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BMC Musculoskeletal Disorders

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Open Access 01-12-2022 | Research

Sagittal intervertebral rotational motion: a deep learning-based measurement on flexion–neutral–extension cervical lateral radiographs

Authors: Yuting Yan, Xinsheng Zhang, Yu Meng, Qiang Shen, Linyang He, Guohua Cheng, Xiangyang Gong

Published in: BMC Musculoskeletal Disorders | Issue 1/2022

Abstract

Background

The analysis of sagittal intervertebral rotational motion (SIRM) can provide important information for the evaluation of cervical diseases. Deep learning has been widely used in spinal parameter measurements, however, there are few investigations on spinal motion analysis. The purpose of this study is to develop a deep learning-based model for fully automated measurement of SIRM based on flexion–neutral–extension cervical lateral radiographs and to evaluate its applicability for the flexion–extension (F/E), flexion–neutral (F/N), and neutral–extension (N/E) motion analysis.

Methods

A total of 2796 flexion, neutral, and extension cervical lateral radiographs from 932 patients were analyzed. Radiographs from 100 patients were randomly selected as the test set, and those from the remaining 832 patients were used for training and validation. Landmarks were annotated for measuring SIRM at five segments from C2/3 to C6/7 on F/E, F/N, and N/E motion. High-Resolution Net (HRNet) was used as the main structure to train the landmark detection network. Landmark performance was assessed according to the percentage of correct key points (PCK) and mean of the percentage of correct key points (MPCK). Measurement performance was evaluated by intra-class correlation coefficient (ICC), Pearson correlation coefficient, mean absolute error (MAE), root mean square error (RMSE), and Bland-Altman plots.

Results

At a 2-mm distance threshold, the PCK for the model ranged from 94 to 100%. Compared with the reference standards, the model showed high accuracy for SIRM measurements for all segments on F/E and F/N motion. On N/E motion, the model provided reliable measurements from C3/4 to C6/7, but not C2/3. Compared with the radiologists’ measurements, the model showed similar performance to the radiologists.

Conclusions

The developed model can automatically measure SIRM on flexion–neutral–extension cervical lateral radiographs and showed comparable performance with radiologists. It may provide rapid, accurate, and comprehensive information for cervical motion analysis.

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Title: Sagittal intervertebral rotational motion: a deep learning-based measurement on flexion–neutral–extension cervical lateral radiographs
Authors: Yuting Yan
Xinsheng Zhang
Yu Meng
Qiang Shen
Linyang He
Guohua Cheng
Xiangyang Gong
Publication date: 01-12-2022
Publisher: BioMed Central
Published in: BMC Musculoskeletal Disorders / Issue 1/2022
Electronic ISSN: 1471-2474
DOI: https://doi.org/10.1186/s12891-022-05927-0

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Sagittal intervertebral rotational motion: a deep learning-based measurement on flexion–neutral–extension cervical lateral radiographs

Abstract

Background

Methods

Results

Conclusions

Keynote webinar | Spotlight on medication adherence

Springer Medicine

Abstract

Background

Methods

Results

Conclusions

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