talus surgical site of a patient and provide a drilling angle for the drilling robotic arm; A data acquisition unit communicatively connected to the drilling controller, configured to acquire drilling feedback signals during the talus surgery process and upload the drilling feedback signals to the drilling controller; The drilling controller is configured to: generate a drilling control instruction according to the drilling feedback signals, and send the drilling control instruction to the drilling robotic arm, and the drilling robotic arm dynamically adjusts the feed speed and / or rotation speed of the drilling in response to the drilling control instruction.

2. The talus surgical robot according to claim 1, wherein The drilling feedback signals include: pressure signals, temperature signals, and acoustic signals; The data acquisition unit includes: a pressure sensor, a temperature sensor, and an acoustic sensor, and the pressure sensor, the temperature sensor, and the acoustic sensor are all communicatively connected to the drilling controller; The pressure sensor is configured to acquire the pressure signals exerted by the drilling robotic arm on the talus surgical site during the talus surgery process; The temperature sensor is configured to acquire the temperature signals of the talus surgical site; The acoustic sensor is configured to acquire the acoustic signals generated by the drilling robotic arm during drilling.

3. An ankle bone surgical instrument, characterized in that, Using a talus surgical instrument as the drilling positioning unit of the talus surgical robot according to any one of claims 1-2, the talus surgical instrument includes: two metal tubes (1) and an arc-shaped telescopic rod (2), and the two metal tubes (1) are respectively installed at both ends of the arc-shaped telescopic rod (2); A connecting rod (3) is slidably connected to the arc-shaped telescopic rod (2), the axis of the connecting rod (3) is perpendicular to the plane formed by the two metal tubes (1) and the arc-shaped telescopic rod (2), and an orthopedic clamp (4) is installed on the connecting rod (3).

4. The talus surgical instrument according to claim 3, wherein The arc-shaped telescopic rod (2) includes: an arc-shaped sleeve, an arc-shaped rod is inserted into the arc-shaped sleeve, and a first locking screw (5) for locking the arc-shaped rod in the arc-shaped sleeve is provided on the arc-shaped sleeve; A sliding sleeve (6) is sleeved on the arc-shaped rod, the connecting rod (3) is fixedly connected to the sliding sleeve (6), and a second locking screw (7) for locking the sliding sleeve (6) on the arc-shaped rod is further provided on the sliding sleeve (6).

5. A method for controlling bone tunnel planning, which is implemented based on the drilling controller of the talus surgical robot according to any one of claims 1-2, characterized in that, The method includes: Obtaining multi-modal images of the talus surgical site of a patient, and generating a three-dimensional finite element model of the talus surgical site based on the multi-modal images; Based on the three-dimensional finite element model, planning the bone tunnel entrance position, bone tunnel exit position, and angulation angle of the talus surgical site, and installing the drilling positioning unit according to the bone tunnel entrance position, bone tunnel exit position, and angulation angle; Obtaining drilling feedback signals during the drilling operation applied to the talus surgical site; Generating a drilling control instruction based on the drilling feedback signals, and sending the drilling control instruction to the drilling robotic arm, and the drilling robotic arm dynamically adjusts the feed speed and / or rotation speed of the drilling in response to the drilling control instruction.

6. The bone tunnel planning control method according to claim 5, characterized in that, The drilling feedback signals include: pressure signals, and the pressure signals include: the axial force and lateral force of the drill bit; Generating a drilling control instruction based on the drilling feedback signals includes: Constructing a relationship model between the axial force of the drill bit and the bone tissue hardness based on the three-dimensional finite element model; Input the axial force in the drilling feedback signal into the relationship model to obtain the local hardness of the patient's bone tissue; Based on the local hardness of the patient's bone tissue, determine the patient's bone density and the hardness difference in the adjacent area; When the hardness difference in the adjacent area reaches the difference threshold, generate a first control instruction for adjusting the rotational speed of the drill; when the patient's bone density reaches the mutation threshold, generate a second control instruction for adjusting the feed rate of the drill.

7. The bone tunnel planning control method according to claim 6, wherein The method further includes: Judge whether the lateral force reaches the preset lateral force threshold. If so, generate a third control instruction for controlling the drilling robotic arm to stop and generate a prompt for re-planning the drilling path.

8. The bone tunnel planning control method according to claim 6, characterized in that The drilling feedback signal further includes: a temperature signal and an acoustic wave signal; Generating a drilling control instruction based on the drilling feedback signal further includes: Extract the feature of the acoustic wave signal to obtain the frequency domain energy; Judge whether the frequency domain energy reaches the preset energy and the temperature signal reaches the preset temperature. If so, generate a fourth control instruction for reducing the rotational speed of the drill to the preset rotational speed.

9. The bone tunnel planning control method according to claim 6, wherein The method further includes: Predict the stress distribution in front of the drill bit of the drilling robotic arm based on the three-dimensional finite element model; Generate an optimization control instruction based on the stress distribution in front of the drill bit of the drilling robotic arm, and the optimization control instruction is used to optimize the feed rate of the drill.

10. The bone tunnel planning control method according to claim 5, characterized in that, The multi-modal images include: MRI images, CT images, and X-ray images; generating a three-dimensional finite element model of the talus surgical site based on the multi-modal images includes: Extract the bone structure in the CT image and the X-ray image based on the threshold segmentation algorithm to obtain a bone model; Extract the ligament contour in the MRI image based on the deep learning algorithm; Map the ligament contour in the MRI image to the bone model based on the mutual information registration algorithm to obtain a composite three-dimensional model; Optimize the resolution of the composite three-dimensional model based on the generative adversarial network to obtain an optimized composite three-dimensional model, and use the optimized composite three-dimensional model as the three-dimensional finite element model.

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