Comprehensive-orthopedic multi-indication artificial-intelligence surgical robotic system

Abstract

Provided in the present application is a comprehensive-orthopedic multi-indication artificial-intelligence surgical robotic system, comprising: a total hip arthroplasty module, which is used for performing preoperative planning, intraoperative total hip arthroplasty, and real-time display; a total knee arthroplasty module, which is used for performing preoperative planning, intraoperative total knee arthroplasty, and real-time display; a unicompartmental knee arthroplasty module, which is used for performing preoperative planning, intraoperative unicompartmental knee arthroplasty, and real-time display; a periacetabular osteotomy module, which is used for performing preoperative planning, intraoperative periacetabular osteotomy, and real-time display; a sports medicine module, which is used for bone tunnel planning, ligament reconstruction, and real-time display; a pedicle screw fixation navigation module, which is used for performing intraoperative navigation and positioning during pedicle screw fixation, and real-time display; and a trauma positioning and navigation module, which is used for performing intraoperative trauma positioning and navigation, and real-time display. By means of the embodiments of the present application, assistance can be quickly and accurately provided during surgery.

Description

Comprehensive orthopedic multi-disease artificial intelligence surgical robot system Technical Field

[0001] This application belongs to the field of surgical robot technology, and in particular relates to an artificial intelligence surgical robot system for multiple diseases in orthopedics. Background Technology

[0002] Currently, procedures such as total hip replacement, total knee replacement, unicompartmental hip replacement, periacetabular osteotomy, sports medicine, spinal screw placement positioning and navigation, and trauma positioning and navigation mainly rely on doctors' experience, but the efficiency and accuracy are not good.

[0003] Therefore, how to perform assisted surgery quickly and accurately is a technical problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0004] This application provides an artificial intelligence surgical robot system for multiple orthopedic diseases, which can perform assisted surgery quickly and accurately.

[0005] This application provides a comprehensive orthopedic multi-disease artificial intelligence surgical robot system, including:

[0006] The total hip replacement module is used for preoperative planning, intraoperative total hip replacement, and real-time display.

[0007] The total knee replacement module is used for preoperative planning, intraoperative total knee replacement, and real-time display.

[0008] The unicompartmental joint replacement module is used for preoperative planning, intraoperative unicompartmental joint replacement, and real-time display.

[0009] The periacetabular osteotomy module is used for preoperative planning, intraoperative periacetabular osteotomy, and real-time display.

[0010] The sports medicine module is used for bone tunnel planning and ligament reconstruction, and displays the results in real time.

[0011] The spinal screw placement positioning and navigation module is used for intraoperative spinal screw placement positioning and navigation and displays the results in real time.

[0012] The trauma localization and navigation module is used for intraoperative trauma localization and navigation and displays the results in real time.

[0013] Optional, the total hip replacement module is configured as follows:

[0014] Preoperative segmentation and three-dimensional reconstruction of hip joint CT images were performed to obtain a three-dimensional model of the hip joint.

[0015] Preoperative planning based on a 3D model of the hip joint;

[0016] Intraoperative point cloud registration, grinding, pressing, positioning, navigation, and real-time display.

[0017] Optional, the total knee replacement module is configured as follows:

[0018] Preoperative segmentation and three-dimensional reconstruction of knee joint CT images were performed to obtain a three-dimensional model of the knee joint.

[0019] Preoperative planning based on a 3D model of the knee joint;

[0020] Intraoperative point cloud registration, osteotomy, and gap balancing are displayed in real time.

[0021] Optional, the unicompartmental arthroplasty module is configured as follows:

[0022] Preoperative segmentation and three-dimensional reconstruction of knee joint CT images were performed to obtain a three-dimensional model of the knee joint.

[0023] Preoperative planning based on a 3D model of the knee joint;

[0024] Intraoperative point cloud registration, osteotomy, and gap balancing are displayed in real time.

[0025] Optional, the periacetabular osteotomy module is configured to: preoperative planning, intraoperative registration, osteotomy, positioning, navigation and real-time display.

[0026] Optional features include preoperative planning, intraoperative registration, osteotomy, localization, navigation, and real-time display, including:

[0027] Obtain images of the patient's hip joint;

[0028] The hip joint image is segmented and reconstructed in three dimensions to obtain a three-dimensional model of the hip joint;

[0029] Using a 3D model of the hip joint, the osteotomy surfaces, guide lines, and safe zones around the acetabulum are planned preoperatively, the boundaries of the safe zones are determined, and the areas are rendered and colored. The preoperative planning results include osteotomy planning based on the anterior ischium, superior pubic ramus, superior iliac bone, and posterior column.

[0030] During the operation, the NDI reflective positioning and navigation system of the infrared camera and the positioning frame on the curved blade of the bone scalpel are used to obtain the position of the end of the bone scalpel in real time when performing osteotomy according to the preoperative plan.

[0031] Optional, the sports medicine module is configured as follows:

[0032] Acquire the first CT image of the knee joint;

[0033] CT image segmentation based on deep learning neural networks;

[0034] 3D modeling of the knee joint is performed based on the segmentation results;

[0035] Establish preoperative guiding points and plan preoperative registration points in the knee joint;

[0036] Bone tunnel planning and ligament reconstruction;

[0037] During the operation, a probe is used to collect intraoperative registration points based on the preoperative guidance points;

[0038] The probe is used to complete the rigid registration of preoperative and intraoperative registration points based on digital twin technology; the rigid registration includes two parts: coarse registration and fine registration.

[0039] Based on the registration results, the coordinate system of the robotic arm, optical positioning tracker, and knee joint is established;

[0040] Controlling the robotic arm to move autonomously to reconstruct bone tunnels.

[0041] Optionally, rigid registration of preoperative and intraoperative registration points based on digital twin technology can be achieved using probes, including:

[0042] First, initial registration is performed using intraoperative and preoperative registration points. At the same time, the position of the probe is located using digital twin technology, and the three-dimensional skeleton is mapped onto the color image. The position of the three-dimensional skeleton is monitored in real time using the position of the probe tip, and the initial registration matrix is ​​corrected.

[0043] The registration matrix is ​​automatically adjusted again based on the corrected initial registration matrix to obtain the final rigid registration matrix;

[0044] The digital twin approach uses a probe tracking algorithm based on multimodal and multiscale fusion to track the probe in real time, and a probe tip positioning algorithm based on multimodal and multiscale fusion to locate the position of the probe tip in real time.

[0045] Optionally, the spinal screw placement positioning and navigation module is configured as follows:

[0046] Record the patient's basic information during the operation;

[0047] Take CT images of the patient's spine;

[0048] 3D registration based on a 3D calibrator;

[0049] Intraoperative planning and real-time positioning and navigation.

[0050] Optionally, the trauma localization and navigation module is configured as follows:

[0051] Record the patient's basic information during the operation;

[0052] Take X-ray images of the patient's wound site;

[0053] Two-dimensional registration is performed based on a two-dimensional calibrator.

[0054] Intraoperative planning and real-time positioning and navigation.

[0055] This application provides an artificial intelligence surgical robot system for multiple orthopedic diseases, which can perform assisted surgery quickly and accurately.

[0056] This application provides a comprehensive orthopedic multi-disease artificial intelligence surgical robot system, including:

[0057] The total hip replacement module is used for preoperative planning, intraoperative total hip replacement, and real-time display.

[0058] The total knee replacement module is used for preoperative planning, intraoperative total knee replacement, and real-time display.

[0059] The unicompartmental joint replacement module is used for preoperative planning, intraoperative unicompartmental joint replacement, and real-time display.

[0060] The periacetabular osteotomy module is used for preoperative planning, intraoperative periacetabular osteotomy, and real-time display.

[0061] The sports medicine module is used for bone tunnel planning and ligament reconstruction, and displays the results in real time.

