Performance Evaluation of a Commercial Deep Learning Software for Detecting Intracranial Hemorrhage in a Pediatric Population.

This study evaluates a commercially available AI tool (Aidoc) for intracranial hemorrhage (ICH) detection-originally trained on adults-in pediatric patients, addressing the critical need for timely diagnosis and current research gaps in pediatric AI applications. This single-center, retrospective study included pediatric patients aged 6-17 who underwent head CT between January 2017 and November 2022. Radiological reports (unaided by AI) and CT images were analyzed by natural language processing (NLP) and image-based algorithms, respectively, to classify ICH presence or absence. Ground truth was assumed for concordant cases. Three radiologists independently reviewed discrepant cases using majority vote. Among 2502 pediatric patients undergoing head CT, the AI algorithm flagged 292 cases as suspected ICH-positive. A total of 174 discordant cases between NLP and AI were independently reviewed to create the reference standard. Results showed 144 true positives, 6 false negatives, 148 false positives, and 2204 true negatives, yielding sensitivity of 96.0% (91.5-98.5%) and specificity of 93.7% (92.6-94.7%). Overall algorithm accuracy was 93.8% (92.8-94.8%). The most frequent false positives were choroid plexus calcifications and hyperdense venous sinuses, while subdural hemorrhages accounted for most false negatives. This deep learning AI algorithm trained on adult data performs well in detecting pediatric ICH, with 96.0% sensitivity and 93.7% specificity. However, common false positives, choroid plexus calcifications and hyperdense venous sinuses, reflect pediatric-specific features, while missed subdural hemorrhages mirror known adult limitations. Results highlight the need for pediatric-focused AI training to improve diagnostic accuracy in this underserved population.