Tamai Koji, Imanishi Keiho, Terakawa Masaki, Uematsu Masato, Kato Minori, Toyoda Hiromitsu, Suzuki Akinobu, Takahashi Shinji, Yabu Akito, Sawada Yuta, Iwamae Masayoshi, Kobayashi Yuto, Okamura Yuki, Taniwaki Hiroshi, Kinoshita Yuki, Hoshino Masatoshi, Tabuchi Hitoshi, Nakamura Hiroaki, Terai Hidetomi
Department of Orthopedics, Osaka Metropolitan University Graduate School of Medicine, 1-5-7, Asahimachi, Abenoku, Osaka City, Osaka, Japan.
e-Growth Co, Kyoto, Japan.
Sci Rep. 2025 Jul 12;15(1):25274. doi: 10.1038/s41598-025-11285-3.
Due to symptomatic gait imbalance and a high incidence of falls, patients with cervical disease-including degenerative cervical myelopathy-have a significantly increased risk of fragility fractures. To prevent such fractures in patients with cervical disease, treating osteoporosis is an important strategy. This study aimed to validate the diagnostic yield of a deep learning algorithm for detecting osteopenia/osteoporosis using cervical radiography and compare its diagnostic accuracy with that of spine surgeons. Samples were divided into training (n = 200) and test (n = 30) datasets. The deep learning algorithm, designed to detect T-scores of the femoral neck or lumbar spine <-1.0 using cervical radiography, was constructed using a convolutional neural network model. The number of correct diagnoses was compared between the algorithm and nine spine surgeons using the independent test dataset. The results indicated that the algorithm's diagnostic accuracy, sensitivity, and specificity in the independent test dataset were 0.800, 0.818, and 0.750, respectively. The rate of corrected answers by the deep learning algorithm was significantly higher than that of nine spine surgeons in the test dataset (80.0% vs. 60.6%; p = 0.032). In conclusion, the diagnostic yield of the algorithm was higher than that of spine surgeons.
由于有症状的步态失衡和高跌倒发生率，患有颈椎病（包括退行性颈椎脊髓病）的患者发生脆性骨折的风险显著增加。为预防颈椎病患者发生此类骨折，治疗骨质疏松症是一项重要策略。本研究旨在验证一种深度学习算法利用颈椎X线摄影检测骨质减少/骨质疏松症的诊断效能，并将其诊断准确性与脊柱外科医生的诊断准确性进行比较。样本被分为训练数据集（n = 200）和测试数据集（n = 30）。使用卷积神经网络模型构建深度学习算法，该算法旨在利用颈椎X线摄影检测股骨颈或腰椎的T值<-1.0。使用独立测试数据集比较该算法与九名脊柱外科医生的正确诊断数量。结果表明，该算法在独立测试数据集中的诊断准确性、敏感性和特异性分别为0.800、0.818和0.750。在测试数据集中，深度学习算法的正确答案率显著高于九名脊柱外科医生（80.0%对60.6%；p = 0.032）。总之，该算法的诊断效能高于脊柱外科医生。
