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
Related News
•Radiology Business
Study Highlights Limitations of AI in Prostate MRI Screening
New research points to several shortcomings in implementing AI for MRI-based prostate cancer screening.
•AuntMinnie
Deep Learning Model Predicts Brain Tumor MRI Enhancement Without Gadolinium
German researchers developed a deep learning approach to predict MRI contrast enhancement in brain tumors without the need for gadolinium-based agents.
•Radiology Business
SimonMed Imaging Introduces Paid AI Add-Ons for Routine Exams
SimonMed Imaging is launching new AI-powered elective services for routine imaging exams with additional out-of-pocket costs for patients.