School of Industrial and Management Engineering, Korea University, Anam-ro 145, Seongbuk-gu, Seoul, 02841, South Korea.
Department of Orthopaedic Surgery, Korea University Ansan Hospital, 123, Jeokgeum-ro, Danwon-gu, Ansan, Gyeonggi-do, South Korea.
Sci Rep. 2024 Jul 15;14(1):16308. doi: 10.1038/s41598-024-67017-6.
Vertebral compression fractures (VCFs) of the thoracolumbar spine are commonly caused by osteoporosis or result from traumatic events. Early diagnosis of vertebral compression fractures can prevent further damage to patients. When assessing these fractures, plain radiographs are used as the primary diagnostic modality. In this study, we developed a deep learning based fracture detection model that could be used as a tool for primary care in the orthopedic department. We constructed a VCF dataset using 487 lateral radiographs, which included 598 fractures in the L1-T11 vertebra. For detecting VCFs, Mask R-CNN model was trained and optimized, and was compared to three other popular models on instance segmentation, Cascade Mask R-CNN, YOLOACT, and YOLOv5. With Mask R-CNN we achieved highest mean average precision score of 0.58, and were able to locate each fracture pixel-wise. In addition, the model showed high overall sensitivity, specificity, and accuracy, indicating that it detected fractures accurately and without misdiagnosis. Our model can be a potential tool for detecting VCFs from a simple radiograph and assisting doctors in making appropriate decisions in initial diagnosis.
胸腰椎压缩性骨折（VCF）通常由骨质疏松症引起，或由创伤事件引起。早期诊断胸腰椎压缩性骨折可以防止患者进一步受损。在评估这些骨折时，通常使用普通 X 光片作为主要诊断方式。在这项研究中，我们开发了一种基于深度学习的骨折检测模型，可以作为骨科初级保健的工具。我们使用 487 张侧位 X 光片构建了一个 VCF 数据集，其中包括 L1-T11 椎体的 598 处骨折。为了检测 VCF，我们训练和优化了 Mask R-CNN 模型，并将其与另外三个流行的模型（Cascade Mask R-CNN、YOLOACT 和 YOLOv5）在实例分割方面进行了比较。使用 Mask R-CNN，我们获得了最高的平均精度分数 0.58，并且能够逐像素定位每个骨折。此外，该模型还表现出较高的总体敏感性、特异性和准确性，表明它可以准确地检测骨折，并且没有误诊。我们的模型可以成为从简单的 X 光片中检测 VCF 的潜在工具，并帮助医生在初步诊断中做出适当的决策。
