Joint ligament reconstruction operation planning system and method

Abstract

The invention relates to the technical field of articular ligament reconstruction operation planning, in particular to an articular ligament reconstruction operation planning system and method, and the system comprises a data collection module which is used for obtaining the joint image data and physiological parameters of a patient; the artificial intelligence analysis module comprises a storage for storing instructions and a processor, and the processor executes the instructions to execute the following operations: the joint ligament reconstruction operation planning system can accurately process joint image data of a patient by integrating the advanced artificial intelligence analysis module; and the ligament injury is comprehensively evaluated based on a deep learning algorithm. The accurate analysis capability ensures individuation and accuracy of surgical planning, so that the surgical accuracy is remarkably improved. Meanwhile, by means of the real-time navigation and auxiliary functions provided by the system, doctors can know the operation area more clearly in the operation process, operation errors are reduced, and the safety of the operation is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of joint ligament reconstruction surgery planning, and in particular to a joint ligament reconstruction surgery planning system and method. Background Art

[0002] Joint ligament injury is one of the common sports injuries and traffic accident injuries, which has a serious impact on the patient's daily life and athletic ability. Traditional joint ligament reconstruction surgery relies on the doctor's experience and surgical skills, but it is limited by individual differences and uncertainties during the operation, and the surgical effect is often difficult to predict. With the continuous advancement of medical technology, especially the development of artificial intelligence technology, new planning methods have been provided for joint ligament reconstruction surgery. By collecting the patient's joint imaging data and physiological parameters, using deep learning algorithms to evaluate joint ligament injuries, and generating personalized surgical planning plans, the accuracy and safety of the operation can be significantly improved. Summary of the invention

[0003] (I) Purpose of the invention

[0004] In view of this, the purpose of the present invention is to provide a joint ligament reconstruction surgery planning system and method to solve the problems raised in the above background.

[0005] (II) Technical solution

[0006] A joint ligament reconstruction surgery planning system, comprising:

[0007] A data acquisition module, used to obtain joint imaging data and physiological parameters of the patient;

[0008] The artificial intelligence analysis module includes a memory storing instructions and a processor, wherein the processor executes the instructions to perform the following operations:

[0009] Preprocessing the joint image data to extract joint structure information;

[0010] Based on the joint structure information and physiological parameters, a deep learning algorithm is used to evaluate joint ligament injuries;

[0011] Generate a preliminary plan for joint ligament reconstruction surgery based on the evaluation results;

[0012] A user interaction interface, used to display the preliminary planning scheme and allow the user to modify and confirm;

[0013] The surgical navigation module provides real-time navigation and assistance during the operation based on the planning plan confirmed by the user.

[0014] Preferably, the artificial intelligence analysis module also includes:

[0015] 1) Real-time feedback module, which is used to collect real-time data during the operation and dynamically adjust the surgical plan based on the real-time data.

[0016] Preferably, a postoperative evaluation and feedback module is also included to evaluate the surgical results.

[0017] Preferably, the data acquisition module further includes:

[0018] 1) Image acquisition equipment, used to obtain X-rays, CT or MRI images of the patient's joints;

[0019] 2) Physiological parameter monitoring equipment, used to monitor the patient's blood pressure, heart rate and other physiological parameters.

[0020] Preferably, it also includes: an augmented reality (AR) auxiliary module, which is used to perform surgical simulation training on patients before surgery and provide visual surgical guidance during the operation, thereby enhancing the safety and accuracy of the operation.

[0021] A method for planning joint ligament reconstruction surgery: the method comprises the following steps:

[0022] 1) Collect the patient's joint imaging data and physiological parameters;

[0023] 2) Preprocessing the joint image data using an artificial intelligence analysis module to extract joint structure information;

[0024] 3) Based on the joint structure information and physiological parameters, using a deep learning algorithm to evaluate joint ligament injuries;

[0025] 4) Generate a preliminary planning plan for joint ligament reconstruction surgery based on the evaluation results;

[0026] 5) Displaying the preliminary planning scheme on the user interaction interface and allowing the user to modify and confirm;

[0027] 6) Based on the planning scheme confirmed by the user, the surgical navigation module is used to provide real-time navigation and assistance during the operation.

