Abstract
BACKGROUND: Developing computer-assisted methods to measure the Torg-Pavlov ratio (TPR), defined as the ratio of the sagittal diameter of the cervical spinal canal to the sagittal diameter of the corresponding vertebral body on lateral radiographs, can reduce subjective influence and speed up processing. The TPR is a critical diagnostic parameter for developmental cervical spinal stenosis (DCSS), as it normalizes variations in radiographic magnification and provides a cost-effective alternative to CT/MRI in resource-limited settings. No study focusing on automatic measurement was reported. The aim was to develop a deep learning-based model for automatically measuring the TPR, and then to establish the distribution of asymptomatic Chinese TPR. METHODS: A total of 1623 lateral cervical X-ray images from normal individuals were collected. 1466 and 157 images were used as the training dataset and testing dataset, respectively. We adopted a neural network called High-Resolution Vision Transformer (HRViT), which was trained on the annotated X-ray image dataset to automatically locate the landmarks and calculate the TPR. The accuracy of the TPR measurement was evaluated using mean absolute error (MAE), intra-class correlation coefficient (ICC), r value and Bland-Altman plot. RESULTS: The TPR at C2-C7 was 1.26, 0.92, 0.90, 0.93, 0.92, and 0.89, respectively. The MAE between HRViT and surgeon R1 was 0.01, between surgeon R1 and surgeon R2 was 0.17, between surgeon R1 and surgeon R3 was 0.17. The accuracy of HRViT for DCSS diagnosis was 84.1%, which was greatly higher than those of both surgeon R2 (57.3%) and surgeon R3 (56.7%). The consistency of TPR measurements was 0.77-0.9 (ICC) and 0.78-0.9 (r value) between HRViT and surgeon R1. CONCLUSIONS: We have explored a deep-learning algorithm for automated measurement of the TPR on cervical lateral radiographs to diagnose DCSS, which had outstanding performance comparable to clinical senior doctors.