Exploiting AI to make insulin pens smart: injection site recognition and lipodystrophy detection

Abstract

Nowadays diabetes still remains one of the leading causes of death worldwide and it has serious consequences if not properly treated. The advent of hybrid closed-loop systems, connection with consumer electronics and cloud-based data systems have hastened the advancement of diabetes technology. In the wake of this progress, we exploit information technology to make insulin pens smart so as to promote adherence to injection therapy and improve the socio-economic impact for the patient. In this respect, this work focuses on two main open issues, namely injection site rotation and lipodystrophies detection while the patient is taking the insulin. The first one is addressed collecting data with IMU sensor which are processed by a machine learning classifier to detect the injection site. The second one is tackled through a sensor equipped with two leds: features computed from such signals fed a one-class Support Vector Machine trained to recognise healthy tissue, so that samples different from those in the training set can be considered as lipodystrophies. The results obtained for the injection site recognition show an average accuracy larger than 0.957, whilst in the case of lipodystrophies detection we reach an accuracy greater than 0.95 using the IR led.