A tool for automated diabetic retinopathy pre-screening based on retinal image computer analysis

• Published in: Computers in biology and medicine 2017-09, Vol.88, p.100-109
• Main Authors: Gegundez-Arias, Manuel E., Marin, Diego, Ponte, Beatriz, Alvarez, Fatima, Garrido, Javier, Ortega, Carlos, Vasallo, Manuel J., Bravo, Jose M.
• Format: Article
• Language: English
• Subjects: Algorithms; Automated screening; Automation; Bioinformatics; Classification; Computer applications; Computer-aided diagnosis; Diabetes; Diabetes mellitus; Diabetic retinopathy; Diabetic Retinopathy - diagnostic imaging; Diagnosis; Diagnostic Techniques, Ophthalmological; Digital imaging; Early detection system; Early Diagnosis; Edema; Eye; Fundus Oculi; Humans; Image classification; Image detection; Image Interpretation, Computer-Assisted - methods; Image processing; Medical diagnosis; Methods; Neural networks; Ophthalmology; Patients; Quality; Regional development; Retina; Retina - diagnostic imaging; Retinal image processing; Retinal Vessels - diagnostic imaging; Retinography; Retinopathy; Screening; Sensitivity
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2017.07.007

This paper presents a methodology and first results of an automatic detection system of first signs of Diabetic Retinopathy (DR) in fundus images, developed for the Health Ministry of the Andalusian Regional Government (Spain). The system detects the presence of microaneurysms and haemorrhages in retinography by means of techniques of digital image processing and supervised classification. Evaluation was conducted on 1058 images of 529 diabetic patients at risk of presenting evidence of DR (an image of each eye is provided). To this end, a ground-truth diagnosis was created based on gradations performed by 3 independent ophthalmology specialists. The comparison between the diagnosis provided by the system and the reference clinical diagnosis shows that the system can work at a level of sensitivity that is similar to that achieved by experts (0.9380 sensitivity per patient against 0.9416 sensitivity of several specialists). False negatives have proven to be mild cases. Moreover, while the specificity of the system is significantly lower than that of human graders (0.5098), it is high enough to screen more than half of the patients unaffected by the disease. Results are promising in integrating this system in DR screening programmes. At an early stage, the system could act as a pre-screening system, by screening healthy patients (with no obvious signs of DR) and identifying only those presenting signs of the disease.