The DOI matches its Crossref record.
Medical Sports Rehabilitation Deep Learning System of Sports Injury Based on MRI Image Analysis
Abstract
Medical sports rehabilitation deep learning system of sports injury based on MRI image analysis is proposed in this paper. Preparation activities are various body exercises that are purposely performed before physical education, training, and competition. It is a transitional phase from the static state to the moving state of the human body. Preparatory activities can improve the excitability of the central nervous system, improve the ability of the cerebral cortex to analyze and judge movements, and thus make the movement more coordinated and accurate. At the same time prepare activity can also improve the respiratory and circulatory system functions and reduce the muscles, ligaments of the sticky nature and the contraction of muscles for speed and strength, in order to maximize the capacity of the physical movement and injury prevention campaign ready. Therefore, how to use the MRI image to numerically analyze the mentioned task is essential. We integrate the deep learning model to propose the novel image enhancement and recognition model to undertake the task of medical sports rehabilitation system. The experimental result proves the performance is robust.
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Citations
Cited by
Analysis and design of dual-feature fusion neural network for sports injury estimation model
Meng, L., & Qiao, E. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06151-y · Cited by 47
Luo, A., Gou, S., Tong, N., Liu, B., Jiao, L., Xu, H., Wang, Y. G., & Ding, T. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01082-z · Cited by 26
“To ensure appropriate treatment of meniscal injuries, precise grading methods based on knee MRI have been proposed to distinctly assess the injury severity 2 – 4 .”
Real-time sports injury monitoring system based on the deep learning algorithm
Ren, L., Wang, Y., & Li, K. (2024) · BMC Medical Imaging · doi:10.1186/s12880-024-01304-6 · Cited by 25
“Compared to existing methods that use similar data, such as MRI [21], the system proposed in this document offers greater real-time capability and specificity, allowing faster and more accurate injury analysis and risk warning, thus protecting the health of athletes.”
MethodCai, H. W. (2022) · The Journal of Supercomputing · doi:10.1007/s11227-021-04145-0 · Cited by 22
“Fitness activities encompass a diversified list of sports during which an effective and scientific training strategy is crucial, since improper training may lead to workout injuries, such as sprained ankles and muscle pulls and strain [1, 2].”
BackgroundMethodCui, H., & Chang, C. (2020) · IEEE Access · doi:10.1109/access.2020.3003652 · Cited by 19
“Today, digital technologies are introducing to the sports arena due to secure mobile networks, wearable, and artificial intelligence technology [1].”
BackgroundSecurity Analysis of Sports Injury Medical System Based on Internet of Health Things Technology
Xia, Y., & Yun-xiang, F. (2020) · IEEE Access · doi:10.1109/access.2020.3039262 · Cited by 16
Big data analytics for image processing and computer vision technologies in sports health management
Jin, N., & Zhan, X. (2024) · Technology and Health Care · doi:10.3233/thc-231875 · Cited by 15
“Health data, mobility information, data on instruction, and assessment are examples of integrated big data services that might be invaluable to athletes as they develop winning tournament tactics [4].”
Research on Sports Injury Rehabilitation Detection Based on IoT Models for Digital Health Care
Wu, Z., Huang, Z., Tang, N., Wang, K., Bian, C., Li, D., Kuraki, V., & Schmid, F. (2024) · Big Data · doi:10.1089/big.2023.0134 · Cited by 11
Xiao, D., Zhu, F., Jiang, J., & Niu, X. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1273931 · Cited by 12
“Sports injuries, especially bone and muscle damages, not only impact athletes’ performance and competitive state but may also lead to prolonged physical discomfort and health issues (Ba, 2020).”
BackgroundTracing Mechanism of Sports Competition Pressure Based on Backpropagation Neural Network
Zhao, H., & Liu, S. (2021) · Complexity · doi:10.1155/2021/6652896 · Cited by 9
“Because of their outstanding work, they have provided a catalyst for the research of neuron networks, making this research and application enter the heyday [15].”
BackgroundTeikari, P., & Pietrusz, A. (2021) · SportRxiv (OSF Preprints) · doi:10.31236/osf.io/w734a · Cited by 14
The teaching of sports science of track and field-based on nonlinear mathematical equations
Guan, F., Cao, J., Ren, J., & Song, W. (2021) · Applied Mathematics and Nonlinear Sciences · doi:10.2478/amns.2021.2.00155 · Cited by 7
“Electronic computers, televisions, video recordings, and other audio-visual equipment directly lead people to the process of physical education and sports training as a means of education and training [10].”
BackgroundTereshchenko, S. N., Cheremisina, I. A., & Safiullina, A. A. (2022) · Terapevticheskii arkhiv · doi:10.26442/00403660.2022.04.201450 · Cited by 7
PRE-COMPETITION PSYCHOLOGICAL TRAINING IN COLLEGE JUMPING ATHLETES
Cheng, F. (2023) · Revista Brasileira de Medicina do Esporte · doi:10.1590/1517-8692202329012022_0637 · Cited by 6
Peng, X., & Tang, L. (2022) · Journal of Healthcare Engineering · doi:10.1155/2022/8547906 · Cited by 5
“However, the medical rehabilitation cycle for sports injuries is long, and there are too many uncontrollable factors, which cannot guarantee the medical rehabilitation effect [1].”