[0028] Preferably, the method further comprises the following steps:

[0029] Collect real-time data during surgery and dynamically adjust surgical plans based on real-time data.

[0030] It can be seen from the above technical solutions that the present application has the following beneficial effects:

[0031] 1. The invented joint ligament reconstruction surgery planning system integrates advanced artificial intelligence analysis modules, which can accurately process the patient's joint imaging data and conduct a comprehensive assessment of ligament injuries based on deep learning algorithms. This precise analysis capability ensures the personalization and accuracy of surgical planning, thereby significantly improving the accuracy of surgery. At the same time, the real-time navigation and auxiliary functions provided by the system enable doctors to have a clearer understanding of the surgical area during surgery, reduce operational errors, and improve the safety of surgery.

[0032] 2. The invented joint ligament reconstruction surgery planning system greatly simplifies the surgical planning process through automation and intelligent means. The doctor only needs to input the patient's imaging data and physiological parameters, and the system can automatically generate a preliminary surgical planning plan and allow the doctor to quickly modify and confirm it. This not only saves the doctor's time and energy, but also improves the efficiency of surgical planning, allowing the operation to be performed faster and the patient to recover faster.

[0033] 3. The invention can comprehensively evaluate the surgical results by providing a postoperative evaluation and feedback module, and use the evaluation results and feedback data for system optimization and deep learning algorithm training. This closed-loop feedback mechanism enables the system to continuously learn and improve, providing patients with better surgical services. At the same time, the system also provides augmented reality (AR) assisted training functions to help patients perform surgical simulation training before surgery, reduce surgical anxiety and fear, and improve patients' surgical experience and satisfaction. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0035] The following description is merely exemplary in nature and is not intended to limit the present disclosure, application, and use. It should be understood that in all of these figures, the same or similar reference numerals indicate the same or similar parts and features. The various drawings only schematically represent the concepts and principles of the embodiments of the present disclosure, and do not necessarily show the specific dimensions and proportions of the various embodiments of the present disclosure. Specific parts in specific drawings may be exaggerated to illustrate the relevant details or structures of the embodiments of the present disclosure.

[0036] See also Figure 1 , an embodiment provided by the present invention:

[0037] A joint ligament reconstruction surgery planning system, comprising:

[0038] A data acquisition module, used to obtain joint imaging data and physiological parameters of the patient;

[0039] The artificial intelligence analysis module includes a memory storing instructions and a processor, and the processor executes the instructions to perform the following operations:

[0040] Preprocess joint image data to extract joint structure information;

[0041] Based on joint structure information and physiological parameters, deep learning algorithms are used to evaluate joint ligament injuries;

[0042] Generate a preliminary plan for joint ligament reconstruction surgery based on the evaluation results;

[0043] A user interaction interface, used to display the preliminary planning scheme and allow the user to modify and confirm it;

[0044] The surgical navigation module provides real-time navigation and assistance during the operation based on the planning plan confirmed by the user.

[0045] Furthermore, the artificial intelligence analysis module also includes:

[0046] 1) Real-time feedback module, which is used to collect real-time data during the operation and dynamically adjust the surgical plan based on the real-time data, so as to ensure the safety and accuracy of the operation. When the surgical conditions change, the system can respond quickly and adjust the surgical plan to avoid potential surgical risks. In addition, real-time feedback can also help doctors better understand the progress and effect of the operation, and provide strong data support for postoperative recovery and evaluation.

[0047] Furthermore, it also includes a postoperative evaluation and feedback module to evaluate the surgical results, which can provide doctors and patients with an objective evaluation of the surgical effects and help doctors understand the success of the operation and areas that need improvement.

