Smart Solution Using Digital Twin and IoT for Diabetic Retinopathy

Advancement in technologies like big data, Internet of Things (IoT) and cloud computing has led to the Digital Twin (DT) being prominently used from idea to practice in numerous industrial fields as a precision simulation model. Substantial simulation models have been suggested for Digital Twin in numerous fields in the past 12 years. In the domain of IoT and healthcare, researchers are keen to find smart automated IoT-based solutions for Healthcare systems. So far, numerous works highlight the problem of DT in healthcare systems through theoretical frameworks. However, application-based solutions and validations are hardly provided. In this paper, we propose the Digital Twin in smart healthcare systems for Diabetic Retinopathy and a framework for the same to enhance healthcare procedures and improve the health of patients. For the first time, we developed a Diabetic Retinopathy identifier and classifier Digital Twin to detect and diagnose damaged retina due to diabetes with the help of Deep Learning and IoT. We used EfficientNet for Transfer Learning leading to a good accuracy score being predicted. The results accumulated towards the end have proven that fusion of healthcare and Digital Twin might result in better processing of healthcare tactics by putting patients and healthcare experts in conjunction in a rational, extensive, and expandable medical ecosphere. Additionally, executing this Digital Twin offers the foundation for making the use of DL, IoT and AI with various human shape metrics for ceaseless tracking, anomaly detection as well as ideating treatments.