Current medical evaluations of athletes' core muscle groups lack sufficient quantification. This study employs an improved U-Net architecture to automatically segment core muscle groups in abdominal and lower back CT images of athletes and extracts morphological parameters such as cross-sectional area, muscle density, and fat infiltration rate. Furthermore, by fusing CT morphological parameters with surface electromyography (sEMG) signals, a Long Short-Term Memory (LSTM) network is utilized to predict the core stability index. The system achieves Dice coefficients for automatic segmentation of five types of core muscle groups ranging between 0.84 and 0.92. The AI detection of fat infiltration rate is highly correlated with the pathological gold standard (correlation coefficient 0.976). The root mean square error (RMSE) of the core strength prediction model across five test movements ranges from 0.6 to 1.8, providing a reliable tool for sports injury prevention and training optimization.

Tao Zeng, Qianqian Xu, Rong Chen. Research on Medical CT Intelligent Image Artificial Intelligence Detection System Based on Athletes'core Strength. International Journal of Multimedia Computing (2022), Vol. 3, Issue 3: 91-99. https://doi.org/10.38007/IJMC.2022.030307.

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