[0062] The spinal screw placement positioning and navigation module is used for intraoperative spinal screw placement positioning and navigation and displays the results in real time.

[0063] The trauma localization and navigation module is used for intraoperative trauma localization and navigation and displays the results in real time. Attached Figure Description

[0064] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0065] Figure 1 is a schematic diagram of the modular structure of an artificial intelligence surgical robot system for multiple diseases in orthopedics provided in an embodiment of this application;

[0066] Figure 2 is a schematic diagram of the structure of an end effector for total hip replacement provided in one embodiment of this application;

[0067] Figure 3 is a schematic diagram of a medical bone drill for total hip replacement provided in one embodiment of this application;

[0068] Figure 4 is a schematic diagram of the structure of an end connector for total knee or unicompartmental joint replacement provided in one embodiment of this application;

[0069] Figure 5 is a schematic diagram of the structure of a medical oscillating saw for total knee or unicompartmental joint replacement provided in one embodiment of this application;

[0070] Figure 6 is a schematic diagram of the structure of an active light-emitting end-effector provided in an embodiment of this application for periacetabular osteotomy, sports medicine, spinal screw placement positioning navigation, and trauma positioning navigation.

[0071] Figure 7 is a schematic diagram of the structure of a guide for periacetabular osteotomy, sports medicine, spinal screw placement positioning navigation, and trauma positioning navigation provided in one embodiment of this application;

[0072] Figure 8 is a schematic diagram of the system structure of an artificial intelligence surgical robot for multiple diseases in orthopedics provided in one embodiment of this application.

[0073] Figure 9 shows a schematic diagram of the structure of the electronic device provided in an embodiment of this application. Detailed Implementation

[0074] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0075] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0076] To address the problems of existing technologies, this application provides a comprehensive orthopedic multi-disease artificial intelligence surgical robot system. The comprehensive orthopedic multi-disease artificial intelligence surgical robot system provided in this application will be described below.

[0077] Figure 1 is a schematic diagram of the modular structure of a multi-disease artificial intelligence surgical robot system for orthopedics provided in one embodiment of this application. As shown in Figure 1, the multi-disease artificial intelligence surgical robot system for orthopedics includes:

[0078] S101, Total Hip Replacement Module, used for preoperative planning, intraoperative total hip replacement, and real-time display;

[0079] S102, Total Knee Replacement Module, used for preoperative planning, intraoperative total knee replacement, and real-time display;

[0080] S103, Unicompartmental Joint Replacement Module, used for preoperative planning, intraoperative unicompartmental joint replacement, and real-time display;

[0081] S104, Periacetabular osteotomy module, used for preoperative planning, intraoperative periacetabular osteotomy and real-time display;

[0082] S105, Sports Medicine Module, used for bone tunnel planning and ligament reconstruction and real-time display;

[0083] S106, Spinal screw placement positioning and navigation module, used for intraoperative spinal screw placement positioning and navigation and real-time display;

[0084] S107, Trauma Localization and Navigation Module, used for intraoperative trauma localization and navigation and real-time display.

[0085] In one embodiment, the total hip replacement module is used for:

[0086] Preoperative segmentation and three-dimensional reconstruction of hip joint CT images were performed to obtain a three-dimensional model of the hip joint.

[0087] Preoperative planning based on a 3D model of the hip joint;

[0088] Intraoperative point cloud registration, grinding, pressing, positioning, navigation, and real-time display.

[0089] This module includes functions such as hip joint CT image segmentation and 3D reconstruction, preoperative planning, intraoperative point cloud registration, reshaping, compression, localization, navigation, and real-time display. The specific steps are as follows:

[0090] 1. Preoperative preparation:

[0091] 1.1 Acquisition of CT images of the hip joint:

[0092] Objective: To obtain CT scan data of the patient's hip joint for subsequent processing.

[0093] step:

[0094] 1. CT scan: Perform multi-slice CT scans on the patient's hip joint to obtain high-resolution tomographic images.

[0095] 2. Image data transmission: The raw data from the CT scan is transmitted to the image processing system of the total hip replacement module.

[0096] 1.2 CT Image Segmentation:

[0097] Objective: To separate the hip joint and surrounding tissues from CT images using automatic or semi-automatic segmentation algorithms.

[0098] step:

[0099] 1. Image preprocessing: Denoising, contrast enhancement, and image filtering are performed on CT images to improve the subsequent segmentation results.

[0100] 2. Segmentation Algorithm Application: Using deep learning-based or traditional segmentation algorithms (such as region growing, threshold segmentation, or convolutional neural networks CNN) to segment structures such as the hip joint, femoral head, and acetabulum, generating binarized or multi-class segmented images of the hip joint region.

[0101] 3. Segmentation result correction: Manual correction of automatic segmentation results to ensure accurate segmentation of joint areas.

[0102] 1.3 Three-dimensional reconstruction:

[0103] Objective: To reconstruct a three-dimensional model of the hip joint based on segmented CT images.

[0104] step:

[0105] 1. Application of 3D Reconstruction Algorithms: Using 3D modeling techniques such as volume rendering and surface reconstruction, the segmented hip joint images are reconstructed into 3D models, clearly showing key structures such as the femoral head and acetabulum.

[0106] 2. Model Refinement: The reconstructed 3D model is smoothed to remove noise and irregular surfaces, making the model more realistic.

[0107] 3. 3D model annotation: Annotate key points and anatomical structures of each part of the hip joint for use in subsequent preoperative planning.

[0108] 1.4 Preoperative planning:

[0109] Objective: To conduct surgical simulation planning based on a 3D reconstruction model to ensure precise intraoperative operation.

[0110] step:

[0111] 1. Prosthesis selection: Select the appropriate hip joint prosthesis model and size based on the patient's hip joint anatomy.

[0112] 2. Implant placement planning: Simulate the implant placement, angle, and depth on a 3D model to ensure optimal postoperative functional recovery of the implant.

[0113] 3. Surgical path design: Design the operation path for grinding, cutting, and prosthesis implantation, and generate a surgical plan.

[0114] 2. Intraoperative procedures:

[0115] 2.1 Point Cloud Registration:

[0116] Objective: To register the preoperatively planned 3D model with the intraoperative point cloud data to achieve precise positioning.

[0117] step:

[0118] 1. Point cloud data acquisition: Use an intraoperative 3D scanner (such as laser scanning or optical scanning) to acquire point cloud data of the patient's hip joint in real time.

[0119] 2. Registration algorithm application: The point cloud data acquired during the operation is registered with the 3D model planned before the operation in a rigid or non-rigid manner to ensure that the intraoperative navigation is consistent with the planning.

[0120] 3. Error correction: Through registration algorithm optimization, errors are corrected to ensure the accuracy of real-time display.

[0121] 2.2 Grinding and Implantation:

[0122] Objective: To perform reshaping and prosthesis implantation of the femoral head and acetabulum under the guidance of a navigation system.

[0123] step:

[0124] 1. Navigation guidance: The navigation system guides the position and angle of surgical instruments to ensure the accuracy of the grinding process.

[0125] 2. Femoral head reshaping: The surgical instruments are guided on the registered 3D model to precisely reshape the femoral head, removing diseased tissue and preparing for prosthesis implantation.

[0126] 3. Acral reshaping: The acetabulum is cut and shaped to ensure a good fit with the prosthesis.

[0127] 4. Prosthesis implantation: Based on the preoperative planned position and angle, the prosthesis is precisely implanted into the femoral head and acetabulum.

[0128] 2.3 Pressing and Positioning:

[0129] Objective: To ensure a firm fit between the prosthesis and the patient's bone and to achieve precise intraoperative positioning.

[0130] step:

[0131] 1. Press fitting procedure: Using specialized tools, the prosthesis is pressed into place with the patient's femoral head or acetabulum to ensure the stability of the prosthesis.

