Assessment of an AI aid in detection of pediatric appendicular skeletal fractures by senior and junior radiologists.

Maarek et. al

ECR 2022

Published In

ECR 2022 (2022)


Richard Maarek A-L. Hermann, A. Kamoun, R. Khelifi, A. Marchi, M. Collin, A. Jaillard, T. Nguyen, H. Ducou Le Pointe;


Purpose or Learning Objective: The number of conventional X-ray examinations in pediatric emergency departments is constantly increasing, leading to avoidable errors in interpretation by the radiologist. The use of artificial intelligence (AI) could improve the interpretation workflow by prioritizing pathological radiographs and providing assistance in fracture detection.

Methods or Background: A cohort of 300 anonymized radiographs performed for peripheral skeletal fracture detection in patients aged 2 to 21 years was retrospectively collected. The gold standard was established for each examination after an independent review by two radiologists experts in musculoskeletal imaging. In case of disagreement, a consensual review with a third expert radiologist was performed. Out of the 300 examinations, 150 presented at least a fracture. All radiographs were then read by 3 senior radiologists and 5 junior radiologists in training between the 2nd and 4th year of residency without and immediately after with the help of an AI. Poor quality radiographs were excluded from the cohort. Sensitivity and specificity for each group of radiologists were calculated without and with the help of AI.

Results or Findings: The standalone sensitivity and specificity of the AI were respectively 91% and 90%. The mean sensitivity for all groups was 73.3% without AI, it increased by almost 10% to 82.8% with the aid of the AI. For the junior radiologists, it increased from 71.9% to 82.2% (+10.3%) and for the seniors from 75.6% to 83.8% (+8.2%). On average, the specificity increased from 89.6% to 90.3% (+0.7%) and from 86.2% to 87.6% (+1.4%) for juniors. For senior radiologists, the average specificity slightly decreased from 95.1% to 94.9% (-0.2%).

Conclusion: The aid of the AI increased sensitivity by an average of 10% without decreasing specificity.

Limitations: No limitations identified.

Ethics committee approval: IRB approval n°20202256.

Funding for this study: No funding was received for this study.