Dynamic Distance Mapping Enhances Hallux Valgus Progression Visualization

Background/Objectives: Hallux valgus (HV), a common foot deformity, is difficult to quantify beyond traditional angular measurements. This study introduces a novel dynamic distance mapping technique to visualize HV progression and identify spatial features linked to severity. Methods: A retrospective analysis of 335 feet from 178 patients undergoing HV surgery at Hasharon Hospital, Israel (2014-2024), utilized custom Python software to annotate 24 landmarks on preoperative standing anteroposterior radiographs. This generated 276 normalized Euclidean distances, analyzed via Pearson correlation against HV angles (HVA, IMA, DMAA, HIA). Results: Seven distances correlated negatively (r > 0.4, p < 0.05) and seven positively with HVA, involving the distal phalanx, sesamoids, and second metatarsal. Eleven distances showed strong positive correlation (r > 0.4, p < 0.05) with IMA, reflecting displacement patterns. Moderate correlations were observed with DMAA (six negative, r -0.3 to -0.4; two positive, r 0.3 to 0.4, p < 0.05) and HIA (two negative, r -0.3 to -0.4, p < 0.05). Visualizations highlighted progressive spatial changes. Conclusions: Dynamic distance mapping provides valuable insights into hallux valgus (HV) progression, as evidenced by significant correlations with HVA and IMA, supporting its potential role in surgical planning. However, its ability to capture 3D deformities requires validation against weightbearing computed tomography (WBCT). Future research should explore correlations with specific indications for corrective osteotomies to enhance clinical applicability.