[0132] 2. Intraoperative positioning confirmation: The actual position of the prosthesis is confirmed to be consistent with the planned position by intraoperative imaging equipment (such as X-ray or intraoperative CT) to ensure good postoperative prosthesis function.

[0133] 2.4 Intraoperative navigation and real-time display:

[0134] Objective: To ensure the precision of each step of the surgery by guiding the procedure through a real-time navigation system and display.

[0135] step:

[0136] 1. Real-time navigation system activated: The navigation system tracks the position of surgical tools and prostheses in real time through the registered 3D model, providing dynamic guidance for the surgery.

[0137] 2. Real-time intraoperative display: The monitor displays information such as the 3D model, surgical instruments, and prosthesis position in real time to help surgeons perform precise operations.

[0138] 3. Dynamic adjustment: If deviations occur during the operation, the surgical path and tool positions are adjusted in real time to ensure the operation is performed accurately.

[0139] 3. Postoperative assessment and follow-up:

[0140] 3.1 Postoperative imaging assessment

[0141] Objective: To evaluate the surgical outcome through imaging.

[0142] step:

[0143] 1. Postoperative CT or X-ray imaging examination: Postoperative imaging examination is performed on the patient to confirm whether the implantation position and angle of the prosthesis are consistent with the preoperative plan.

[0144] 2. Image comparison: Postoperative images are compared and analyzed with preoperative 3D models to assess the success rate of the surgery and the accuracy of the prosthesis.

[0145] 3.2 Postoperative follow-up:

[0146] Objective: To track the patient's postoperative recovery and ensure the long-term stability of the prosthesis.

[0147] step:

[0148] 1. Regular imaging examinations: Regularly perform imaging examinations on patients to track the stability of the prosthesis and the functional recovery of the hip joint.

[0149] 2. Functional assessment: Through physical therapy and rehabilitation training, help patients restore hip joint function, and regularly assess the patient's mobility and the effectiveness of the prosthesis.

[0150] The specific process of the total hip replacement module covers everything from preoperative CT image segmentation and 3D reconstruction, preoperative surgical planning, to intraoperative point cloud registration, navigation guidance, refining, prosthesis implantation and real-time display, and then to postoperative image evaluation and functional tracking, ensuring high precision in the surgical process and good postoperative rehabilitation for patients.

[0151] The formula for generating the 3D model of the hip joint and for intraoperative registration is as follows:

[0152] M hip The generated 3D model of the hip joint is used for intraoperative navigation.

[0153] I CT (xyzt): The three-dimensional voxel value of the CT image as a function of time t, defined in xyz space coordinates.

[0154] The Laplacian operator for CT images, used to enhance edge information in the images.

[0155] α: Adjustment coefficient, used to control the smoothness of the image.

[0156] Ω CT The three-dimensional volume of the CT image, with the integral region representing the three-dimensional structure of the hip joint.

[0157] λ i Weighting coefficients control the impact of intraoperative point cloud registration.

[0158] The registration coordinates of the i-th preprocessed point cloud change over time.

[0159] The coordinates for the i-th postoperative point cloud registration.

[0160] Rigid body transformation matrix R ij The registration matrix corresponding to (t) is used to align the postoperative coordinates with the preoperative coordinates.

[0161] Figure 2 is a schematic diagram of an end effector for total hip replacement provided in one embodiment of this application. The end effector is fixed to the end of a robotic arm and is used to hold a grinding rod and a press rod.

[0162] Figure 3 is a schematic diagram of a medical bone drill for total hip replacement provided in one embodiment of this application. The medical bone drill is a power source for grinding the acetabulum.

[0163] In one embodiment, the total knee replacement module is used for:

[0164] Preoperative segmentation and three-dimensional reconstruction of knee joint CT images were performed to obtain a three-dimensional model of the knee joint.

[0165] Preoperative planning based on a 3D model of the knee joint;

[0166] Intraoperative point cloud registration, osteotomy, and gap balancing are displayed in real time.

[0167] The specific steps and procedures for the "total knee replacement module" cover preoperative CT image segmentation and 3D reconstruction, preoperative planning, and intraoperative point cloud registration, osteotomy, gap balancing, and real-time display. The specific steps are as follows:

[0168] 1. Preoperative preparation:

[0169] 1.1 Acquisition of CT images of the knee joint:

[0170] Objective: To obtain CT scan data of the patient's knee joint for subsequent model building and surgical planning.

[0171] step:

[0172] 1. CT scan: A high-resolution CT scan of the patient's knee joint is performed to obtain multi-slice images, ensuring the clarity of key parts of the knee joint.

[0173] 2. Image data import: Import the acquired knee CT images into the processing system of the total knee replacement module.

[0174] 1.2 Knee joint CT image segmentation:

[0175] Objective: To extract the skeletal and soft tissue structures of the knee joint from CT images using a segmentation algorithm.

[0176] step:

[0177] 1. Image preprocessing: Denoising, image enhancement, and filtering are performed on CT images to enhance the contrast of joint edges and improve segmentation accuracy.

[0178] 2. Application of segmentation algorithms: Deep learning algorithms (such as convolutional neural networks CNN) or traditional image processing methods (such as region growing, thresholding, etc.) are used to segment the bones (femur, tibia, patella) and surrounding soft tissues of the knee joint.

[0179] 3. Manual correction: The segmentation results are manually corrected according to the needs of clinicians to ensure that the segmentation accuracy meets the requirements of the surgery.

[0180] 1.3 Three-dimensional reconstruction:

[0181] Objective: To reconstruct a three-dimensional model of the knee joint based on segmented CT images for subsequent preoperative planning.

[0182] step:

[0183] 1. Application of 3D Reconstruction Technology: Using volume rendering or surface reconstruction techniques, the segmented knee joint structure is reconstructed into a 3D model. The 3D model should accurately represent the bone structures such as the femur, tibia, and patella.

[0184] 2. Model smoothing: The reconstructed model is smoothed and noise is eliminated to ensure the smoothness and realism of the model surface.

[0185] 3. Mark key structures: Mark the important anatomical structures of the knee joint and mark important bony landmarks for subsequent surgical planning and navigation.

[0186] 1.4 Preoperative planning:

[0187] Objective: To conduct preoperative planning for total knee arthroplasty based on a reconstructed 3D model of the knee joint, ensuring precise surgical execution.

[0188] step:

[0189] 1. Prosthesis selection: Based on the three-dimensional model and anatomical structure of the knee joint, select the appropriate knee joint prosthesis model and specifications, taking into account the size, type and material of the prosthesis.

[0190] 2. Osteotomy Planning: Design the osteotomy plan on the 3D model of the knee joint, determine the cut surface, angle and depth of the osteotomy, and ensure perfect fit between the prosthesis and the bone after surgery.

[0191] 3. Gap balance design: Plan the gap balance between the femur and tibia to ensure the knee joint is balanced during extension and flexion after surgery, and avoid joint laxity or tension.

[0192] 4. Surgical Path and Tool Planning: Design the specific surgical path, plan the tools to be used during the operation, and determine the sequence of operations at key locations.

[0193] 2. Intraoperative procedures:

[0194] 2.1 Point Cloud Registration

[0195] Objective: To accurately register the preoperatively planned 3D model with the intraoperative point cloud data to achieve real-time navigation.

[0196] step:

[0197] 1. Point cloud data acquisition: During the operation, real-time point cloud data of the knee joint is acquired using 3D scanning equipment (such as optical scanners and laser scanners).

[0198] 2. Registration Algorithm: The point cloud acquired during the operation is aligned with the 3D model planned before the operation by rigid body registration or non-rigid body registration algorithm to ensure that the model is accurately matched with the actual anatomical structure.

[0199] 3. Error correction: Using registration algorithms such as Iterative Closest Point (ICP), the alignment error between point cloud data and model is corrected to ensure the accuracy of intraoperative operations.

[0200] 2.2 Osteotomy:

[0201] Objective: To perform precise osteotomy of the femur and tibia according to the preoperative plan in order to install a knee prosthesis.

