Maki Satoshi, Furuya Takeo, Inoue Masahiro, Shiga Yasuhiro, Inage Kazuhide, Eguchi Yawara, Orita Sumihisa, Ohtori Seiji
Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan.
Center for Frontier Medical Engineering, Chiba University, Chiba 263-8522, Japan.
J Clin Med. 2024 Jan 25;13(3):705. doi: 10.3390/jcm13030705.
Spinal injuries, including cervical and thoracolumbar fractures, continue to be a major public health concern. Recent advancements in machine learning and deep learning technologies offer exciting prospects for improving both diagnostic and prognostic approaches in spinal injury care. This narrative review systematically explores the practical utility of these computational methods, with a focus on their application in imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI), as well as in structured clinical data. Of the 39 studies included, 34 were focused on diagnostic applications, chiefly using deep learning to carry out tasks like vertebral fracture identification, differentiation between benign and malignant fractures, and AO fracture classification. The remaining five were prognostic, using machine learning to analyze parameters for predicting outcomes such as vertebral collapse and future fracture risk. This review highlights the potential benefit of machine learning and deep learning in spinal injury care, especially their roles in enhancing diagnostic capabilities, detailed fracture characterization, risk assessments, and individualized treatment planning.
脊柱损伤，包括颈椎和胸腰椎骨折，仍然是一个重大的公共卫生问题。机器学习和深度学习技术的最新进展为改善脊柱损伤护理中的诊断和预后方法提供了令人兴奋的前景。这篇叙述性综述系统地探讨了这些计算方法的实际效用，重点关注它们在计算机断层扫描（CT）和磁共振成像（MRI）等成像技术以及结构化临床数据中的应用。在纳入的39项研究中，34项专注于诊断应用，主要使用深度学习来执行诸如椎体骨折识别、良性和恶性骨折鉴别以及AO骨折分类等任务。其余5项是预后研究，使用机器学习来分析预测椎体塌陷和未来骨折风险等结果的参数。本综述强调了机器学习和深度学习在脊柱损伤护理中的潜在益处，特别是它们在增强诊断能力、详细骨折特征描述、风险评估和个性化治疗规划方面的作用。
