A pilot study on deep learning-based grading of corners of vertebral bodies for assessment of radiographic progression in patients with ankylosing spondylitis

Bon San Koo; Jae Joon Lee; Jae-Woo Jung; Chang Ho Kang; Kyung Bin Joo; Tae-Hwan Kim; Seunghun Lee

doi:10.1177/1759720X221114097

A pilot study on deep learning-based grading of corners of vertebral bodies for assessment of radiographic progression in patients with ankylosing spondylitis

Ther Adv Musculoskelet Dis. 2022 Jul 22:14:1759720X221114097. doi: 10.1177/1759720X221114097. eCollection 2022.

Authors

Bon San Koo¹, Jae Joon Lee², Jae-Woo Jung², Chang Ho Kang³, Kyung Bin Joo⁴, Tae-Hwan Kim⁴, Seunghun Lee⁵

Affiliations

¹ Division of Rheumatology, Department of Internal Medicine, Inje University Seoul Paik Hospital, College of Medicine, Inje University, Seoul, Korea.
² CRESCOM, Seongnam-si, Korea.
³ Department of Radiology, Korea University Anam Hospital, Seoul, Korea.
⁴ Department of Rheumatology, Hanyang University Hospital for Rheumatic Diseases, Seoul, Korea.
⁵ Department of Radiology, Hanyang University Hospital for Rheumatic Diseases, 222-1, Wangsimni-ro, Seongdong-gu, Seoul 04763, Korea.

Abstract

Background: Radiographs are widely used to evaluate radiographic progression with modified stoke ankylosing spondylitis spinal score (mSASSS).

Objective: This pilot study aimed to develop a deep learning model for grading the corners of the cervical and lumbar vertebral bodies for computer-aided detection of mSASSS in patients with ankylosing spondylitis (AS).

Methods: Digital radiographic examination of the spine was performed using Discovery XR656 (GE Healthcare) and Digital Diagnost (Philips). The disk points were detected between the bodies using a key-point detection deep learning model from the image obtained in DICOM (digital imaging and communications in medicine) format from the cervical and lumbar spinal radiographs. After cropping the vertebral regions around the disk point, the lower and upper corners of the vertebral bodies were classified as grade 3 (total bony bridges) or grades 0, 1, or 2 (non-bridges). We trained a convolutional neural network model to predict the grades in the lower and upper corners of the vertebral bodies. The performance of the model was evaluated in a validation set, which was separate from the training set.

Results: Among 1280 patients with AS for whom mSASSS data were available, 5,083 cervical and 5245 lumbar lateral radiographs were reviewed. The total number of corners where mSASSS was measured in the cervical and lumbar vertebrae, including the upper and lower corners, was 119,414. Among them, the number of corners in the training and validation sets was 110,088 and 9326, respectively. The mean accuracy, sensitivity, and specificity for mSASSS scoring in one corner of the vertebral body were 0.91604, 0.80288, and 0.94244, respectively.

Conclusion: A high-performance deep learning model for grading the corners of the vertebral bodies was developed for the first time. This model must be improved and further validated to develop a computer-aided tool for assessing mSASSS in the future.

Keywords: ankylosing spondylitis; artificial intelligence; deep learning; mSASSS; radiographic progression.