[0202] step:

[0203] 1. Osteotomy navigation: The surgical instruments are guided by an intraoperative navigation system, so that the osteotomy tools are accurately positioned on the planned cutting surface.

[0204] 2. Femoral osteotomy: The distal femur is osteotomized according to the preoperatively designed angle and position. Ensure that the installation surface of the femoral prosthesis is smooth and flat.

[0205] 3. Tibial osteotomy: The proximal end of the tibia is cut according to the plan to ensure that the fitting surface of the tibial prosthesis is precisely aligned with the prosthesis.

[0206] 4. Osteotomy verification: During the operation, the accuracy of the osteotomy is checked by navigation or X-ray imaging to ensure that the cut surface and angle are consistent with the preoperative plan.

[0207] 2.3 Clearance Balance:

[0208] Objective: To ensure the balance of knee joint gaps at different flexion angles after osteotomy, so that the prosthesis can achieve a stable range of motion after implantation.

[0209] step:

[0210] 1. Gap test: After osteotomy, the anterior-posterior and lateral gaps between the femur and tibia are assessed using a gap test tool to ensure balance in flexion and extension.

[0211] 2. Adjustment and optimization: Based on the results of the gap test, adjust the installation angle of the prosthesis or further modify the osteotomy surface to ensure that the gap remains balanced during flexion and extension, and avoid joint laxity or excessive tightness.

[0212] 3. Real-time balance adjustment: The intraoperative navigation system displays the interarticular balance of the knee joint in real time, guiding doctors to further optimize the balance.

[0213] 2.4 Intraoperative real-time display and navigation:

[0214] Objective: To ensure precise operation at every step of the surgical procedure through a real-time navigation and display system.

[0215] step:

[0216] 1. Navigation system activation: The intraoperative navigation system displays the positions of surgical instruments, patient bones, and prostheses on a 3D model in real time, assisting doctors in accurately executing each step of the operation.

[0217] 2. Real-time display: The status of osteotomy, prosthesis implantation and gap balance is updated in real time on the intraoperative display screen to help doctors make immediate adjustments.

[0218] 3. Error correction and feedback: With the help of a real-time navigation and display system, doctors can make fine adjustments to the surgical path, tool operation, etc. based on feedback during the operation to ensure that the final result of the operation is consistent with the preoperative plan.

[0219] 3. Postoperative assessment and follow-up:

[0220] 3.1 Postoperative imaging examinations:

[0221] Objective: To confirm the accuracy of prosthesis placement and the recovery of knee joint function through postoperative imaging.

[0222] step:

[0223] 1. Imaging examination: Using imaging techniques such as X-ray, CT or MRI, assess whether the postoperative implantation position and angle are consistent with the preoperative plan.

[0224] 2. Image comparison: Compare postoperative images with the preoperative 3D model to confirm the position of the prosthesis and the recovery of the knee joint.

[0225] 3.2 Postoperative follow-up and rehabilitation:

[0226] Objective: To track the recovery of knee joint function after surgery and to monitor the long-term use of the prosthesis.

[0227] step:

[0228] 1. Regular check-ups: Monitor the use of the knee prosthesis and the patient's recovery progress through regular imaging examinations and functional assessments.

[0229] 2. Functional rehabilitation assessment: Through exercise assessment and rehabilitation training, we help patients restore knee joint function and ensure that joint movement is flexible and stable.

[0230] The total knee replacement module's process ranges from preoperative knee CT image segmentation and 3D reconstruction, preoperative planning, to intraoperative point cloud registration, osteotomy, gap balancing, real-time display and navigation, and finally to postoperative image evaluation and rehabilitation tracking, ensuring precise surgical execution and maximizing the patient's postoperative functional recovery.

[0231] The formula for the three-dimensional model reconstruction and osteotomy path of the knee joint is as follows:

[0232] M knee A 3D model of the knee joint is generated for use in surgery.

[0233] Segmentation function based on CT image gradient Extract the three-dimensional structure of the knee joint.

[0234] Ω knee : The knee joint region in a CT image.

[0235] μ i (t): The time variation coefficient controlling the osteotomy path of the knee joint.

[0236] Coordinates of the osteotomy path in the knee joint.

[0237] λ: Regularization coefficient, which controls the smoothness of the osteotomy path.

[0238] The Laplacian operator for the osteotomy path is used to adjust the smoothness of the cut surface.

[0239] In one embodiment, the unicompartmental joint replacement module is used for:

[0240] Preoperative segmentation and three-dimensional reconstruction of knee joint CT images were performed to obtain a three-dimensional model of the knee joint.

[0241] Preoperative planning based on a 3D model of the knee joint;

[0242] Intraoperative point cloud registration, osteotomy, and gap balancing are displayed in real time.

[0243] Specifically, the optimized formulas for preoperative segmentation and intraoperative cutting in unicompartmental arthroplasty are as follows:

[0244] M unicompartment Three-dimensional model and osteotomy results of the unicompartmental joint.

[0245] P unicompartment (x,y,z): The structure of the unicompartmental joint in the (x,y,z) space.

[0246] Gradient information from a unicompartmental joint is used for structure segmentation and extraction.

[0247] β i : Controls the weighting coefficient of the cutting path.

[0248] f i (θ(t),φ(t)): represents the cutting angles θ(t) and φ(t).

[0249] Γ cutIntraoperative cutting path.

[0250] γ: Smoothing coefficient, used to adjust the smoothness of the cutting path.

[0251] The gradient of the cutting angle represents the rate of change of the cutting angle over time.

[0252] Figure 4 is a schematic diagram of the structure of an end connector for total knee or unicompartmental joint replacement provided in one embodiment of this application. The end connector is fixed to the end of a robotic arm for connecting a medical oscillating saw.

[0253] Figure 5 is a schematic diagram of a medical oscillating saw for total knee or unicompartmental joint replacement provided in one embodiment of this application. The medical oscillating saw is used to perform osteotomy on the femur and tibia.

[0254] In one embodiment, the periacetabular osteotomy module is used for: preoperative planning, intraoperative registration, osteotomy, positioning, navigation, and real-time display.

[0255] In one embodiment, preoperative planning, intraoperative registration, osteotomy, localization, navigation, and real-time display include:

[0256] Obtain images of the patient's hip joint;

[0257] The hip joint image is segmented and reconstructed in three dimensions to obtain a three-dimensional model of the hip joint;

[0258] Using a 3D model of the hip joint, the osteotomy surfaces, guide lines, and safe zones around the acetabulum are planned preoperatively, the boundaries of the safe zones are determined, and the areas are rendered and colored. The preoperative planning results include osteotomy planning based on the anterior ischium, superior pubic ramus, superior iliac bone, and posterior column.

[0259] During the operation, the NDI reflective positioning and navigation system of the infrared camera and the positioning frame on the curved blade of the bone scalpel are used to obtain the position of the end of the bone scalpel in real time when performing osteotomy according to the preoperative plan.

[0260] The specific steps and procedures for the "periacetabular osteotomy module" are explained below:

[0261] 1. Preoperative preparation:

[0262] 1.1 Obtain images of the patient's hip joint:

[0263] Objective: To ensure the acquisition of clear and detailed joint images to guide subsequent treatment.

[0264] step:

[0265] 1. Image acquisition: Obtain imaging data of the patient's hip joint through CT or MRI scans, focusing on the joint structure and surrounding soft tissues.

[0266] 2. Image Import: Import the acquired image data into the processing system for subsequent analysis.

[0267] 1.2 Image Segmentation and 3D Reconstruction:

[0268] Objective: To extract the three-dimensional structure of the hip joint to provide a basis for surgical planning.

[0269] step:

[0270] 1. Image preprocessing: Improve image quality and enhance the visibility of joint edges through denoising and enhancement techniques.