BackgroundRetractedAn Efficient Teaching Model of International Cooperation Based on Artificial Intelligence
Xiao, M., & Yi, H. (2021) · Scientific Programming · doi:10.1155/2021/7049857 · Cited by 4
Research and Implementation of the Sports Analysis System Based on 3D Image Technology
Wang, H., Gao, J., & Liu, J. (2021) · Wireless Communications and Mobile Computing · doi:10.1155/2021/4266417 · Cited by 4
Statistical Calculation Method and Analysis of Athletes’ Biorhythm State and Sports Injury
Su, S. (2021) · Mobile Information Systems · doi:10.1155/2021/8313185 · Cited by 3
Yang, G., Yang, H., Wang, M., et al. (2022) · Journal of Nanomaterials · doi:10.1155/2022/6062005 · Cited by 3
“The preparation method is more precise and simple [6]; however, the operation steps of this method are too many and the operation is too complicated [7], but the cost of this method is too high.”
MethodA Study on the Optimization Simulation of Big Data Video Image Keyframes in Motion Models
Guo, J., Sun, P., & Tsai, S. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/2508174 · Cited by 2
Combining Medical Images and Biomechanical Data in Sports Injury Prediction ModelsPreprint
Cai, W., Zhu, J., & Zhuang, X. (2024) · Research Square · doi:10.21203/rs.3.rs-4884511/v1 · Cited by 2
Naser, I. A., Adheem, R. N. A., Ali, A. K., et al. (2024) · International Sports Science Alexandria Journal · doi:10.21608/isalexu.2024.434099 · Cited by 1
PHYSICAL TRAINING GUIDED BY FUNCTIONAL MOVEMENT SCREEN IN THE REHABILITATION OF SPORTS INJURIES
Lv, T., & Fu, T. (2022) · Revista Brasileira de Medicina do Esporte · doi:10.1590/1517-8692202228052022_0076 · Cited by 1
Yu, L. (2024) · Molecular & cellular biomechanics · doi:10.62617/mcb674 · Cited by 1
Auxiliary diagnosis of small tumor in mammography based on deep learning
Liu, Y., Li, J., Xu, D., Meng, H., Dong, J., Zhao, T., Tang, L., & Zou, H. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03358-8 · Cited by 1
“Experimental results show the robustness of the algorithm (Ba 2020; Kim et al.”
MethodLi, K., & Fu, N. (2022) · Scanning · doi:10.1155/2022/4368871 · Cited by 1
“In the final follow-up, 88% of patients had no slight pain and 72% of patients had no daily dysfunction [4].”
BackgroundRetractedMa, X., & Zhang, Z. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/4219976 · Cited by 1
“From the beginning of use to the scrapping of medical equipment, the medical equipment software system’s perception of itself and the early warning of possible failures are significant [1].”
BackgroundRetractedEFFECT OF MODERATE RUNNING ON SPORTS INJURY REHABILITATION
Zhang, X., & Liu, L. (2022) · Revista Brasileira de Medicina do Esporte · doi:10.1590/1517-8692202228062022_0066
An Improved Neural Network Model for Early Detection of Joint Injuries in Tai Chi Sports
Shao, Z., Zhong, Y., Yu, Z., & Chu, X. (2022) · Mobile Information Systems · doi:10.1155/2022/8476803 · Cited by 1
Application of biomechanics in sports rehabilitation
Zhang, N. (2024) · Molecular & cellular biomechanics · doi:10.62617/mcb.v21.178
“The experiment showed that the robustness of this method was robust [7].”
Method
Metadata
- Authors
- Ba, H.
- Affiliations
- Aba Teachers University (CN)
- Corresponding author
- Ba, Hongbing
- Venue
- Journal of Medical Imaging and Health Informatics
- Publisher
- American Scientific Publishers
- Published
- 2020-05-01
- Volume
- 10
- Issue
- 5
- Pages
- 1091-1097
- ISSN
- 2156-7018 (ISSN-L)
- OpenAlex
- W3006109402
- Semantic Scholar
- 2ad6a37ee0163a79a6abda6e6ffbca6fb6b0f66f
- Cited by
- 30
- References
- 0
- Citations by year
- 2024: 8 · 2023: 2 · 2022: 10 · 2021: 8 · 2020: 2
- FWCI
- 4.96
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Medical Imaging and Health Informatics (publishedVersion)
- Topics
- Health Sciences › Medicine › Orthopedics and Sports Medicine › Sports injuries and prevention
- Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- sports injuries, sports rehabilitation, MRI image analysis, deep learning, image enhancement, injury recognition, warm-up, injury prevention
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
