Koo Bon San, Lee Jae Joon, Jung Jae-Woo, Kang Chang Ho, Joo Kyung Bin, Kim Tae-Hwan, Lee Seunghun
Division of Rheumatology, Department of Internal Medicine, Inje University Seoul Paik Hospital, College of Medicine, Inje University, Seoul, Korea.
CRESCOM, Seongnam-si, Korea.
Ther Adv Musculoskelet Dis. 2022 Jul 22;14:1759720X221114097. doi: 10.1177/1759720X221114097. eCollection 2022.
Radiographs are widely used to evaluate radiographic progression with modified stoke ankylosing spondylitis spinal score (mSASSS).
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).
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.
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.
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.
X线片广泛用于通过改良斯托克强直性脊柱炎脊柱评分（mSASSS）评估影像学进展。
本初步研究旨在开发一种深度学习模型，用于对颈椎和腰椎椎体的角进行分级，以辅助检测强直性脊柱炎（AS）患者的mSASSS。
使用Discovery XR656（GE医疗）和Digital Diagnost（飞利浦）对脊柱进行数字化X线检查。利用从颈椎和腰椎X线片的DICOM（医学数字成像和通信）格式图像中获取的关键点检测深度学习模型，在椎体之间检测椎间盘点。在裁剪椎间盘点周围的椎体区域后，将椎体的下角和上角分类为3级（完全骨桥）或0、1或2级（非骨桥）。我们训练了一个卷积神经网络模型来预测椎体下角和上角的分级。在与训练集分开的验证集中评估模型的性能。
在1280例有mSASSS数据的AS患者中，共回顾了5083张颈椎侧位X线片和5245张腰椎侧位X线片。在颈椎和腰椎中测量mSASSS的角的总数，包括上角和下角，为119414个。其中，训练集和验证集中的角的数量分别为110088个和9326个。椎体一个角的mSASSS评分的平均准确率、敏感性和特异性分别为0.91604、0.80288和0.94244。
首次开发了一种用于对椎体角进行分级的高性能深度学习模型。该模型必须进一步改进和验证，以便将来开发一种辅助评估mSASSS的计算机工具。
