Title : : Diagnostic test accuracy of Artificial Intelligence (AI) in early diagnosis of osteoarthritis: A systematic review and meta-analysis of 45,588 Knees
Abstract:
Background/Purpose: Artificial intelligence (AI) rapid advancement opens new opportunities in the field of rheumatology. With better imaging, AI may help find early osteoarthritic changes that would not have been otherwise detected and help physicians with the diagnosis of early stage of Knee OsteoArthritis (KOA). The early diagnosis and timely intervention ultimately results in more favorable outcomes for the patients. This diagnostic accuracy systematic review and meta-analysis of AI detection and classification of radiographic Kellgren–Lawrence
(KL) grades for KOA.
Methods: We conducted a systematic search across PubMed, Web of Science, and Scopus databases, covering all available literature up to March 1st, 2025. Following the PRISMA guidelines (Figure 1), we screened and evaluated the methodological quality of the eligible studies, selecting only those deemed to be of high quality. We performed a meta-analysis to estimate pooled sensitivity, pooled specificity, and diagnostic likelihood ratio (LR+/LR-) were calculated. all analyses were conducted using RStudio version 4.4.2
Results: A total of 14 high-quality studies were included in the systematic review and meta-analysis, encompacing 45588 radiographs. As for KL grading, AI showed robust diagnostic performance across all grades. In KL
Grade 0, specificity was 0.954 (95% CI: 0.877–0.984) and sensitivity was 0.829 (95% CI: 0.488–0.961). For KL
Grade 1specificity remained high at 0.956 (95% CI: 0.880–0.984), with modest sensitivity 0.680 (95% CI:
0.392–0.875). For KL Grade 2, specificity was 0.937 (95% CI: 0.883–0.967) and sensitivity was 0.850 (95% CI:
0.733–0.921). KL Grade 3 yielded a specificity of 0.977 (95% CI: 0.937–0.992) and sensitivity of 0.906 (95% CI:
0.793–0.961). in KL Grade 4, specificity reached 0.995 (95% CI: 0.984–0.999) and sensitivity 0.938 (95% CI:
0.800–0.983).
Conclusion: This is the first systematic review and meta-analysis to evaluate the use of AI in the early diagnosis of KOA. AI demonstrated consistently good sensitivity and specificity across all KL grades. However, further research is needed to focus on developing, training, and validation of a unified model capable of accurate KOA diagnosis especially the early stages of the disease.
