Using machine learning to automatically measure axial vertebral rotation on radiographs in adolescents with idiopathic scoliosis.

Abstract

Adolescent idiopathic scoliosis is a 3D lateral spinal curvature coupled with axial vertebral rotation (AVR). Measuring AVR during clinic is important because it affects treatment options and predicts the risk of scoliosis progression. However, manual measurements are time consuming and have high inter-rater and intra-rater errors. This study aimed to develop a machine learning algorithm based on convolutional neural networks (CNNs) to automatically calculate AVR on posteroanterior radiographs using three different segmentations including spinal column, individual vertebra, and pedicles. Separate labeling and training processes were performed on each of the developed segmentation algorithms. The final machine learning software was tested on 221 vertebrae from 17 spinal radiographs. An experienced rater with over 25 years of experience measured the 221 vertebral rotations manually. By comparing the manual and the fully automatic measurements, 81% (178/221) of the automatic measurements were within the clinical acceptance error (±5°). The mean absolute difference and the standard deviation between the manual and automatic measurements was 4.3°±5.7°. Based on the Bland-Altman plot, the manual and automatic measurements had a strong correlation and no bias. The error did not relate to the severity of the rotation. This method is fully automatic, and the result is comparable to others.