Artificial intelligence powered advancements in upper extremity joint MRI: A review

Chen, Wei; Lim, Lincoln Jian Rong; Lim, Rebecca Qian Ru; Yi, Zhe; Huang, Jiaxing; He, Jia; Yang, Ge; Liu, Bo

doi:10.1016/j.heliyon.2024.e28731

Heliyon

2024

DOI: 10.1016/j.heliyon.2024.e28731

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Artificial intelligence powered advancements in upper extremity joint MRI: A review

Wei Chen,

Lincoln Jian Rong Lim,

Rebecca Qian Ru Lim

et al.

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2024

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Cited by 1 publication

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Real-Time Monitoring and Assessment of Rehabilitation Exercises for Low Back Pain through Interactive Dashboard Pose Analysis Using Streamlit—A Pilot Study

Ekambaram,

Ponnusamy

2024

Electronics

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In the modern era, AI-driven algorithms have significantly influenced medical diagnosis and therapy. In this pilot study, we propose using Streamlit 1.38.0 to create an interactive dashboard, PoAna .v1—Pose Analysis, as a new approach to address these concerns. In real-time, our system accurately tracks and evaluates individualized rehabilitation exercises for patients suffering from low back pain using features such as exercise visualization and guidance, real-time feedback and monitoring, and personalized exercise plans. This dashboard was very effective for tracking rehabilitation progress. We recruited 32 individuals to participate in this pilot study. We monitored an individual’s overall performance for one week. Of the participants, 18.75% engaged in rehabilitative exercises less frequently than twice daily; 81.25% did so at least three times daily. The proposed Long Short-Term Memory (LSTM) architecture had a training accuracy score of 98.8% and a testing accuracy of 99.7%, with an average accuracy of 10-fold cross-validation of 98.54%. On the pre- and post-test assessments, there is a significant difference between pain levels, with a p < 0.05 and a t-stat value of 12.175. The proposed system’s usability score is 79.375, indicating that it provides a user-friendly environment for the user to use the PoAna .v1 web application. So far, our research suggests that the Streamlit 1.38.0-based dashboard improves patients’ engagement, adherence, and success with exercise. Future research aims to add more characteristics that can improve the complete care of low back pain (LBP) and validate the effectiveness of this intervention in larger patient cohorts.

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Real-Time Monitoring and Assessment of Rehabilitation Exercises for Low Back Pain through Interactive Dashboard Pose Analysis Using Streamlit—A Pilot Study

Ekambaram,

Ponnusamy

2024

Electronics

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Artificial intelligence powered advancements in upper extremity joint MRI: A review

Cited by 1 publication

References 91 publications

Real-Time Monitoring and Assessment of Rehabilitation Exercises for Low Back Pain through Interactive Dashboard Pose Analysis Using Streamlit—A Pilot Study

Real-Time Monitoring and Assessment of Rehabilitation Exercises for Low Back Pain through Interactive Dashboard Pose Analysis Using Streamlit—A Pilot Study

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