SUMMARY OBJECTIVE: The primary objective was to assess the diagnostic accuracy of a deep learning-based artificial intelligence model for the detection of acute appendicular fractures in pediatric patients presenting with a recent history of trauma to the emergency department. The secondary goal was to examine the effect of assistive support on the emergency doctor’s ability to detect fractures. METHODS: The dataset was 5,150 radiographs of which 850 showed fractures, while 4,300 radiographs did not show any fractures. The process utilized 4,532 (88%) radiographs, inclusive of both fractured and non-fractured radiographs, in the training phase. Subsequently, 412 (8%) radiographs were appraised during validation, and 206 (4%) were set apart for the testing phase. With and without artificial intelligence assistance, the emergency doctor reviewed another set of 2,000 radiographs (400 fractures and 600 non-fractures each) for labeling in the second test. RESULTS: The artificial intelligence model showed a mean average precision 50 of 89%, a specificity of 92%, a sensitivity of 90%, and an F1 score of 90%. The confusion matrix revealed that the model trained with artificial intelligence achieved accuracies of 93 and 95% in detecting fractures, respectively. Artificial intelligence assistance improved the reading sensitivity from 93.7% (without assistance) to 97.0% (with assistance) and the reading accuracy from 88% (without assistance) to 94.9% (with assistance). CONCLUSION: A deep learning-based artificial intelligence model has proven to be highly effective in detecting fractures in pediatric patients, enhancing the diagnostic capabilities of emergency doctors through assistive support. OBJECTIVE learningbased learning based department s METHODS 5150 5 150 5,15 85 4300 4 300 4,30 4532 532 4,53 88 (88% nonfractured non phase Subsequently 41 8% 8 (8% validation 20 4% (4% 2000 2 000 2,00 400 (40 60 nonfractures each test RESULTS 89 89% 92 92% 90 90% F 9 95 respectively 937 7 93.7 970 97 0 97.0 949 94 94.9 assistance. . CONCLUSION 515 15 5,1 430 30 4,3 453 53 4,5 (88 (8 (4 200 00 2,0 40 6 93. 97. 94. 51 1 5, 43 3 4, 45 ( 2,