Automatic measurement of the Cobb angle for adolescent idiopathic scoliosis using convolutional neural network

Abstract

This study proposes a convolutional neural network method for automatic vertebrae detection and Cobb angle (CA) measurement on X-ray images for scoliosis. 1021 full-length X-ray images of the whole spine of patients with adolescent idiopathic scoliosis (AIS) were used for training and segmentation. The proposed AI algorithm's results were compared with those of the manual method by six doctors using the intraclass correlation coefficient (ICC). The ICCs recorded by six doctors and AI were excellent or good, with a value of 0.973 for the major curve in the standing position. The mean error between AI and doctors was not affected by the angle size, with AI tending to measure 1.7°–2.2° smaller than that measured by the doctors. The proposed method showed a high correlation with the doctors’ measurements, regardless of the CA size, doctors’ experience, and patient posture. The proposed method showed excellent reliability, indicating that it is a promising automated method for measuring CA in patients with AIS.

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

“…In this study, we followed the measurement method proposed by Maeda et al [27], which automatically measures the CA on an input X-ray image of patients with AIS. To enhance the precision of CA measurements for both AIS and ASD, we developed three AI algorithms using the same learning method with three different sets of teaching data as follows: AIS/ASD-trained AI, AIS-trained AI, and ASD-trained AI.…”

“…In this study, we developed a preprocessing method and CNN-based deep-learning architecture for spine segmentation and vertebral detection that could be used for AIS and ASD. The presented AI algorithms were trained using the same method for developing an AI algorithm that has already achieved high measurement accuracy in AIS cases, as reported by Maeda et al [27], with additional teaching data. Of the three AI algorithms, the AIS/ASD-trained AI showed the highest accuracy.…”

“…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.…”

“…In this study, we developed three AI algorithms including the AIS/ASD-trained AI, trained with both AIS and ASD cases; the AIS-trained AI, trained solely with AIS cases; and the ASD-trained AI, trained solely with ASD cases by expanding on the development of the AI algorithm that achieved high measurement accuracy for AIS [27]. We measured the major as well as the minor curves and compared the accuracies of the three AI algorithms.…”