[0048] Furthermore, the data acquisition module also includes:

[0049] 1) Image acquisition equipment, used to obtain X-rays, CT or MRI images of the patient's joints;

[0050] 2) Physiological parameter monitoring equipment, used to monitor the patient's blood pressure, heart rate and other physiological parameters.

[0051] Furthermore, it also includes: an augmented reality (AR) auxiliary module, which is used to perform surgical simulation training on patients before surgery and provide visual surgical guidance during the operation, thereby enhancing the safety and accuracy of the operation.

[0052] A method for planning joint ligament reconstruction surgery: the method comprises the following steps:

[0053] 1) Collect the patient's joint imaging data and physiological parameters;

[0054] 2) Use artificial intelligence analysis modules to pre-process joint image data and extract joint structure information;

[0055] 3) Based on joint structure information and physiological parameters, deep learning algorithms are used to evaluate joint ligament injuries;

[0056] 4) Generate a preliminary planning plan for joint ligament reconstruction surgery based on the evaluation results;

[0057] 5) Display the preliminary planning scheme on the user interaction interface and allow the user to modify and confirm;

[0058] 6) Based on the planning scheme confirmed by the user, the surgical navigation module is used to provide real-time navigation and assistance during the operation.

[0059] Furthermore, the method further comprises the following steps:

[0060] Real-time data is collected during the operation and the surgical plan is dynamically adjusted based on the real-time data. As the training data continues to increase, the algorithm will be able to adapt to more diverse case conditions, including patients of different ages, different causes of disease, and different surgical difficulty.

[0061] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A joint ligament reconstruction surgery planning system, characterized in that: include: A data acquisition module, used to obtain joint imaging data and physiological parameters of the patient; The artificial intelligence analysis module includes a memory storing instructions and a processor, wherein the processor executes the instructions to perform the following operations: Preprocessing the joint image data to extract joint structure information; Based on the joint structure information and physiological parameters, a deep learning algorithm is used to evaluate joint ligament injuries; Generate a preliminary plan for joint ligament reconstruction surgery based on the evaluation results; A user interaction interface, used to display the preliminary planning scheme and allow the user to modify and confirm; The surgical navigation module provides real-time navigation and assistance during the operation based on the planning plan confirmed by the user.

2. A joint ligament reconstruction surgery planning system according to claim 1, characterized in that: The artificial intelligence analysis module also includes: 1) Real-time feedback module, which is used to collect real-time data during the operation and dynamically adjust the surgical plan based on the real-time data.

3. A joint ligament reconstruction surgery planning system according to claim 1, characterized in that: It also includes a postoperative evaluation and feedback module to evaluate the surgical results.

4. A joint ligament reconstruction surgery planning system according to claim 1, characterized in that: The data acquisition module also includes: 1) Image acquisition equipment, used to obtain X-rays, CT or MRI images of the patient's joints; 2) Physiological parameter monitoring equipment, used to monitor the patient's blood pressure, heart rate and other physiological parameters.

5. A joint ligament reconstruction surgery planning system according to claim 1, characterized in that: Also includes: The augmented reality (AR) auxiliary module is used to conduct surgical simulation training for patients before surgery and provide visual surgical guidance during surgery, thereby enhancing the safety and accuracy of the surgery.

6. A method for planning joint ligament reconstruction surgery, characterized in that: The method comprises the following steps: 1) Collect the patient's joint imaging data and physiological parameters; 2) Preprocessing the joint image data using an artificial intelligence analysis module to extract joint structure information; 3) Based on the joint structure information and physiological parameters, using a deep learning algorithm to evaluate joint ligament injuries; 4) Generate a preliminary planning plan for joint ligament reconstruction surgery based on the evaluation results; 5) Displaying the preliminary planning scheme on the user interaction interface and allowing the user to modify and confirm; 6) Based on the planning scheme confirmed by the user, the surgical navigation module is used to provide real-time navigation and assistance during the operation.

7. A method for planning joint ligament reconstruction surgery according to claim 6, characterized in that: The following steps are also included: Collect real-time data during surgery and dynamically adjust surgical plans based on real-time data.

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