Artificial intelligence in degenerative cervical disease: A systematic review of MRI-based diagnostic models - Qian Du; Xinxin Shao; Minbo Zhang; Guangru Cao (Digital Health)
Objective This systematic review evaluates the performance and limitations of AI-based models for Degenerative cervical diseases (DCD) diagnosis using MRI. Methods A comprehensive literature search was conducted in three databases—PubMed, Embase, and Web of Science—covering studies published between January 2010 and March 2024. Studies were included if they employed AI techniques for the diagnosis or prognosis of DCD using MRI. Key performance metrics, methodological details, and limitations were extracted and analyzed. Results Eleven studies met the inclusion criteria, with AI models showing high diagnostic performance. Accuracy ranged from 81.58% to 98%, sensitivities from 84% to 98%, specificities from 90% to 100%, and AUC values reached up to 0.97. Convolutional neural networks (CNN) were the most frequently used models (four studies), followed by support vector machines (three studies). Comparative analysis revealed that CNN-based approaches showed consistently high performance in ossification of the posterior longitudinal ligament detection, while traditional machine learning methods demonstrated varying effectiveness in cervical spondylotic myelopathy classification. Sample sizes varied significantly, ranging from 28 to 900 patients. MRI protocols also differed across studies, with variations in field strengths, slice thicknesses, and sequences used. Seven studies assessed inter-rater reliability. Most studies lacked external validation, which raises concerns about the generalizability of the models. Additionally, hardware configurations were inconsistently reported, and data augmentation techniques were underutilized, limiting the robustness of the models in smaller datasets. Conclusion While AI models for DCD diagnosis using MRI show high diagnostic potential, methodological weaknesses such as insufficient external validation and small sample sizes hinder broader clinical adoption. Future research should focus on larger, standardized, multi-center studies to improve the robustness and clinical relevance of AI-driven tools for DCD diagnosis.