Artificial intelligence for detecting temporomandibular joint osteoarthritis using radiographic image data: A systematic review and meta-analysis of diagnostic test accuracy

• Published in: PloS one 2023-07, Vol.18 (7), p.e0288631-e0288631
• Main Authors: Xu, Liang, Chen, Jiang, Qiu, Kaixi, Yang, Feng, Wu, Weiliang
• Format: Article
• Language: English
• Subjects: Accuracy; Arthritis; Artificial Intelligence; Bias; Biology and Life Sciences; Computer and Information Sciences; Data analysis; Database searching; Deep learning; Diagnosis; Diagnostic imaging; Diagnostic tests; Diagnostic Tests, Routine; Disease prevention; Diseases; Evaluation; Heterogeneity; Humans; Imaging; Internet/Web search services; Machine learning; Magnetic resonance imaging; Medical diagnosis; Medical imaging equipment; Medical tests; Medicine and Health Sciences; Meta-analysis; Methods; Minority & ethnic groups; Modelling; Morphology; Neural networks; Online searching; Osteoarthritis; Osteoarthritis - diagnostic imaging; Physical Sciences; Quality assessment; Quality of life; Radiography; Regression analysis; Research and Analysis Methods; Reviews; ROC Curve; Systematic review; Temporomandibular joint; Temporomandibular Joint - diagnostic imaging; Temporomandibular joint disorders; Tomography
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0288631

In this review, we assessed the diagnostic efficiency of artificial intelligence (AI) models in detecting temporomandibular joint osteoarthritis (TMJOA) using radiographic imaging data. Based upon the PRISMA guidelines, a systematic review of studies published between January 2010 and January 2023 was conducted using PubMed, Web of Science, Scopus, and Embase. Articles on the accuracy of AI to detect TMJOA or degenerative changes by radiographic imaging were selected. The characteristics and diagnostic information of each article were extracted. The quality of studies was assessed by the QUADAS-2 tool. Pooled data for sensitivity, specificity, and summary receiver operating characteristic curve (SROC) were calculated. Of 513 records identified through a database search, six met the inclusion criteria and were collected. The pooled sensitivity, specificity, and area under the curve (AUC) were 80%, 90%, and 92%, respectively. Substantial heterogeneity between AI models mainly arose from imaging modality, ethnicity, sex, techniques of AI, and sample size. This article confirmed AI models have enormous potential for diagnosing TMJOA automatically through radiographic imaging. Therefore, AI models appear to have enormous potential to diagnose TMJOA automatically using radiographic images. However, further studies are needed to evaluate AI more thoroughly.