aceAutomated Diagnosis of Infantile Skull Fractures From X-Ray Images Using an Ensemble Deep Learning Model
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-…
An ensemble deep learning model combining AP and lateral skull X-rays detected skull fractures in neonates and infants with 91.6% accuracy and 0.938 AUC on external validation, improving diagnostic accuracy while reducing need for CT.
Evidence
- Peer-reviewedJournal of Korean Medical Science2026-09-07
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Truvace Impact Record TRV-2026-1031, v1: “Automated Diagnosis of Infantile Skull Fractures From X-Ray Images Using an Ensemble Deep Learning Model.” Truvace, 2026-09-09. /record/TRV-2026-1031 (accessed at citation time). sha256 46bae60096fd3e42…
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