[0271] 2. Segmentation Algorithm Application: Using deep learning (such as CNN) or traditional image processing methods, the hip joint is segmented to extract key structures such as the acetabulum and femur.

[0272] 3. Three-dimensional reconstruction: Using the segmented data, a three-dimensional model of the hip joint is created to ensure that the model accurately reflects the patient's anatomical features.

[0273] 1.3 Preoperative planning:

[0274] Objective: To develop a precise osteotomy plan to ensure surgical success.

[0275] step:

[0276] 1. Osteotomy surface planning: Design the osteotomy surfaces around the acetabulum based on the three-dimensional model, and plan them according to anatomical structures such as the anterior part of the ischium, the superior pubic ramus, the superior ilium, and the posterior column.

[0277] 2. Determining the guiding line and safe zone: Draw the surgical guiding line, define the safe zone for osteotomy and mark its boundaries to avoid damage to important structures during surgery.

[0278] 3. Result rendering: The planning results are visualized using rendering technology, and the osteotomy surface and safety zone are highlighted by coloring to facilitate the doctor's preoperative assessment.

[0279] 2. Intraoperative procedures:

[0280] 2.1 Intraoperative registration

[0281] Objective: To ensure accurate alignment of the intraoperative navigation system with the patient's anatomical structures.

[0282] step:

[0283] 1. Infrared camera setup: Install the NDI reflective positioning and navigation system to ensure that it can capture the positioning data of the surgical area in real time.

[0284] 2. Initial positioning: Preliminary registration is performed based on the preoperative planning model and the actual anatomical structure to ensure the accuracy of the navigation system.

[0285] 2.2 Osteotomy:

[0286] Objective: To accurately perform osteotomy according to the plan.

[0287] step:

[0288] 1. Bone scalpel positioning: The curved blade of the bone scalpel is fixed in the predetermined osteotomy position using a positioning frame to ensure it conforms to the preoperative plan.

[0289] 2. Real-time monitoring: During the osteotomy process, the navigation system acquires the position of the end of the bone cutter in real time and monitors its deviation from the planned result.

[0290] 3. Perform osteotomy: Perform osteotomy according to the preoperative plan to ensure the precision of the cutting angle and depth.

[0291] 2.3 Positioning and Navigation:

[0292] Objective: To achieve real-time feedback and ensure surgical precision.

[0293] step:

[0294] 1. Real-time feedback: During the osteotomy, the system displays a comparison between the position of the bone cutter and the preoperative plan to ensure surgical accuracy.

[0295] 2. Adjustment and optimization: Based on real-time data, make necessary adjustments to the position of the bone cutter to ensure that the final surgical result meets expectations.

[0296] 3. Postoperative assessment:

[0297] 3.1 Postoperative examination

[0298] Objective: To verify the effectiveness of osteotomy and the recovery of joint function.

[0299] step:

[0300] 1. Imaging examination: Use imaging techniques such as X-ray or CT to assess the accuracy of osteotomy and the recovery of bone structure.

[0301] 2. Effect assessment: Confirm the postoperative functional status of the hip joint, including range of motion and stability, and evaluate the surgical outcome.

[0302] The entire periacetabular osteotomy module workflow, from preoperative image acquisition, segmentation, and 3D reconstruction, to precise preoperative planning, real-time intraoperative registration, osteotomy, and navigation, and postoperative examination and evaluation, ensures high precision in surgical procedures and good patient recovery. This module, by combining modern imaging technology and navigation systems, enhances the safety and effectiveness of surgery, and advances the development of joint replacement surgery.

[0303] Specifically, the optimization formula for the osteotomy surface and positioning around the acetabulum is as follows:

[0304] S cut : The osteotomy surface of the acetabulum.

[0305] P acetadular (x,y,z): The three-dimensional structure around the acetabulum.

[0306] P safe (x,y,z): The three-dimensional coordinates of the safe area.

[0307] κ(θ,ψ): Curvature function along the osteotomy path, depending on angles θ and ψ.

[0308] Ω acetabular The three-dimensional spatial region of the acetabulum.

[0309] λ i Weighting coefficients are used to adjust the positioning accuracy of the infrared camera.

[0310] The real-time position information of the bone knife tip changes with time t.

[0311] In one embodiment, the sports medicine module is used for:

[0312] Acquire the first CT image of the knee joint;

[0313] CT image segmentation based on deep learning neural networks;

[0314] 3D modeling of the knee joint is performed based on the segmentation results;

[0315] Establish preoperative guiding points and plan preoperative registration points in the knee joint;

[0316] Bone tunnel planning and ligament reconstruction;

[0317] During the operation, a probe is used to collect intraoperative registration points based on the preoperative guidance points;

[0318] The probe is used to complete the rigid registration of preoperative and intraoperative registration points based on digital twin technology; the rigid registration includes two parts: coarse registration and fine registration.

[0319] Based on the registration results, the coordinate system of the robotic arm, optical positioning tracker, and knee joint is established;

[0320] Controlling the robotic arm to move autonomously to reconstruct bone tunnels.

[0321] In one embodiment, rigid registration of preoperative and intraoperative registration points based on digital twin technology is achieved using probes, including:

[0322] First, initial registration is performed using intraoperative and preoperative registration points. At the same time, the position of the probe is located using digital twin technology, and the three-dimensional skeleton is mapped onto the color image. The position of the three-dimensional skeleton is monitored in real time using the position of the probe tip, and the initial registration matrix is ​​corrected.

[0323] The registration matrix is ​​automatically adjusted again based on the corrected initial registration matrix to obtain the final rigid registration matrix;

[0324] The digital twin approach uses a probe tracking algorithm based on multimodal and multiscale fusion to track the probe in real time, and a probe tip positioning algorithm based on multimodal and multiscale fusion to locate the position of the probe tip in real time.

[0325] The detailed steps and procedures for the "Sports Medicine Module" are as follows:

[0326] 1. Preoperative preparation:

[0327] 1.1 Obtaining CT images of the first knee joint:

[0328] step:

[0329] 1. Patient preparation: Ensure the patient is comfortable and explain the scanning process.

[0330] 2. CT scan: The knee joint is imaged using a high-resolution CT scanner to ensure that the scan covers all important structures of the joint and obtains high-quality images.

[0331] 3. Data saving: The scan results are saved in DICOM format for easy subsequent processing.

[0332] 1.2 Deep Learning-Based Neural Network CT Image Segmentation:

[0333] step:

[0334] 1. Data preprocessing: Preprocess the CT images, including noise reduction and contrast enhancement, to improve the segmentation effect.

[0335] 2. Model training: Select a suitable deep learning model (such as U-Net), train it using labeled knee joint images, and optimize the model parameters.

[0336] 3. Image segmentation: The trained model is used to segment CT images, automatically extracting structures such as the knee joint, bones, and soft tissues, and generating binarized segmentation results.

[0337] 1.3 Perform 3D modeling of the knee joint based on the segmentation results:

[0338] step:

[0339] 1. Generate a 3D model: Convert the segmented binary image data into a 3D mesh model using 3D reconstruction software (such as MeshLab).

[0340] 2. Model Optimization: The 3D model is smoothed to remove noise and unnecessary details, ensuring the accuracy of the model and the visualization effect.

[0341] 3. Model Validation: By comparing with the professional knowledge of doctors, the model is verified to accurately reflect the anatomical structure of the knee joint.

[0342] 2. Preoperative planning:

[0343] 2.1 Setting up preoperative guidance points and planning preoperative registration points

[0344] step:

[0345] 1. Guiding point identification: Select key anatomical landmarks on the 3D model and set preoperative guiding points, such as the attachment points of the anterior cruciate ligament and posterior cruciate ligament of the knee joint.

[0346] 2. Registration point selection: Based on the bone tunnel planning, select multiple registration points and record their coordinates on the three-dimensional model as the reference for intraoperative registration.

