A Smart Telemedicine System with Deep Learning to Manage Diabetic Retinopathy and Foot Ulcers

Artificial intelligence in combination with modern technologies including medical screening devices has the potential to deliver better management services to deal with chronic diseases with higher accuracy, efficiency, and satisfaction. With the recent evolution in digitized data acquisition, computer vision and machine learning, AI solutions are spreading into areas which were previously examined by well-trained clinicians. Early diagnosis of diabetic retinopathy (DR) and foot ulcers (DFU) occurrence through image analysis is in high demand as many individuals are left without any supervision due to the limited resources such as trained clinicians or suitable equipment especially, in rural areas. Furthermore, the existing system will become even more insufficient as the number of people with diabetes increases. In this research paper, we propose a prototype that involves an autonomous system called an Intelligent Diabetic Assistant (IDA), which decides the diagnosis and the treatment prioritization depending upon the observations appeared in the screen. The IDA consists of knowledge-based modules for severity level-based classification, clinical decision support and near real-time foot ulcer detection and boundary screening. We use the System Usability Scale (SUS) in terms of performance, learnability, and satisfaction to measure the usability of the IDA. The mean SUS score was 88.5, demonstrating good but not exceptional system usability. We perform our experiments with clinicians who have been involved in diabetic care.