Standardizing Joint-Line Determination on Anteroposterior Knee Radiographs: Multicenter Validation of the Adductor Ratio and a Novel Composite Index in 3,000 Knees

Objective :To quantify how sex, age, and region affect radiographic joint-line (JL) measurements and indices on standardized AP knee radiographs across ten countries, and to identify a demography-resistant metric set for reliable JL restoration. Methods :Multicenter retrospective study of 3,000 AP knee radiographs (1,500 female/1,500 male; ages 20–79) from 10 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. Results :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. Conclusion :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.