Abstract：
After nasal bone fractures, fractures of the mandible are the most frequently encountered injuries of the facial skeleton. Accurate identification of fracture locations is critical for effectively managing these injuries. To address this need, JawFracNet, an innovative artificial intelligence method, has been developed to enable automated detection of mandibular fractures in cone-beam computed tomography (CBCT) scans. JawFracNet employs a 3-stage neural network model that processes 3-dimensional patches from a CBCT scan. Stage 1 predicts a segmentation mask of the mandible in a patch, which is subsequently used in stage 2 to predict a segmentation of the fractures and in stage 3 to classify whether the patch contains any fracture. The final output of JawFracNet is the fracture segmentation of the entire scan, obtained by aggregating and unifying voxel-level and patch-level predictions. A total of 164 CBCT scans without mandibular fractures and 171 CBCT scans with mandibular fractures were included in this study. Evaluation of JawFracNet demonstrated a precision of 0.978 and a sensitivity of 0.956 in detecting mandibular fractures. The current study proposes the first benchmark for mandibular fracture detection in CBCT scans. Straightforward replication is promoted by publicly sharing the code and providing access to JawFracNet on grand-challenge.org.
摘要：
鼻骨骨折后，下颌骨骨折是最常见的面部骨骼损伤。准确识别骨折位置对于有效管理这些损伤至关重要。为了满足这一需求，JawFracNet,一种创新的人工智能方法，已开发用于在锥形束计算机断层扫描（CBCT）扫描中自动检测下颌骨骨折。JawFracNet采用3阶段神经网络模型，该模型处理来自CBCT扫描的3维补丁。阶段1预测了一块中下颌骨的分割掩模，其随后在阶段2中用于预测骨折的分割,并且在阶段3中用于对补片是否包含任何骨折进行分类。JawFracNet的最终输出是整个扫描的断裂分割，通过聚合和统一体素级别和补丁级别的预测获得。本研究共包括164次无下颌骨骨折的CBCT扫描和171次下颌骨骨折的CBCT扫描。对JawFracNet的评估显示，在检测下颌骨骨折方面的精度为0.978，灵敏度为0.956。当前的研究提出了CBCT扫描中下颌骨骨折检测的第一个基准。通过公开共享代码并在grand-challenge.org上提供对JawFracNet的访问，可以促进直接复制。
