Automated Diagnosis of Infantile Skull Fractures From X-Ray Images Using an Ensemble Deep Learning Model.

Abstract

Background: To develop an artificial intelligence (AI)-assisted model for detecting skull fractures in neonates and infants using plain radiographs, enhancing diagnostic accuracy while minimizing radiation exposure.

Methods: A retrospective dataset of skull X-rays from 1,184 patients with head trauma (2010-2021) was collected. Images underwent preprocessing, including background removal, Gaussian blurring, binarization, and CLAHE-based contrast enhancement. Three convolutional neural network architectures (ResNet-50, DenseNet-121, EfficientNet-B5) were trained, with DenseNet-121 optimized using CLAHE. An ensemble model combining anterior-posterior (AP) and lateral views was constructed. The model's performance was evaluated using internal (4,298 images) and external (460 images) datasets.

Results: DenseNet-121 with CLAHE preprocessing achieved the highest mean area under the curve (AUC) of 0.926 for the AP view and 0.911 for the lateral view, demonstrating robust performance. The ensemble model, which combined both AP and lateral views, further enhanced the model's diagnostic performance. It achieved an overall AUC of 0.938, with a classification accuracy of 91.6% on the external validation dataset. Expert annotation by neurosurgeons and radiologists significantly improved the model's reliability, enabling accurate differentiation between skull fractures and normal anatomical structures, such as cranial sutures, reducing false positives.

Conclusion: This study presents a highly accurate AI model for pediatric skull fracture detection, incorporating CLAHE-enhanced preprocessing, deep learning, and expert-annotated data. The model improves clinical decision-making, reduces unnecessary computed tomography scans, and provides valuable anatomical insights into the challenges of distinguishing fractures from normal cranial variations.