[0347] 2.2 Bone tunnel planning and ligament reconstruction:

[0348] step:

[0349] 1. Tunnel planning and design: Use professional software (such as Mimics) to design the path of the bone tunnel to ensure its safety with the surrounding structure and compliance with biomechanical requirements.

[0350] 2. Ligament reconstruction strategy: Determine the reconstruction plan, define the type and location of the ligament to be reconstructed, and formulate the surgical procedure.

[0351] 3. Intraoperative procedures:

[0352] 3.1 Using a probe to acquire intraoperative registration points

[0353] step:

[0354] 1. Probe preparation: Select a suitable probe for the surgery and ensure that it can be accurately positioned.

[0355] 2. Intraoperative localization: During the operation, a probe is used to locate the patient against the pre-set guide point and registration point, and the probe position is recorded.

[0356] 3.2 Rigid registration based on digital twin technology:

[0357] step:

[0358] 1. Initial registration: The registration points obtained during the operation are initially registered with the points set before the operation to form an initial registration matrix.

[0359] 2. Digital Twin Positioning: Digital twin technology is used to position the probe in real time, ensuring that the probe's position is accurately mapped in three-dimensional space.

[0360] 3. 3D Mapping and Monitoring: The 3D skeletal model is mapped onto a real-time color image, and the 3D model is monitored using the position of the probe tip to correct the initial registration matrix.

[0361] 3.3 Automatic adjustment of the registration matrix:

[0362] step:

[0363] 1. Matrix Correction: Based on real-time data feedback, the initial registration matrix is ​​corrected to ensure its accuracy.

[0364] 2. Final registration: The registration matrix is ​​further adjusted using an automatic algorithm to generate the final rigid registration matrix.

[0365] 4. Establishing the coordinate system:

[0366] 4.1 Establish the coordinate system of the robotic arm, optical positioning tracker, and knee joint.

[0367] step:

[0368] 1. Coordinate system calibration: Based on the final rigid registration matrix, calibrate the coordinate systems of the robotic arm and the optical positioning system.

[0369] 2. Coordinate system integration: Ensure that the coordinate systems of the robotic arm, optical system and knee joint are consistent to achieve seamless collaboration.

[0370] 5. Bone tunnel reconstruction:

[0371] 5.1 Controlling the autonomous movement of the robotic arm for bone tunnel reconstruction

[0372] step:

[0373] 1. Robotic arm control program: Based on the registration results, write the control program for the robotic arm so that it can accurately perform the bone tunnel reconstruction operation.

[0374] 2. Reconstruction Operation Execution: The robotic arm is activated to reconstruct the tunnel according to the preset tunnel planning path, and the parameters and status during the process are monitored in real time to ensure the accuracy of the operation.

[0375] 6. Applications of digital twin technology:

[0376] 6.1 Probe Tracking and Positioning:

[0377] step:

[0378] 1. Real-time probe tracking: A multi-modal, multi-scale fusion probe tracking algorithm is used to continuously track the position of the probe in three-dimensional space.

[0379] 2. Application of tip positioning algorithm: The tip positioning algorithm based on multimodal and multiscale fusion is used to obtain the specific position of the probe tip in real time, ensuring the accuracy and stability of its positioning.

[0380] This module details the entire process of bone tunnel reconstruction, from preoperative CT image acquisition, deep learning segmentation, and 3D modeling, to the setting of preoperative guide points and registration points, the use of intraoperative probes, the application of digital twin technology, and the autonomous movement of the robotic arm. By combining advanced image processing, artificial intelligence, real-time monitoring, and mechanical control technologies, this module significantly improves the precision and safety of knee surgery, contributing to better postoperative recovery for patients.

[0381] Specifically, the optimized formula for bone tunnel reconstruction and registration is as follows:

[0382] T bone (x,y,z,t): A three-dimensional model of bone tunnel reconstruction, which changes with time t and is used for intraoperative navigation.

[0383] Ω bone : The three-dimensional region of the bone tunnel.

[0384] The expected coordinates of the preoperative bone tunnel are defined in the x′, y′, z′ space and change with time t.

[0385] G(xx′,yy′,zz′): Gaussian kernel function, used to smooth changes in bone tunnel coordinates and adjust the smoothness between different locations.

[0386] α: Regularization coefficient, which controls the influence of registration points before and after surgery.

[0387] The coordinates of the bone tunnel after surgery change with time t.

[0388] In one embodiment, the spinal screw placement positioning and navigation module is used for:

[0389] Record the patient's basic information during the operation;

[0390] Take CT images of the patient's spine;

[0391] 3D registration based on a 3D calibrator;

[0392] Intraoperative planning and real-time positioning and navigation.

[0393] The specific steps and procedures for the "spinal screw placement positioning and navigation module" are further detailed and complicated as follows:

[0394] 1. Preoperative preparation:

[0395] 1.1 Record the patient's basic information during the operation:

[0396] step:

[0397] 1. Patient information collection:

[0398] Use an electronic health record system to enter the patient's basic information, including name, age, gender, medical record number, etc.

[0399] Record the patient's past medical history, allergic reactions, and current illnesses so that the surgical team can fully understand the patient's condition.

[0400] 2. Preoperative discussion:

[0401] A team meeting is held before the operation to discuss the patient's specific situation and decide on the best surgical plan.

[0402] Confirm the surgical team members, anesthesia plan, and postoperative care arrangements.

[0403] 1.2 Take CT images of the patient's spine:

[0404] step:

[0405] 1. Patient localization:

[0406] Place the patient on the CT scanner, ensuring that the midline of the spine is aligned with the center of the scan to avoid image distortion caused by postural deviations.

[0407] Use positioning tools (such as laser locators) to ensure the accuracy of patient positioning.

[0408] 2. CT scan execution:

[0409] Choose appropriate scanning parameters (such as slice thickness and reconstruction algorithm) to ensure high-quality spinal images.

[0410] Perform multi-phase scans (such as transverse, sagittal, and coronal planes) to obtain more comprehensive anatomical information.

[0411] 3. Data processing and storage:

[0412] After scanning, the images are post-processed using professional image processing software, including noise reduction, contrast enhancement, and reconstruction.

[0413] The results are saved in DICOM format to a secure storage server to ensure that the images can be accessed at any time during the surgery.

[0414] 2. Intraoperative procedures:

[0415] 2.1 3D registration based on a 3D calibrator:

[0416] step:

[0417] 1. Installation of the 3D calibrator:

[0418] The 3D calibrator is precisely installed on the patient's spine, ensuring it is aligned with anatomical features such as vertebral body edges.

[0419] Use a special clamp to secure the calibrator and prevent it from moving during the procedure.

[0420] 2. Image registration process:

[0421] The real-time position information of the calibrator is obtained through an optical or laser tracking system and registered with CT images.

[0422] By applying registration algorithms (such as rigid registration and non-rigid registration) and iteratively optimizing the calculations, CT images are aligned with the patient's actual anatomical structures.

[0423] 3. Registration Verification:

[0424] Registration accuracy is evaluated by comparing the position of the calibrator in the CT image with its actual position using statistical analysis methods (such as root mean square error).

[0425] If there is a deviation, adjust the position of the calibrator and repeat the registration process until the accuracy requirements are met.

[0426] 2.2 Intraoperative planning and real-time positioning and navigation:

[0427] step:

[0428] 1. Navigation route planning:

[0429] Based on the registered CT images, specialized software (such as navigation system integration software) is used to design the pin placement path to ensure that important nerve and blood vessel structures are avoided.

[0430] Determine the angle, depth, and relative position of the screws, develop a detailed surgical plan, and generate a surgical guidance diagram.

[0431] 2. Real-time monitoring and feedback:

[0432] The navigation system is activated to monitor the position of the probe or nail using optical or electromagnetic locators and update the position information in real time on the display screen.

[0433] By applying data fusion technology, real-time location information is overlaid with CT images to provide visual navigation support.

