Development and validation of a deep learning model for automatic detection of depressed skull fractures from CT scans.

BACKGROUND: Depressed skull fractures with bone depression greater than in one cortex might cause major consequences and require surgery in traumatic head injury patients. Therefore, skull fractures with depression in more than one cortex must be identified quickly and accurately. METHODS: This study proposes using a deep learning model to deal with the task. Cranial CT scans of traumatic head injury patients with and without depressed skull fractures were collected for this retrospective investigation. A real-time object detection model, You Only Look Once (YOLO), was adopted to detect depressed skull fractures in more than one cortex. We proposed a two-phase training strategy for training the model. The model was evaluated using internal and external test datasets. The detection performance was reported in terms of accuracy, sensitivity, specificity, precision, negative predictive value, F1-score, and area under the receiver operating characteristic curve. RESULTS: The deep learning model demonstrated strong performance on an internal test dataset (accuracy = 0.957); however, its performance declined on two external test datasets (accuracy = 0.884 and 0.857). CONCLUSION: This model enables automated detection of depressed skull fractures, streamlining the clinical workflow by flagging high-priority cases for expedited radiologist review.