Authors:
Simão Felgueiras
;
João Costa
;
João Gonçalves
and
Filipe Soares
Affiliation:
Fraunhofer Portugal AICOS, Portugal
Keyword(s):
Diabetic Retinopathy, Retinal Image Acquisition, Automated Detection, Exudates, Microaneuryms, Decision Support System.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Distributed and Mobile Software Systems
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Knowledge-Based Systems
;
Mobile Technologies
;
Mobile Technologies for Healthcare Applications
;
Neural Rehabilitation
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition and Machine Learning
;
Software Engineering
;
Symbolic Systems
Abstract:
The large prevalence of diabetes in the global population is associated with an increasing number of Diabetic
Retinopathy cases. This disease is associated with a progressive risk of blindness, due to physiological changes
that affect the retina. Since most of the progression is asymptomatic and late stage damage is often irreversible,
there is a large incentive to implement effective methodologies that allow large scale screening of the diabetic
population. In this work, a research study of a mobile approach for the assessment of Diabetic Retinopathy
was conducted, by analyzing 80 patients already being followed for ophthalmological care. A smartphone-based
fundus imaging system was used to acquire images of the retina during the normal clinical workflow in
a Central Hospital in Portugal. Relevant images were automatically analyzed by a Decision Support System
(DSS) based on computer vision methods. The results were obtained for ground-truth correlation as well
as time impa
ct of this novel system. Our conclusions support that the DSS is highly sensitive in detecting
pathological information on images, after a dedicated quality image filtering, and the acquisition procedure
has minimal adverse impact in the clinical setting.
(More)