Department of Orthopedics, Osaka City University Graduate School of Medicine, 1-5-7, Asahimachi, Abenoku, Osaka city, Osaka, 545-8585, Japan.
Department of Technology and Design Thinking for Medicine, Hiroshima University, Hiroshima, Japan.
Sci Rep. 2022 Feb 8;12(1):2113. doi: 10.1038/s41598-022-06140-8.
The cervical ossification of the posterior longitudinal ligament (cOPLL) is sometimes misdiagnosed or overlooked on radiography. Thus, this study aimed to validate the diagnostic yield of our deep learning algorithm which diagnose the presence/absence of cOPLL on cervical radiography and highlighted areas of ossification in positive cases and compare its diagnostic accuracy with that of experienced spine physicians. Firstly, the radiographic data of 486 patients (243 patients with cOPLL and 243 age and sex matched controls) who received cervical radiography and a computer tomography were used to create the deep learning algorithm. The diagnostic accuracy of our algorithm was 0.88 (area under curve, 0.94). Secondly, the numbers of correct diagnoses were compared between the algorithm and consensus of four spine physicians using 50 independent samples. The algorithm had significantly more correct diagnoses than spine physicians (47/50 versus 39/50, respectively; p = 0.041). In conclusion, the accuracy of our deep learning algorithm for cOPLL diagnosis was significantly higher than that of experienced spine physicians. We believe our algorithm, which uses different diagnostic criteria than humans, can significantly improve the diagnostic accuracy of cOPLL when radiography is used.
颈椎后纵韧带骨化症（cOPLL）在影像学上有时会被误诊或漏诊。因此，本研究旨在验证我们的深度学习算法在颈椎 X 光片上诊断 cOPLL 存在/不存在的能力，并突出阳性病例中的骨化区域，同时将其诊断准确性与经验丰富的脊柱医师进行比较。首先，我们使用 486 名患者（243 名患有 cOPLL 和 243 名年龄和性别相匹配的对照组）的影像学数据来创建深度学习算法。该算法的诊断准确性为 0.88（曲线下面积为 0.94）。其次，我们使用 50 个独立样本比较了算法和四位脊柱医师共识之间的正确诊断数量。算法的正确诊断数量明显多于脊柱医师（分别为 47/50 与 39/50；p=0.041）。总之，我们的深度学习算法在 cOPLL 诊断方面的准确性明显高于经验丰富的脊柱医师。我们相信，与人类使用的不同诊断标准相比，我们的算法可以在使用 X 光片时显著提高 cOPLL 的诊断准确性。
