Comparison of manual versus automated measurement of Cobb angle in idiopathic scoliosis based on a deep learning keypoint detection technology

Abstract

Purpose The present study compared manual and automated measurement of Cobb angle in idiopathic scoliosis based on deep learning keypoint detection technology. Methods A total of 181 anterior–posterior spinal X-rays were included in this study, including 165 cases of idiopathic scoliosis and 16 normal adult cases without scoliosis. We labeled all images and randomly chose 145 as the training set and 36 as the test set. Two state-of-the-art deep learning object detection models based on convolutional neural networks were used in sequence to segment each vertebra and locate the vertebral corners. Cobb angles measured from the output of the models were compared to manual measurements performed by orthopedic experts. Results The mean Cobb angle in test cases was 27.4° ± 19.2° (range 0.00–91.00°) with manual measurements and 26.4° ± 18.9° (range 0.00–88.00°) with automated measurements. The automated method needed 4.45 s on average to measure each radiograph. The intra-class correlation coefficient (ICC) for the reliability of the automated measurement of the Cobb angle was 0.994. The Pearson correlation coefficient and mean absolute error between automated positioning and expert annotation were 0.990 and 2.2° ± 2.0°, respectively. The analytical result for the Spearman rank-order correlation was 0.984 (p < 0.001). Conclusion The automated measurement results agreed with the experts’ annotation and had a high degree of reliability when the Cobb angle did not exceed 90° and could locate multiple curves in the same scoliosis case simultaneously in a short period of time. Our results need to be verified in more cases in the future.

Key-Point Detection Algorithm of Deep Learning Can Predict Lower Limb Alignment with Simple Knee Radiographs

“…To solve these problems, a new prediction method based on DL using a key-point detection algorithm is proposed in our study. The key-point detection algorithm was considered the most suitable model because of the ability of the algorithm to find a specific point where the WBL passes through the tibia plateau [ 22 , 23 ]. Key-point detection algorithms are often used for pose estimation, face detection, and object detection [ 17 , 23 ].…”

“…The key-point detection algorithm was considered the most suitable model because of the ability of the algorithm to find a specific point where the WBL passes through the tibia plateau [ 22 , 23 ]. Key-point detection algorithms are often used for pose estimation, face detection, and object detection [ 17 , 23 ]. Interestingly, the prediction accuracy decreased when the marking increased.…”

Comparative Accuracy of Three Artificial Intelligence Algorithms for Automatic Cobb Angle Measurement in Scoliosis: An Analysis Using Disease-specific Teaching Data

“…It can also measure CA with high accuracy in postures other than standing, such as supine and lateral bending. There are algorithms for measuring curves other than the main curve [14,17,18], but all of them reported only on AIS cases and there was no mention of supine or lateral bending postures. The measurement of CA in the minor curves and in the supine or lateral bending position is necessary to classify the cases based on classi cations, such as the Lenke classi cation, and to determine the range of xation in surgery.…”

“…However, these methods require the manual selection of the appropriate end vertebrae and endplates by an operator. In this context, there has been a recent marked increase in reports on arti cial intelligence (AI) algorithms for fully automated measurement of CA using convolutional neural networks (CNN) [14][15][16][17][18][19][20][21][22][23][24][25][26][27]. The measurement error of the CA in these reports ranged from 1.9° to 9.9°, which is similar to or lower than that in the manual and computer-assisted manual methods.…”

“…Most of these reports focused only on AIS, and only few have mentioned ASD cases [22,26]. Moreover, reports on the measurement of minor curves are limited [14,17,18], and only one report has veri ed the accuracy in the supine or lateral bending posture [27]. These problems, which include a lack of versatility for AIS and ASD, lack of measurement of minor curves, and lack of proven accuracy in various postures, remain unresolved.…”