Wang Ming Xing, Kim Jeoung Kun, Kim Chung Reen, Chang Min Cheol
Department of Business Administration, School of Business, Yeungnam University, Gyeongsan-si, Republic of Korea.
Department of Physical Medicine and Rehabilitation, Ulsan University Hospital, University of Ulsan College of Medicine, Ulsan, Republic of Korea.
Pain Ther. 2024 Feb;13(1):173-183. doi: 10.1007/s40122-023-00573-3. Epub 2024 Jan 8.
We developed a convolutional neural network (CNN) model to predict treatment outcomes of transforaminal epidural steroid injection (TFESI) for controlling cervical radicular pain due to cervical foraminal stenosis.
We retrospectively recruited 293 patients with cervical TFESI due to cervical radicular pain caused by cervical foraminal stenosis. We obtained a single oblique cervical radiograph from each patient. We cut each oblique cervical radiograph image into a square shape, including the foramen that was targeted for TFESI, the intervertebral disc, the facet joint of the corresponding level with the targeted foramen, and the pedicles of the vertebral bodies just above and below the targeted foramen. Therefore, images including the targeted foramen and structures around the targeted foramen were used as input data. A favorable outcome was defined as a ≥ 50% reduction in the numeric rating scale (NRS) score at 2 months post TFESI compared to the pretreatment NRS score. A poor outcome was defined as a < 50% reduction in the NRS score at 2 months post TFESI vs. the pretreatment score.
The area under the curve of our developed model for predicting the treatment outcome of cervical TFESI in patients with cervical foraminal stenosis was 0.823.
A CNN model trained using oblique cervical radiographs can be helpful in predicting treatment outcomes after cervical TFESI in patients with cervical foraminal stenosis. If the predictive accuracy is increased, we believe that the deep learning model using cervical radiographs as input data can be easily and widely used in clinics or hospitals.
我们开发了一种卷积神经网络（CNN）模型，用于预测经椎间孔硬膜外类固醇注射（TFESI）治疗因颈椎椎间孔狭窄引起的颈神经根性疼痛的效果。
我们回顾性招募了293例因颈椎椎间孔狭窄导致颈神经根性疼痛而接受颈椎TFESI治疗的患者。我们为每位患者获取了一张颈椎斜位X线片。我们将每张颈椎斜位X线片图像裁剪成正方形，包括TFESI的目标椎间孔、椎间盘、与目标椎间孔相应节段的小关节以及目标椎间孔上方和下方椎体的椎弓根。因此，包括目标椎间孔和目标椎间孔周围结构的图像被用作输入数据。良好的治疗效果定义为TFESI术后2个月时数字评分量表（NRS）评分较治疗前NRS评分降低≥50%。不良治疗效果定义为TFESI术后2个月时NRS评分较治疗前评分降低<50%。
我们开发的用于预测颈椎椎间孔狭窄患者颈椎TFESI治疗效果的模型的曲线下面积为0.823。
使用颈椎斜位X线片训练的CNN模型有助于预测颈椎椎间孔狭窄患者颈椎TFESI术后的治疗效果。如果预测准确性提高，我们相信以颈椎X线片作为输入数据的深度学习模型能够在诊所或医院中轻松且广泛地应用。
