Diagnostic performance of deep-learning algorithms in radiographic grading of knee osteoarthritis: a systematic review and meta-analysis.

BackgroundDeep learning (DL) has been increasingly applied to grade knee osteoarthritis (KOA) on radiographs, but reported diagnostic performance varies across Kellgren-Lawrence (K-L) grades.PurposeTo systematically evaluate the diagnostic performance of DL models for radiographic KOA grading.Material and MethodsPubMed, Embase, and Web of Science were searched through November 2024 for studies using DL algorithms to grade KOA on X-ray images. Sensitivity and precision were synthesized. Heterogeneity was assessed using the I2 statistic. Subgroup analyses and meta-regression were conducted according to transfer learning, external validation, multi-task learning, joint training strategy, and data splitting. Publication bias was assessed using funnel plots and Egger's test. Study quality was evaluated using the revised QUADAS-2 tool.ResultsOf 1004 records screened, 32 studies were included. Pooled sensitivity for K-L grades 0-4 was 0.90, 0.66, 0.80, 0.87, and 0.88, respectively, and pooled precision was 0.87, 0.71, 0.81, 0.86, and 0.91, respectively. Diagnostic performance was poorest for K-L grade 1, particularly in sensitivity, indicating limited reliability for early-stage KOA detection. Heterogeneity was high across outcomes and grades, particularly for sensitivity in K-L grades 1 and 2 and precision in K-L grades 0 and 1. Meta-regression identified transfer learning and data splitting as potential sources of heterogeneity. Egger's tests suggested no statistically significant small-study effects.ConclusionDL models showed better diagnostic performance for moderate-to-severe radiographic KOA than for early-stage disease. However, the poor sensitivity for K-L grade 1, substantial heterogeneity, and limited external validation suggest that current DL models are not yet reliable for early KOA detection or ready for routine clinical implementation. Further standardized reporting, robust validation, and multicenter external evaluation are required.