Clinical Evaluation of Novel Custom 3D-Printed Meshed-Silicone Orthotics Utilizing Standing Foot Scans and Dynamic Gait Data.

Abstract

Background: Conventional orthotic insoles demonstrate limited accommodation for individual foot morphology and plantar pressure distribution patterns, resulting in biomechanical inefficiencies and patient discomfort. Computational approaches integrating artificial intelligence with additive manufacturing technologies offer promising solutions for personalized orthotic design. This study investigates the clinical efficacy of AI-driven 3D-printed meshed-silicone orthotics through comprehensive biomechanical assessment.

Methods: A prospective cohort study (n = 21; 8 females, 13 males; age 25.6 ± 3.68 years; BMI 25.48 ± 3.46) evaluated custom orthotics fabricated using machine learning algorithms applied to individual foot and gait data. Pre- and post-intervention assessments included Visual Analog Scale (VAS), Foot Function Index (FFI), Foot Posture Index (FPI), plantar pressure distribution analysis, and 3-dimensional gait analysis over a 4-week period.

Results: FFI scores showed minimal variation (pre-intervention: 13.48 ± 13.14; post-intervention: 14.10 ± 12.96). Significant biomechanical modifications were observed: multi-planar lower extremity alignment correction at hip, knee, and ankle joints. Plantar pressure redistribution demonstrated decreased heel loading with unchanged forefoot pressure distribution, accompanied by significant maximum metatarsal pressure elevation (P < .05).

Conclusions: II. AI-integrated 3D-printed meshed-silicone orthotics demonstrated measurable biomechanical improvements including lower extremity alignment optimization and plantar pressure redistribution. These computational design methodologies combined with advanced manufacturing technologies establish a foundation for personalized orthotic interventions in clinical biomechanics applications.