Abstract:
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.
