Journal of Shandong University (Health Sciences) ›› 2026, Vol. 64 ›› Issue (2): 34-43.doi: 10.6040/j.issn.1671-7554.0.2024.0799

• Review • Previous Articles    

Orthopedic disease diagnosis and treatment assistance methods based on artificial intelligence and gait analysis

JI Xinyu, YU Siyi, SUN Yuanyuan, JI Bing   

  1. School of Control Science and Engineering, Shandong University, Jinan 250061, Shandong, China
  • Published:2026-02-10

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Abstract: As a non-invasive method, gait analysis plays an important role in the diagnosis and treatment of orthopaedic diseases. By observing and analysing a patients walking style, gait analysis can reveal motor function problems such as changes in stride length, decreased stride speed and abnormal joint angles, all of which are early symptoms of cervical spondylotic myelopathy, lumbar spinal stenosis, osteoarthritis and other diseases. Traditional gait analysis methods typically require professionals to manually interpret the results from gait measurement devices. The integration of artificial intelligence(AI)technology with gait data has led to intelligent analysis methods that not only automate gait analysis but also significantly enhance the objectivity, consistency, and accuracy of the process. The application of intelligent analysis methods in orthopedic disease diagnosis and treatment can facilitate more efficient and accurate diagnoses and provide valuable insights for personalized rehabilitation plans through real-time monitoring of gait changes. However, challenges such as multimodal gait data fusion, the interpretability of AI models, and the portability and ease of use of gait measurement devices remain areas that require further research and development. These issues represent key directions for future studies. This paper primarily explored the research progress and existing challenges of intelligent gait analysis in assisting orthopedic diagnosis and treatment, with the aim of promoting the wider clinical application of gait analysis technology.

Key words: Gait analysis, Artificial intelligence, Assisted diagnosis and treatment, Orthopaedic diseases, Gait data

CLC Number: 

  • R681
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