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Poster Session

Osteoarthritis

Poster Session A

Session: (0306–0336) Osteoarthritis – Clinical Poster I

0333: Diagnostic Test Accuracy of Artificial Intelligence in Early Diagnosis of Osteoarthritis: A Systematic Review and Meta-Analysis of 45,588 Knees

Sunday, October 26, 2025
10:30 AM - 12:30 PM Central Time
Location: Hall F1

    Abstract Poster Presenter(s)

  • MA

    Mohamed Abdelsalam, MSc, MBBCH

    MUST University
    Giza, Egypt

    Disclosure(s): No financial relationships with ineligible companies to disclose


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 study aims to assess the diagnostic accuracy 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 and excluding studies without outcome data or insufficient quality. We performed a meta-analysis to estimate pooled sensitivity, pooled specificity, and diagnostic likelihood ratios (LR+/LR-). All analyses were conducted using RStudio version 4.4.2.

Results: A total of 14 studies were included in the systematic review and meta-analysis, encompassing 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) with Pre Test Probability (PP) of 30%, Post Test Probability +ve (PTP +ve) of 88%, and Post Test Probability -ve (PTP -ve) of 7%. For KL Grade 1, specificity remained high at 0.956 (95% CI: 0.880–0.984), with modest sensitivity of 0.680 (95% CI: 0.392–0.875) and (PP: 20%, PTP +ve: 79%, PTP -ve: 8%). For KL Grade 2, specificity was 0.937 (95% CI: 0.883–0.967), sensitivity was 0.850 (95% CI: 0.733–0.921) and (PP: 25%, PTP +ve: 82%, PTP -ve: 5%). KL Grade 3 yielded a specificity of 0.977 (95% CI: 0.937–0.992), sensitivity of 0.906 (95% CI: 0.793–0.961) and (PP: 15%, PTP +ve: 87%, PTP -ve: 2%). in KL Grade 4, specificity reached 0.995 (95% CI: 0.984–0.999), sensitivity 0.938 (95% CI: 0.800–0.983) and (PP: 10%, PTP +ve: 96%, PTP -ve: 1%).

Conclusion: This is the first systematic review and meta-analysis to evaluate the use of AI in the early diagnosis of KOA. AI demonstrates consistently good sensitivity and specificity across all KL grades. These findings support the use of AI as a valuable assistant for rheumatologists in the early diagnosis of KOA. However, further research is needed to focus on developing, training, and validating of a unified model capable of accurate KOA diagnosis, especially the early stages of the disease.