AI-Enhanced Modular Material Selection for Patient-Specific Diabetic Insoles via Silicone Molding.

ObjectiveThis study proposes an AI-enhanced modular material selection approach for designing diabetic insoles. By customizing materials for forefoot and heel modules, the method enables personalized support and pressure redistribution, resulting in cost-effective insoles.BackgroundDiabetic foot ulcers occur as a result of elevated plantar pressure and poor foot sensation. To mitigate this risk, developing insoles that redistribute plantar pressure can significantly lower the likelihood of ulcer formation.MethodThe insole was fabricated using cost-effective silicone molding, with functional personalization achieved via an AI-enhanced approach that selected optimal, interchangeable cushioning materials for forefoot and heel modules of each patient. The design integrates a ¾-length porous silicone upper layer with regionally optimized materials. Laboratory wear trials involving 23 diabetic patients compared the offloading performance of the hybrid insole (AIO) against barefoot, a PORON® Medical 4708 insole (PUR), and a cork-EVA insole (EVA).ResultsAIO demonstrated a 37.6% reduction in peak plantar pressure compared to barefoot condition while increasing contact area by 36.0% across the plantar surface. It significantly outperformed the commercial insole (EVA) and matched a therapeutic insole (PUR) overall, with superior regional offloading at the heel.ConclusionThis study enhances diabetic foot care by combining efficiency of silicone molding with functionally personalized, modular design to achieve superior pressure redistribution. It establishes a paradigm of "modular personalization" for diabetic insoles, leveraging AI for patient-specific material selection within a standardized, biomechanically optimized geometry.ApplicationThe findings offer practical insights for clinicians and manufacturers seeking scalable, patient-specific solutions for preventing diabetic foot ulcers.