Adding AI to MRI-based classification systems improves return-to-play predictions for professional athletes with muscle injuries.
Key Details
- 1Research presented at RSNA explores AI augmentation of MRI for classifying sports-related thigh muscle injuries.
- 2Study involved 40 professional athletes with thigh muscle injuries, all receiving 1.5-T MRI within 72 hours of injury.
- 3Traditional systems assessed: BAMIC, MLG-R, and their integrated use.
- 4Without AI, integrated BAMIC + MLG-R showed AUC 0.984, sensitivity 96.4%, and specificity 94.4%.
- 5With AI, integrated model performance improved: AUC 0.993, sensitivity 98.2%, specificity 96.7%.
- 6Researchers recommend further, larger validation studies and refinement of deep-learning models for clinical use.
Why It Matters
This study demonstrates that AI can enhance MRI-based classification systems, potentially leading to more accurate and evidence-based decisions on athlete rehabilitation and return to play, reducing reinjury risks and improving outcomes. Broader, validated models could standardize and streamline sports injury management in radiology.
Source
AuntMinnie
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