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  • Title: [Development and validation of an automatic diagnostic tool for lumbar stability based on deep learning].
    Author: Hu H, Wang X, Yang H, Zhang J, Li K, Zeng J.
    Journal: Zhongguo Xiu Fu Chong Jian Wai Ke Za Zhi; 2023 Jan 15; 37(1):81-90. PubMed ID: 36708120.
    Abstract:
    OBJECTIVE: To develop an automatic diagnostic tool based on deep learning for lumbar spine stability and validate diagnostic accuracy. METHODS: Preoperative lumbar hyper-flexion and hyper-extension X-ray films were collected from 153 patients with lumbar disease. The following 5 key points were marked by 3 orthopedic surgeons: L4 posteroinferior, anterior inferior angles as well as L5 posterosuperior, anterior superior, and posterior inferior angles. The labeling results of each surgeon were preserved independently, and a total of three sets of labeling results were obtained. A total of 306 lumbar X-ray films were randomly divided into training (n=156), validation (n=50), and test (n=100) sets in a ratio of 3∶1∶2. A new neural network architecture, Swin-PGNet was proposed, which was trained using annotated radiograph images to automatically locate the lumbar vertebral key points and calculate L4, 5 intervertebral Cobb angle and L4 lumbar sliding distance through the predicted key points. The mean error and intra-class correlation coefficient (ICC) were used as an evaluation index, to compare the differences between surgeons' annotations and Swin-PGNet on the three tasks (key point positioning, Cobb angle measurement, and lumbar sliding distance measurement). Meanwhile, the change of Cobb angle more than 11° was taken as the criterion of lumbar instability, and the lumbar sliding distance more than 3 mm was taken as the criterion of lumbar spondylolisthesis. The accuracy of surgeon annotation and Swin-PGNet in judging lumbar instability was compared. RESULTS: ① Key point: The mean error of key point location by Swin-PGNet was (1.407±0.939) mm, and by different surgeons was (3.034±2.612) mm. ② Cobb angle: The mean error of Swin-PGNet was (2.062±1.352)° and the mean error of surgeons was (3.580±2.338)°. There was no significant difference between Swin-PGNet and surgeons (P>0.05), but there was a significant difference between different surgeons (P<0.05). ③ Lumbar sliding distance: The mean error of Swin-PGNet was (1.656±0.878) mm and the mean error of surgeons was (1.884±1.612) mm. There was no significant difference between Swin-PGNet and surgeons and between different surgeons (P>0.05). The accuracy of lumbar instability diagnosed by surgeons and Swin-PGNet was 75.3% and 84.0%, respectively. The accuracy of lumbar spondylolisthesis diagnosed by surgeons and Swin-PGNet was 70.7% and 71.3%, respectively. There was no significant difference between Swin-PGNet and surgeons, as well as between different surgeons (P>0.05). ④ Consistency of lumbar stability diagnosis: The ICC of Cobb angle among different surgeons was 0.913 [95%CI (0.898, 0.934)] (P<0.05), and the ICC of lumbar sliding distance was 0.741 [95%CI (0.729, 0.796)] (P<0.05). The result showed that the annotating of the three surgeons were consistent. The ICC of Cobb angle between Swin-PGNet and surgeons was 0.922 [95%CI (0.891, 0.938)] (P<0.05), and the ICC of lumbar sliding distance was 0.748 [95%CI(0.726, 0.783)] (P<0.05). The result showed that the annotating of Swin-PGNet were consistent with those of surgeons. CONCLUSION: The automatic diagnostic tool for lumbar instability constructed based on deep learning can realize the automatic identification of lumbar instability and spondylolisthesis accurately and conveniently, which can effectively assist clinical diagnosis. 目的: 基于深度学习研发腰椎稳定性自动诊断工具，并验证其诊断精度。. 方法: 收集153例腰椎疾病患者术前腰椎过屈、过伸位X线片，由3名骨科医师标注5个关键点，分别为L4后下角、前下角以及L5后上角、前上角、后下角，共获得3套标注结果。将306张腰椎X线片按照3∶1∶2比例随机分为训练集（n=156）、验证集（n=50）和测试集（n=100）。提出一种新的神经网络结构Swin-PGNet，使用已标注的X线片图像对其进行训练，使其能自动定位腰椎椎体关键点，并通过关键点测算L4、5椎间Cobb角和L4椎体滑移距离。对于关键点定位、Cobb角测量和椎体滑移距离测量，以平均误差、组内相关系数（intra-class correlation coefficient，ICC）比较医师标注与Swin-PGNet之间的差异。椎间Cobb角变化超过11° 作为腰椎不稳判断标准，腰椎滑移距离超过3 mm作为腰椎滑脱判断标准，比较医师和Swin-PGNet判断腰椎稳定性的准确率。. 结果: ① Swin-PGNet关键点定位平均误差为（1.407±0.939）mm，医师间平均误差为（3.034±2.612）mm。 ② Cobb角标注：Swin-PGNet平均误差为（2.062±1.352）°，医师间平均误差为（3.580±2.338）°；Swin-PGNet与3名医师间误差比较，差异均无统计学意义（P>0.05），但不同医师间误差比较差异有统计学意义（P<0.05）。③ 椎体滑移距离：Swin-PGNet平均误差为（1.656±0.878）mm，医师标注平均误差为（1.884±1.612）mm；Swin-PGNet与3名医师间误差比较以及不同医师间误差比较，差异均无统计学意义（P>0.05）。Swin-PGNet腰椎不稳判断准确率为84.0%、医师为75.3%，腰椎滑脱判断准确率分别为71.3%、70.7%，Swin-PGNet与3名医师间误差比较以及不同医师间误差比较，差异均无统计学意义（P>0.05）。④ 腰椎稳定性判定一致性分析：3名医师标注椎间Cobb角的ICC为0.913 ［95%CI（0.898，0.934）］ （P<0.05），椎体滑移距离为0.741 ［95%CI（0.729，0.796）］ （P<0.05），说明3名医师间标注具有一致性。Swin-PGNet-所有医师间椎间Cobb角ICC为0.922 ［95%CI（0.891，0.938）］ （P<0.05），椎体滑移距离为0.748 ［95%CI（0.726，0.783）］（P<0.05），说明Swin-PGNet与医师标注具有一致性。. 结论: 基于深度学习构建的腰椎稳定性自动诊断工具Swin-PGNet实现了腰椎不稳与滑脱的准确、便捷自动识别，可辅助临床进行诊断。.
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