医学
人工智能
面部外伤
深度学习
断裂(地质)
计算机科学
放射科
口腔正畸科
计算机视觉
外科
地质学
岩土工程
作者
Daiki Morita,Ayako Kawarazaki,Mazen Soufi,Yoshito Otake,Yoshinobu Sato,Toshiaki Numajiri
标识
DOI:10.1016/j.jormas.2024.101914
摘要
Midfacial fractures are among the most frequent facial fractures. Surgery is recommended within 2 weeks of injury, but this time frame is often extended because the fracture is missed on diagnostic imaging in the busy emergency medicine setting. Using deep learning technology, which has progressed markedly in various fields, we attempted to develop a system for the automatic detection of midfacial fractures. The purpose of this study was to use this system to diagnose fractures accurately and rapidly, with the intention of benefiting both patients and emergency room physicians.
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