AI-Powered Insulin Pens for Pediatric Diabetes: Advancements in Lipodystrophy Detection and Injection Site Recognition

Effective management of pediatric diabetes remains a clinical challenge, particularly due to the onset of lipodystrophy resulting from repeated insulin injections and inadequate rotation of injection sites. These complications adversely affect subcutaneous tissue integrity and insulin pharmacokinetics, ultimately compromising glycemic control. In this study, we introduce diPen, a novel smart case designed to integrate with commercial insulin pens and equipped with a dual-sensor system: an optical module for non-invasive lipodystrophy detection and an inertial measurement unit for monitoring injection-site rotation. To evaluate the feasibility of the proposed approach, a clinical study was conducted involving a pediatric cohort. The system employs a personalized machine learning pipeline, leveraging a leave-one-acquisition-out validation strategy to replicate realworld deployment scenarios, wherein newly acquired data from the same subject are evaluated without prior exposure during training. The system demonstrated promising performance in both detection and classification tasks, suggesting that di-Pen may represent a viable tool for enhancing insulin therapy through personalized, data-driven injection guidance and tissue health monitoring.