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Machine Learning and Deep Learning for the Diagnosis of Cervical Degenerative Diseases: Systematic Review and
Hongwei Duan1, Ruiyuan Chen1, Minghui Liang1
1Department of Orthopedics, Beijing Chao-Yang Hospital, Capital Medical University, 5 JingYuan Road, Shijingshan District, Beijing, China, 86 51718688.
Background:
Cervical degenerative diseases are a global public health issue, and their incidence is rising worldwide. Although an increasing number of studies on traditional machine learning (TML) and deep learning (DL) have been conducted in the detection and segmentation of cervical degenerative diseases and have reported promising task-specific results, the performance of these models has not yet been systematically analyzed.
Objective:
This systematic review and meta-analysis aimed to summarize and evaluate existing evidence on TML and DL approaches for diagnosing cervical degenerative diseases, thereby comprehensively guiding future research and clinical applications.
Methods:
This systematic review was conducted in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. A comprehensive literature search was conducted on PubMed, Embase, the Cochrane Library, Web of Science, Scopus, and the Institute of Electrical and Electronics Engineers (IEEE Xplore) from January 2000 to June 2026, supplemented by backward and forward citation searching in Scopus. Studies evaluating TML and DL algorithms for diagnosing cervical degenerative diseases using medical imaging were included. Methodological quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool and the Quality Assessment of Diagnostic Accuracy Studies AI (QUADAS-AI) tool. For the primary diagnostic accuracy meta-analysis, data were synthesized using a bivariate mixed-effects logistic regression model. Sensitivity and specificity were summarized separately using random-effects meta-analysis with the Knapp-Hartung adjustment, and 95% prediction intervals (PIs) were reported. Certainty of evidence was assessed using the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach.
Results:
This systematic review included 30 studies, of which 21 involved a total of 25,301 patients included in the meta-analysis. The pooled sensitivity and specificity were 0.92 (95% CI 0.89-0.96; 95% PI 0.80-1.00) and 0.88 (95% CI 0.84-0.91; 95% PI 0.72-1.00), respectively. The positive likelihood ratio (LR) was 8.36 (95% CI 6.14-11.36), and the negative LR was 0.07 (95% CI 0.04-0.11). The area under the summary receiver operating characteristic (SROC) curve was 0.96 (95% CI 0.94-0.97). Leave-one-out analyses did not materially alter the pooled estimates. High risk of bias was identified in 4 studies using QUADAS-2 and in 17 using QUADAS-AI. The overall certainty of evidence was rated as low according to the GRADE approach.
Conclusions:
TML and DL models demonstrated satisfactory diagnostic performance for cervical degenerative diseases, although external validation was limited. Unlike previously published reviews in this field, this study provides pooled estimates of the diagnostic performance of TML and DL for cervical degenerative diseases and indicates that, given between-study heterogeneity and low certainty of evidence, AI should currently be used as clinical decision support rather than an independent replacement for physicians.
