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. 2022 Jul 20:3:124-133.
doi: 10.1109/OJEMB.2022.3192780. eCollection 2022.

Artificial Intelligence-Based Teleopthalmology Application for Diagnosis of Diabetics Retinopathy

S Ghouali  1 , E M Onyema  2   2   3 , M S Guellil  4 , M A Wajid  5 , O Clare  2 , W Cherifi  6 , M Feham  7
Affiliations

Artificial Intelligence-Based Teleopthalmology Application for Diagnosis of Diabetics Retinopathy

S Ghouali et al. IEEE Open J Eng Med Biol. .

Abstract

Diabetic Retinopathy (DR) is one of the leading causes of blindness for people who have diabetes in the world. However, early detection of this disease can essentially decrease its effects on the patient. The recent breakthroughs in technologies, including the use of smart health systems based on Artificial intelligence, IoT and Blockchain are trying to improve the early diagnosis and treatment of diabetic retinopathy. In this study, we presented an AI-based smart teleopthalmology application for diagnosis of diabetic retinopathy. The app has the ability to facilitate the analyses of eye fundus images via deep learning from the Kaggle database using Tensor Flow mathematical library. The app would be useful in promoting mHealth and timely treatment of diabetic retinopathy by clinicians. With the AI-based application presented in this paper, patients can easily get supports and physicians and researchers can also mine or predict data on diabetic retinopathy and reports generated could assist doctors to determine the level of severity of the disease among the people.

Keywords: Deep learning; IoT; artificial intelligence; diabetic retinopathy; eye fundus images; smart health; tensorflow.

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Figures

Fig. 1.
Fig. 1.
Different DR levels.
Fig. 2.
Fig. 2.
Effect of diabetic retinopathy: (a) Normal vision, (b) Vision with DR. (National Institute of Health).
Fig. 3.
Fig. 3.
Matrices convolution.
Fig. 4.
Fig. 4.
(a) Right eye fundus; (b) Left eye fundus.
Fig. 5.
Fig. 5.
Authentication.
Fig. 6.
Fig. 6.
Menu screen.
Fig. 7.
Fig. 7.
Retinopathy diabetic information.
Fig. 8.
Fig. 8.
Patients lists.
Fig. 9.
Fig. 9.
Doctors lists.
Fig. 10.
Fig. 10.
RD database.
Fig. 11.
Fig. 11.
Choice of picture to be examined.
Fig. 12.
Fig. 12.
Examination Results.
See this image and copyright information in PMC

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