Knee joint stress distribution calculation method, device and equipment and storage medium
By combining finite element simulation and machine learning technology, a knee stress distribution calculation model was established, and the speed and accuracy of calculating knee stress distribution in the existing technology was solved, achieving the need for clinical personalization and rapidity.
CN119724598AActive Publication Date: 2025-03-28TONGJI UNIV
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Patent Information
- Application Number
- CN202411690926.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-25
- Publication Date
- 2025-03-28
- Estimated Expiration
- 2044-11-25
AI Technical Summary
Technical Problem
The prior art is difficult to quickly and accurately calculate knee stress distribution, and cannot meet the needs of clinical personalization and rapidity.
Method used
By establishing a finite element model of the knee joint, setting up different knee joint conditions for simulation calculation, building a data set and training using machine learning models, a knee joint stress distribution calculation model was obtained, which was used to quickly calculate the stress distribution of the knee joint under specified conditions.
Benefits of technology
The rapid and accurate calculation of knee stress distribution is achieved to meet the needs of clinical personalization and rapid development, which helps to evaluate knee joint health status and exercise safety.
✦ Generated by Eureka AI based on patent content.
Abstract
The embodiment of the invention provides a knee joint stress distribution calculation method and device, equipment and a storage medium. The method comprises the following steps: establishing a knee joint finite element model; setting different knee joint conditions for the knee joint finite element model, and performing simulation calculation under the different knee joint conditions by using the knee joint finite element model to obtain stress distribution simulation results of the knee joint under the different knee joint conditions; taking knee joint conditions as samples, taking stress distribution simulation results corresponding to the knee joint conditions as labels, constructing a data set, and training the machine learning model by using the data set to obtain a knee joint stress distribution calculation model; and calculating the stress distribution of the knee joint under the specified knee joint condition by using the knee joint stress distribution calculation model. In this way, the knee joint stress distribution can be quickly and accurately calculated through the combination of finite element simulation and machine learning.
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