Study design: Retrospective study.

Objective: To develop and validate a computed tomography-based deep learning(DL) model for diagnosing cervical spinal stenosis(CSS).

Summary of background data: Although magnetic resonance imaging (MRI) is widely used for diagnosing CSS, its inherent limitations, including prolonged scanning time, limited availability in resource-constrained settings, and contraindications for patients with metallic implants, make computed tomography (CT) a critical alternative in specific clinical scenarios. The development of CT-based DL models for CSS detection holds promise in transcending the diagnostic efficacy limitations of conventional CT imaging, thereby serving as an intelligent auxiliary tool to optimize healthcare resource allocation.

Methods: Paired CT/MRI images were collected. CT images were divided into training, validation, and test sets in an 8:1:1 ratio. The two-stage model architecture employed: (1) a Faster R-CNN-based detection model for localization, annotation, and extraction of regions of interest (ROI); (2) comparison of 16 convolutional neural network (CNN) models for stenosis classification to select the best-performing model. The evaluation metrics included accuracy, F1-score, and Cohen's κ coefficient, with comparisons made against diagnostic results from physicians with varying years of experience.

Results: In the multiclass classification task, four high-performing models (DL1-b0, DL2-121, DL3-101, and DL4-26d) achieved accuracies of 88.74%, 89.40%, 89.40%, and 88.08%, respectively. All models demonstrated >80% consistency with senior physicians and >70% consistency with junior physicians.In the binary classification task, the models achieved accuracies of 94.70%, 96.03%, 96.03%, and 94.70%, respectively. All four models demonstrated consistency rates slightly below 90% with junior physicians. However, when compared with senior physicians, three models (excluding DL4-26d) exhibited consistency rates exceeding 90%.

Conclusions: The DL model developed in this study demonstrated high accuracy in CT image analysis of CSS, with a diagnostic performance comparable to that of senior physicians.

Keywords: Cervical Spinal Stenosis; Computed Tomography; Convolutional Neural Network; Deep Learning.

Copyright © 2025 Wolters Kluwer Health, Inc. All rights reserved.

研究设计：回顾性研究。

目的：开发并验证一种基于计算机断层扫描（CT）的深度学习模型，用于诊断颈椎管狭窄症（CSS）。

背景资料摘要：尽管磁共振成像（MRI）广泛应用于诊断CSS，但其固有的局限性包括长时间扫描时间、在资源受限环境中可用性有限以及对于植入了金属假体的患者存在禁忌等，使得CT成为某些临床情况下的重要替代选择。基于CT的深度学习模型开发在超越传统CT影像诊断效能限制方面具有前景，从而可以作为智能辅助工具优化医疗资源配置。

方法：收集配对的CT/MRI图像，并将CT图像按8:1:1的比例分为训练集、验证集和测试集。采用两阶段模型架构：（1）基于Faster R-CNN的目标检测模型用于定位、标注及提取感兴趣区域（ROI）；（2）比较16种卷积神经网络（CNN）模型进行狭窄分类，以选择最佳性能的模型。评估指标包括准确性、F1分数和Cohen的κ系数，并与不同年资医生的诊断结果进行了对比。

结果：在多类分类任务中，四个高性能模型（DL1-b0、DL2-121、DL3-101和DL4-26d）分别实现了88.74%、89.40%、89.40%和88.08%的准确率。所有模型与资深医生的一致性均超过80%，与初级医生的一致性均超过70%。在二分类任务中，各模型分别实现了94.70%、96.03%、96.03%和94.70%的准确率。所有四个模型与初级医生的一致性略低于90%，但与资深医生相比，三个模型（不包括DL4-26d）的一致性均超过90%。

结论：本研究开发的深度学习模型在CT图像分析中对CSS诊断表现出高准确性，并且其诊断性能可媲美资深医生。

关键词：颈椎管狭窄症；计算机断层扫描；卷积神经网络；深度学习。

版权所有 © 2025 Wolters Kluwer Health, Inc.保留所有权利。