Enhancing pediatric fracture detection: multicenter evaluation of a deep learning AI model and its impact on radiologist performance
Objective
This study investigates the efficacy of a deep learning-based artificial intelligence (AI) model in detecting pediatric fractures on musculoskeletal (MSK) radiographs and assesses the impact of AI-assistance on the performance of radiologists.
Methods
In Phase 1, the performance of the AI model was evaluated on 3016 MSK pediatric radiographs from 4 imaging centers in the US. Ground truth was established by consensus of pediatric radiologists. Phase 2 was a retrospective multi-reader, multi-center (MRMC) study using 189 cases. Twenty readers participated in two separate reading sessions evaluating for fracture, with and without AI assistance, with a one-month washout period.
Results
The AI model achieved a high standalone performance with accuracy (0.94), sensitivity (0.96), and specificity (0.86). Subgroup analysis revealed that the model maintained high performance across study types and confounders, including age (Se>0.94), gender (Se>0.96), anatomical region (Se>0.93), and fracture types (Se>0.93). With AI assistance, reader accuracy increased significantly from 0.93 to 0.96 (p < 0.05), sensitivity significantly improved from 0.86 to 0.93 (p < 0.05), and specificity improved from 0.94 to 0.95. The average reading time per exam was shortened by 26.1% with AI assistance.
Conclusion
The AI model's high accuracy in detecting pediatric fractures underscores its significant clinical utility. The integration of this tool enhanced overall radiologist performance and boosted the diagnostic confidence among non-specialist readers.
