Artificial intelligence for osteoporosis detection on panoramic radiography: A systematic review and meta analysis
Nikoo Ghasemi1, Rata Rokhshad2, Qonche Zare3
1Department of Orthodontics and Dentofacial Orthopedics, School of Dentistry, Zanjan University of Medical Sciences, Zanjan, Iran.
Introduction:
Osteoporosis is a disease characterized by low bone mineral density and an increased risk of fractures. In dentistry, mandibular bone morphology, assessed for example on panoramic images, has been employed to detect osteoporosis. Artificial intelligence (AI) can aid in diagnosing bone diseases from radiographs. We aimed to systematically review, synthesize and appraise the available evidence supporting AI in detecting osteoporosis on panoramic radiographs.
Data:
Studies that used AI to detect osteoporosis on dental panoramic images were included.
Sources:
On April 8, 2023, a first comprehensive search of electronic databases was conducted, including PubMed, Scopus, Embase, IEEE, arXiv, and Google Scholar (grey literature). This search was subsequently updated on October 6, 2024.
Study Selection:
The Quality Assessment and Diagnostic Accuracy Tool-2 was employed to determine the risk of bias in the studies. Quantitative analyses involved meta-analyses of diagnostic accuracy measures, including sensitivity and specificity, yielding Diagnostic Odds Ratios (DOR) and synthesized positive likelihood ratios (LR+). The certainty of evidence was assessed using the Grading of Recommendations Assessment, Development, and Evaluation system.
Results:
A total of 24 studies were included. Accuracy ranged from 50% to 99%, sensitivity from 50% to 100%, and specificity from 38% to 100%. A minority of studies (n=10) had a low risk of bias in all domains, while the majority (n=18) showed low risk of applicability concerns. Pooled sensitivity was 87.92% and specificity 81.93%. DOR was 32.99, and L+ 4.87. Meta-regression analysis indicated that sample size had only a marginal impact on heterogeneity (R² = 0.078, p = 0.052), suggesting other study-level factors may contribute to variability. Egger's test suggested potential small-study effects (p < 0.001), indicating a risk of publication bias.
Conclusion:
AI, particularly deep learning, showed high diagnostic accuracy in detecting osteoporosis on panoramic radiographs. The results indicate a strong potential for AI to enhance osteoporosis screening in dental settings. However, significant heterogeneity across studies and potential small-study effects highlight the need for further validation, standardization, and larger, well-powered studies to improve model generalizability.
Clinical Significance:
The application of AI in analyzing panoramic radiographs could transform osteoporosis screening in routine dental practice by providing early and accurate diagnosis. This has the potential to integrate osteoporosis detection seamlessly into dental workflows, improving patient outcomes and enabling timely referrals for medical intervention. Addressing issues of model validation and comparability is critical to translating these findings into widespread clinical use.
