Treffer: Cognitive evaluation model and high-resolution medical images in sports injury rehabilitation under bone density changes.

Title:
Cognitive evaluation model and high-resolution medical images in sports injury rehabilitation under bone density changes.
Authors:
Li W; Department of PE, Zhejiang Sci-Tech University, Hangzhou 310018 Zhejiang, China. Electronic address: liwenping@zstu.edu.cn., Gu Z; Basic Education Department, Jiaxing Vocational & Techniacal College, Jiaxing 314000 Zhejiang, China. Electronic address: gzm@jxvtc.edu.cn.
Source:
SLAS technology [SLAS Technol] 2025 Dec; Vol. 35, pp. 100350. Date of Electronic Publication: 2025 Sep 19.
Publication Type:
Evaluation Study; Journal Article
Language:
English
Journal Info:
Publisher: SAGE Publications Country of Publication: United States NLM ID: 101697564 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2472-6311 (Electronic) Linking ISSN: 24726303 NLM ISO Abbreviation: SLAS Technol Subsets: MEDLINE
Imprint Name(s):
Original Publication: Thousand Oaks, CA : SAGE Publications, [2017]-
MeSH Terms:
Bone Density*/physiology , Athletic Injuries*/diagnostic imaging , Athletic Injuries*/physiopathology , Athletic Injuries*/rehabilitation , Boosting Machine Learning Algorithms* , Image Processing, Computer-Assisted*/methods, Humans ; Tomography, X-Ray Computed
Contributed Indexing:
Keywords: Bone density changes; Cognitive evaluation model; Extreme gradient boosting; Graph convolutional network; High-resolution medical images; Sports injury rehabilitation
Entry Date(s):
Date Created: 20250921 Date Completed: 20251217 Latest Revision: 20251217
Update Code:
20251217
DOI:
10.1016/j.slast.2025.100350
PMID:
40976400
Database:
MEDLINE

Weitere Informationen

In the study of bone density changes and sports injury rehabilitation, traditional image processing technology lacks accuracy in analysis, rehabilitation assessment methods lack quantitative and systematic analysis, and interdisciplinary comprehensive evaluation is lacking. This paper constructs an innovative cognitive assessment model that combines bone density changes, sports injury rehabilitation, and high-resolution medical image analysis. It uses high-resolution CT (Computed Tomography) images and X-ray images to extract bone density data. It uses image processing technology to remove noise, enhance, and standardize, providing accurate bone density values for subsequent input. GCN (Graph Convolutional Network) can be used to automatically identify and classify images of sports injury sites, extract features of the injured area, record and analyze the patient's physical activities during the rehabilitation stage, and evaluate the recovery process of sports injuries in real time. Combining bone density data with sports injury imaging features, XGBoost (Extreme Gradient Boosting) is used to build a cognitive evaluation model, which conducts a comprehensive analysis of multi-dimensional data and provides personalized rehabilitation evaluation. It can integrate technologies from fields such as medicine, engineering, and computer science to establish an interdisciplinary comprehensive evaluation system, achieve multi-angle and multi-dimensional analysis, and ensure the comprehensiveness and accuracy of the model. The experimental results show that the MAE (Mean Absolute Error) of the GCN in this paper is 0.131 in 10 different injury sites, and the average MSE (Mean Squared Error) is about 0.032, which has higher image analysis accuracy. The average accuracy and R² of XGBoost in six different samples are about 0.87 and 0.91, respectively, and the prediction effect of the cognitive evaluation model is apparent.
(Copyright © 2025. Published by Elsevier Inc.)

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.