A deep learning model accurately identifies hard-to-detect physeal fractures in children's wrist x-rays.
Key Details
- 1AI model developed using 2,103 x-rays from 1,082 pediatric patients (mean age 10)
- 2Best performing model (EfficientNet-B2) achieved 84% accuracy, 81% precision, 89% recall, and F1-score of 0.86 on test data
- 3Model uses Grad-CAM for interpretability, highlighting regions contributing to decisions
- 4Physeal fractures are often misdiagnosed (up to 46% rate), risking later growth deformities
- 5Proposed use as decision-support tool in urgent care and ER settings; potential to reduce missed diagnoses
Why It Matters
Missed pediatric fracture diagnoses can cause significant long-term harm. AI decision-support could help frontline clinicians recognize subtle injuries and improve outcomes, addressing a known gap in pediatric musculoskeletal radiology.
Source
AuntMinnie
Related News
•AuntMinnie
AI Advancements and Studies Highlighted in Digital X-Ray Insider
This edition covers AI models for fracture detection, mortality prediction, and more, along with new research using x-ray and DEXA modalities.
•Cardiovascular Business
AI Model Dramatically Enhances Severe Heart Attack Detection via ECGs
Queen of Hearts AI model outperforms standard care in detecting severe heart attacks from ECGs.
•AuntMinnie
Machine Learning Model Enhances Risk Stratification for Prostate MRI
Researchers developed machine learning models that outperform PSA testing in predicting abnormal prostate MRI findings for suspected prostate cancer.