Standardizing Joint-Line determination on anteroposterior knee radiographs: multicenter validation of the adductor ratio and a novel composite index in 3000 knees

To quantify how sex, age, and region affect radiographic joint-line (JL) measurements and indices on standardized anteroposterior knee radiographs across ten countries, and to identify a demography-resistant metric set for reliable JL restoration. Multicenter retrospective study of 3,000 AP knee radiographs obtained from ten countries. Standardized acquisition (SID 100 cm; AP, full extension, no rotation). Exclusions: prior peri-knee fracture/surgery, KL 3–4 OA, neurovascular deficit, septic arthritis, rheumatologic disease, BMI > 30, inadequate AP. Measurements: ATJL, FHJL, MEJL, LEJL, FW. Derived indices: literature-based ATJL/FW, FHJL/FW, MEJL/FW, LEJL/FW, JL1, JL2; newly defined JL-AF, JL-Combine, JL-Symmetry, JL-Ratio, JL3. Statistics: t-tests, one-/two-way ANOVA, multiple regression; effect sizes (Cohen’s d, η2); variability (CoV). α = 0. 05. Men showed higher FW (92. 83 ± 11. 15 vs 81. 38 ± 8. 54), ATJL (52. 16 ± 6. 31 vs 46. 07 ± 5. 46), FHJL (20. 74 ± 4. 05 vs 19. 13 ± 4. 04), MEJL (38. 22 ± 8. 96 vs 34. 62 ± 7. 67), and LEJL (35. 35 ± 8. 90 vs 32. 07 ± 7. 64); all p < 0. 001. With aging, FHJL, MEJL, and LEJL decreased (p < 0. 001); FW and ATJL showed no relevant age effect (p > 0. 05). Region strongly impacted all variables (largest η2: LEJL 0. 583, MEJL 0. 561, FW 0. 493). Among derived metrics, ATJL/FW (η2 = 0. 016, CoV = 0. 228) and JL3 (η2 = 0. 023, CoV = 0. 235) were the most stable across demographics. JL-AF (η2 = 0. 036), JL-Combine (0. 028), and JL-Symmetry (0. 028) were low-dependency validators. FW-based JL1 (η2 = 0. 899) and JL2 (0. 702) were demography-sensitive and unreliable as stand-alone predictors. Basic anatomic distances are demography-dependent and poor single guides for JL restoration. A normalize-and-combine strategy—using ATJL/FW as the anchor and JL3 as the composite confirmatory index (optionally cross-checked by JL-AF/JL-Combine/JL-Symmetry)—provides robust, transferable radiographic estimation across centers. Avoid single-variable FW models (e. g., JL1) in routine planning. Larger, population-level datasets should support personalized thresholds.