Department of Radiology, Balgrist University Hospital, Forchstrasse 340, 8008, Zurich, Switzerland.
Faculty of Medicine, University of Zurich, Zurich, Switzerland.
Eur Radiol. 2023 May;33(5):3188-3199. doi: 10.1007/s00330-022-09354-6. Epub 2022 Dec 28.
The aim is to validate the performance of a deep convolutional neural network (DCNN) for vertebral body measurements and insufficiency fracture detection on lumbar spine MRI.
This retrospective analysis included 1000 vertebral bodies in 200 patients (age 75.2 ± 9.8 years) who underwent lumbar spine MRI at multiple institutions. 160/200 patients had ≥ one vertebral body insufficiency fracture, 40/200 had no fracture. The performance of the DCNN and that of two fellowship-trained musculoskeletal radiologists in vertebral body measurements (anterior/posterior height, extent of endplate concavity, vertebral angle) and evaluation for insufficiency fractures were compared. Statistics included (a) interobserver reliability metrics using intraclass correlation coefficient (ICC), kappa statistics, and Bland-Altman analysis, and (b) diagnostic performance metrics (sensitivity, specificity, accuracy). A statistically significant difference was accepted if the 95% confidence intervals did not overlap.
The inter-reader agreement between radiologists and the DCNN was excellent for vertebral body measurements, with ICC values of > 0.94 for anterior and posterior vertebral height and vertebral angle, and good to excellent for superior and inferior endplate concavity with ICC values of 0.79-0.85. The performance of the DCNN in fracture detection yielded a sensitivity of 0.941 (0.903-0.968), specificity of 0.969 (0.954-0.980), and accuracy of 0.962 (0.948-0.973). The diagnostic performance of the DCNN was independent of the radiological institution (accuracy 0.964 vs. 0.960), type of MRI scanner (accuracy 0.957 vs. 0.964), and magnetic field strength (accuracy 0.966 vs. 0.957).
A DCNN can achieve high diagnostic performance in vertebral body measurements and insufficiency fracture detection on heterogeneous lumbar spine MRI.
• A DCNN has the potential for high diagnostic performance in measuring vertebral bodies and detecting insufficiency fractures of the lumbar spine.
旨在验证深度学习卷积神经网络（DCNN）在腰椎 MRI 上进行椎体测量和不稳定性骨折检测的性能。
本回顾性分析纳入了在多个机构接受腰椎 MRI 检查的 200 名患者（75.2±9.8 岁）的 1000 个椎体。160/200 名患者有≥1 个椎体不稳定性骨折，40/200 名患者无骨折。比较 DCNN 与 2 名接受过专业培训的肌肉骨骼放射科医生在椎体测量（前后高度、终板凹陷程度、椎体角度）和不稳定性骨折评估方面的表现。统计分析包括（a）使用组内相关系数（ICC）、kappa 统计和 Bland-Altman 分析评估观察者间可靠性指标，（b）诊断性能指标（灵敏度、特异性、准确性）。如果 95%置信区间不重叠，则认为存在统计学显著差异。
放射科医生与 DCNN 之间在椎体测量方面的读者间一致性非常好，前后椎体高度和椎体角度的 ICC 值>0.94，上下终板凹陷的 ICC 值为 0.79-0.85，为良好至非常好。DCNN 在骨折检测中的性能表现为灵敏度 0.941（0.903-0.968），特异性 0.969（0.954-0.980），准确性 0.962（0.948-0.973）。DCNN 的诊断性能与放射科机构（准确性 0.964 比 0.960）、MRI 扫描仪类型（准确性 0.957 比 0.964）和磁场强度（准确性 0.966 比 0.957）无关。
DCNN 可在异质腰椎 MRI 上实现椎体测量和不稳定性骨折检测的高诊断性能。
• DCNN 在测量腰椎椎体和检测不稳定性骨折方面具有很高的诊断性能潜力。