[0434] 3. Navigation Operation:

[0435] Based on real-time feedback provided by the navigation system, the doctor adjusts the direction and position of the probe or nail to ensure accurate insertion into the intended location.

[0436] Throughout the procedure, maintain communication with the surgical team, address any potential issues promptly, and ensure the surgery proceeds smoothly.

[0437] 3. Postoperative steps:

[0438] 3.1 Data Recording and Analysis

[0439] step:

[0440] 1. Postoperative data processing:

[0441] Record key data during the surgical procedure, including parameters such as position, angle, and depth provided by the navigation system.

[0442] Collect real-time intraoperative images and final images for comparative analysis.

[0443] 2. Result Evaluation:

[0444] Postoperatively, the navigation path is compared with the actual screw placement location to assess the accuracy and success rate of screw placement.

[0445] Based on the postoperative evaluation results, suggestions for improvement will be provided for future surgeries.

[0446] This module details the entire process, from preoperative patient information entry and CT image acquisition to the installation and registration of the 3D calibrator, and finally to real-time navigation planning and monitoring. By introducing sophisticated technologies and data analysis methods, it ensures the accuracy and safety of spinal screw placement surgery, ultimately improving patient treatment outcomes and recovery efficiency.

[0447] Specifically, the three-dimensional registration and positioning formulas for spinal screw placement are as follows:

[0448] E spine (x,y,z,t): Registration error function in spinal screw placement, which varies with time t.

[0449] Ω spine The three-dimensional spatial region of the spine.

[0450] Preoperative spinal registration point coordinates change over time.

[0451] Postoperative spinal registration point coordinates.

[0452] R spine (x,y,z): A rigid rotation matrix used to rotate the postoperative coordinates to the preoperative position.

[0453] The gradient of the spine is used to calculate the rate of change of coordinates.

[0454] λ: Regularization parameter used to control the smoothness of positioning.

[0455] Information on the position of surgical instruments during the operation.

[0456] In one embodiment, the trauma localization and navigation module is used for:

[0457] Record the patient's basic information during the operation;

[0458] Take X-ray images of the patient's wound site;

[0459] Two-dimensional registration is performed based on a two-dimensional calibrator.

[0460] Intraoperative planning and real-time positioning and navigation.

[0461] The specific steps and procedures for the "trauma localization and navigation module" are further detailed and complicated as follows:

[0462] 1. Preoperative preparation:

[0463] 1.1 Record the patient's basic information during the operation:

[0464] step:

[0465] 1. Input of electronic information system:

[0466] In the operating room, an electronic health record system is used to accurately enter the patient's basic information (name, gender, age, medical record number, etc.).

[0467] Record the patient's medical history (including allergies, chronic diseases, etc.) to ensure that the surgical team has a comprehensive understanding of the patient's health condition.

[0468] 2. Preoperative case discussion:

[0469] A multidisciplinary team meeting was held to review the patient's medical records and imaging results, and to discuss the surgical risks and expected outcomes.

[0470] Determine the division of labor within the surgical team and the postoperative rehabilitation plan, ensuring that each team member understands their responsibilities.

[0471] 1.2 Take X-ray images of the patient's wound site:

[0472] step:

[0473] 1. Patient localization:

[0474] The patient is placed under the X-ray machine, and their posture is adjusted to ensure optimal visualization of the trauma area.

[0475] Use an airbag support or other fixation device to stabilize the wound site and ensure it does not move during the imaging process.

[0476] 2. Multi-angle X-ray imaging:

[0477] Multiple X-ray views (such as frontal, lateral, and oblique views) should be selected to ensure comprehensive trauma information is obtained.

[0478] Set appropriate exposure parameters (such as kilovolt characteristics and milliampere numbers) to improve image quality and reduce radiation dose.

[0479] 3. Image post-processing:

[0480] Post-processing of X-ray images using image processing software includes noise reduction, contrast enhancement, and pseudo-color processing to improve the visibility of anatomical structures.

[0481] The processed images are stored in DICOM format and linked to the patient's file to ensure they can be retrieved at any time during the procedure.

[0482] 2. Intraoperative procedures:

[0483] 2.1 Two-dimensional registration based on a two-dimensional calibrator

[0484] step:

[0485] 1. Calibrator Installation and Calibration:

[0486] Accurately fix the two-dimensional calibrator at the patient's wound site, ensuring that the key points of the calibrator are consistent with the surrounding anatomical structures (such as bones and soft tissues).

[0487] A 3D laser scanner was used to perform a preliminary scan of the calibrator and its surrounding structure to ensure that its position was stable and accurately recorded.

[0488] 2. Two-dimensional registration process:

[0489] The intraoperative X-ray images were registered with the calibrator, and feature matching and position correction were performed using professional image processing software.

[0490] Image fusion technology is used to combine preoperative CT images with intraoperative X-ray images, and the position of the calibrator is precisely aligned with the wound site through algorithm optimization.

[0491] 3. Registration accuracy verification:

[0492] The registration results are compared with the actual position of the calibrator in the X-ray image, and the accuracy of the registration is evaluated by statistical analysis (such as standard deviation and root mean square error).

[0493] If a deviation is found, readjust the calibrator position and re-register until the registration result meets clinical requirements.

[0494] 2.2 Intraoperative planning and real-time positioning and navigation:

[0495] step:

[0496] 1. Navigation route and destination planning:

[0497] Based on the registration, the navigation system software is used to determine the surgical path to the wound site and identify key points (such as the location of bone screws, incision sites, etc.).

[0498] Based on anatomical features, trauma type, and expected outcome, a detailed surgical guide map is generated, including the coordinates, angles, and depths of each key manipulation point.

[0499] 2. Real-time monitoring and feedback system activated:

[0500] The navigation system is activated to continuously track the real-time position of surgical instruments (such as probes and bone screws) using optical or electromagnetic locators.

[0501] The system dynamically compares real-time location information with the planned path, providing real-time feedback to ensure that doctors can adjust the direction and intensity of their operations in a timely manner.

[0502] 3. Precise operation execution:

[0503] Based on real-time data provided by the navigation system, the doctor adjusts the position of the probe or bone screw to ensure accurate insertion into the intended location.

[0504] During the procedure, the movement trajectory of the probe or bone screw is continuously monitored, and intelligent algorithms are used to identify potential risks and issue timely warnings.

[0505] 3. Postoperative steps:

[0506] 3.1 Data Recording and Analysis:

[0507] step:

[0508] 1. Surgical data organization and archiving:

[0509] Record key data during the surgical procedure, including parameters such as position, angle, and depth provided by the navigation system.

[0510] Integrate surgical images, navigation data, registration results, and surgical records into the patient's electronic health record.

[0511] 2. Postoperative outcome analysis and evaluation:

[0512] By comparing the navigation path during surgery with the actual trauma management results, the accuracy of screw placement, surgical success rate, and possible complications can be evaluated.

[0513] Based on the assessment results, postoperative discussions are conducted to provide improvement suggestions for similar cases in the future, and to enhance team collaboration and learning.

[0514] This module details the entire process from preoperative patient information entry and X-ray image acquisition to the installation and registration of the two-dimensional calibrator, and finally to real-time navigation planning and monitoring. By introducing sophisticated technologies and data analysis methods, it ensures the precision and safety of trauma surgery, improves patient treatment outcomes and recovery efficiency, and promotes the development of medical technology.

[0515] Specifically, the formula for two-dimensional registration and navigation of the wound site is as follows:

[0516] The two-dimensional registration results of the trauma site change over time.

[0517] Ω trauma : A two-dimensional image region of the wound site.

[0518] The coordinates of the registration point at the initial trauma site change over time.

[0519] G(xx′,yy′): Gaussian kernel function, used to smooth the relationship between trauma registration points.

[0520] Gradient information of a two-dimensional image.

[0521] λ j : Regularization coefficient used to control the smoothness of wound site localization.

[0522] The location information of the intraoperative trauma localization tool changes over time.

[0523] Figure 6 is a schematic diagram of the structure of an active light-emitting end provided in an embodiment of this application for positioning and navigation of periacetabular osteotomy, sports medicine, spinal screw placement, and trauma positioning and navigation. The active light-emitting end is fixed to the end of a robotic arm for positioning.

[0524] Figure 7 is a schematic diagram of the structure of a guide provided in one embodiment of this application for periacetabular osteotomy, sports medicine, spinal screw placement positioning navigation, and trauma positioning navigation. The guide is used for trauma functional sleeve positioning.

[0525] Figure 8 is a schematic diagram of the system structure of an artificial intelligence surgical robot for multiple orthopedic diseases provided in one embodiment of this application. From left to right in Figure 8, the components are the navigation carriage, the robotic arm carriage, and the main control carriage.

[0526] Figure 9 shows a schematic diagram of the structure of the electronic device provided in an embodiment of this application.

[0527] The electronic device may include a processor 901 and a memory 902 storing computer program instructions.

[0528] Specifically, the processor 901 may include a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0529] Memory 902 may include mass storage for data or instructions. For example, and not limitingly, memory 902 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. Where suitable, memory 902 may include removable or non-removable (or fixed) media. Where suitable, memory 902 may be internal or external to an electronic device. In a particular embodiment, memory 902 may be a non-volatile solid-state memory.

[0530] In one embodiment, memory 902 may be read-only memory (ROM). In one embodiment, the ROM may be a mask-programmed ROM, a programmable ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (EEPROM), an electrically rewritable ROM (EAROM), or flash memory, or a combination of two or more of these.

[0531] The processor 901 implements any of the methods described above by reading and executing computer program instructions stored in the memory 902.

[0532] In one example, the electronic device may also include a communication interface 903 and a bus 910. As shown in Figure 9, the processor 901, memory 902, and communication interface 909 are connected via the bus 910 and communicate with each other.

[0533] The communication interface 903 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0534] Bus 910 includes hardware, software, or both, that couples components of an electronic device together. For example, and not limitingly, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Enhanced Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), HyperTransport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 910 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0535] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0536] The functional modules shown in the above-described block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or code segments used to perform the required tasks. Programs or code segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (EROM), floppy disks, CD-ROMs, optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Code segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0537] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0538] The aspects of this application have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by dedicated hardware performing the specified functions or actions, or can be implemented by a combination of dedicated hardware and computer instructions.

[0539] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A comprehensive orthopedic multi-disease artificial intelligence surgical robot system, comprising: The total hip replacement module is used for preoperative planning, intraoperative total hip replacement, and real-time display. The total knee replacement module is used for preoperative planning, intraoperative total knee replacement, and real-time display. The unicompartmental joint replacement module is used for preoperative planning, intraoperative unicompartmental joint replacement, and real-time display. The periacetabular osteotomy module is used for preoperative planning, intraoperative periacetabular osteotomy, and real-time display. The sports medicine module is used for bone tunnel planning and ligament reconstruction, and displays the results in real time. The spinal screw placement positioning and navigation module is used for intraoperative spinal screw placement positioning and navigation and displays the results in real time. The trauma localization and navigation module is used for intraoperative trauma localization and navigation and displays the results in real time.

2. The multi-disease artificial intelligence surgical robot system for orthopedics according to claim 1, wherein, The total hip replacement module is configured as follows: Preoperative segmentation and three-dimensional reconstruction of hip joint CT images were performed to obtain a three-dimensional model of the hip joint. Preoperative planning based on a 3D model of the hip joint; Intraoperative point cloud registration, grinding, pressing, positioning, navigation, and real-time display.

3. The multi-disease artificial intelligence surgical robot system for orthopedics according to claim 1, wherein, The total knee replacement module is configured as follows: Preoperative segmentation and three-dimensional reconstruction of knee joint CT images were performed to obtain a three-dimensional model of the knee joint. Preoperative planning based on a 3D model of the knee joint; Intraoperative point cloud registration, osteotomy, and gap balancing are displayed in real time.

4. The multi-disease artificial intelligence surgical robot system for orthopedics according to claim 1, wherein, The unicompartmental joint replacement module is configured as follows: Preoperative segmentation and three-dimensional reconstruction of knee joint CT images were performed to obtain a three-dimensional model of the knee joint. Preoperative planning based on a 3D model of the knee joint; Intraoperative point cloud registration, osteotomy, and gap balancing are displayed in real time.

5. The multi-disease artificial intelligence surgical robot system for orthopedics according to claim 1, wherein, The periacetabular osteotomy module is configured for: preoperative planning, intraoperative registration, osteotomy, positioning, navigation, and real-time display.

6. The multi-disease artificial intelligence surgical robot system for orthopedics according to claim 5, wherein, Preoperative planning, intraoperative registration, osteotomy, positioning, navigation and real-time display, including: Obtain images of the patient's hip joint; The hip joint image is segmented and reconstructed in three dimensions to obtain a three-dimensional model of the hip joint; Using a 3D model of the hip joint, the osteotomy surfaces, guide lines, and safe zones around the acetabulum are planned preoperatively, the boundaries of the safe zones are determined, and the areas are rendered and colored. The preoperative planning results include osteotomy planning based on the anterior ischium, superior pubic ramus, superior iliac bone, and posterior column. During the operation, the NDI reflective positioning and navigation system of the infrared camera and the positioning frame on the curved blade of the bone scalpel are used to obtain the position of the end of the bone scalpel in real time when performing osteotomy according to the preoperative plan.

7. The multi-disease artificial intelligence surgical robot system for orthopedics according to claim 1, wherein, The sports medicine module is configured as follows: Acquire the first CT image of the knee joint; CT image segmentation based on deep learning neural networks; 3D modeling of the knee joint is performed based on the segmentation results; Establish preoperative guiding points and plan preoperative registration points in the knee joint; Bone tunnel planning and ligament reconstruction; During the operation, a probe is used to collect intraoperative registration points based on the preoperative guidance points; The probe is used to complete the rigid registration of preoperative and intraoperative registration points based on digital twin technology; the rigid registration includes two parts: coarse registration and fine registration. Based on the registration results, the coordinate system of the robotic arm, optical positioning tracker, and knee joint is established; Controlling the robotic arm to move autonomously to reconstruct bone tunnels.

8. The multi-disease artificial intelligence surgical robot system for orthopedics according to claim 7, wherein, Rigid registration of preoperative and intraoperative registration points based on digital twin technology is achieved using probes, including: First, initial registration is performed using intraoperative and preoperative registration points. At the same time, the position of the probe is located using digital twin technology, and the three-dimensional skeleton is mapped onto the color image. The position of the three-dimensional skeleton is monitored in real time using the position of the probe tip, and the initial registration matrix is ​​corrected. The registration matrix is ​​automatically adjusted again based on the corrected initial registration matrix to obtain the final rigid registration matrix; The digital twin approach uses a probe tracking algorithm based on multimodal and multiscale fusion to track the probe in real time, and a probe tip positioning algorithm based on multimodal and multiscale fusion to locate the position of the probe tip in real time.

9. The multi-disease artificial intelligence surgical robot system for orthopedics according to claim 1, wherein, The spinal screw placement positioning and navigation module is configured as follows: Record the patient's basic information during the operation; Take CT images of the patient's spine; 3D registration based on a 3D calibrator; Intraoperative planning and real-time positioning and navigation.

10. The multi-disease artificial intelligence surgical robot system for orthopedics according to claim 1, wherein, The trauma localization and navigation module is configured as follows: Record the patient's basic information during the operation; Take X-ray images of the patient's wound site; Two-dimensional registration is performed based on a two-dimensional calibrator. Intraoperative planning and real-time positioning and navigation.

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