Artificial Intelligence for the Detection of Diabetic Retinopathy in Primary Care: Protocol for Algorithm Development

Josep Vidal-Alaball; Dídac Royo Fibla; Miguel A Zapata; Francesc X Marin-Gomez; Oscar Solans Fernandez

doi:10.2196/12539

Artificial Intelligence for the Detection of Diabetic Retinopathy in Primary Care: Protocol for Algorithm Development

JMIR Res Protoc. 2019 Feb 1;8(2):e12539. doi: 10.2196/12539.

Authors

Josep Vidal-Alaball^{1

2}, Dídac Royo Fibla³, Miguel A Zapata³, Francesc X Marin-Gomez¹, Oscar Solans Fernandez⁴

Affiliations

¹ Health Promotion in Rural Areas Research Group, Gerència Territorial de la Catalunya Central, Catalan Health Institute, Sant Fruitós de Bages, Spain.
² Unitat de Suport a la Recerca de la Catalunya Central, Institut Universitari d'Investigació en Atenció Primària Jordi Gol, Sant Fruitós de Bages, Spain.
³ OPTretina, Sant Cugat del Vallès, Spain.
⁴ eHealth Directorate, Department of Health, Government of Catalonia, Barcelona, Spain.

PMID: 30707105
PMCID: PMC6376335
DOI: 10.2196/12539

Abstract

Background: Diabetic retinopathy (DR) is one of the most important causes of blindness worldwide, especially in developed countries. In diabetic patients, periodic examination of the back of the eye using a nonmydriatic camera has been widely demonstrated to be an effective system to control and prevent the onset of DR. Convolutional neural networks have been used to detect DR, achieving very high sensitivities and specificities.

Objective: The objective of this is paper was to develop an artificial intelligence (AI) algorithm for the detection of signs of DR in diabetic patients and to scientifically validate the algorithm to be used as a screening tool in primary care.

Methods: Under this project, 2 studies will be conducted in a concomitant way: (1) Development of an algorithm with AI to detect signs of DR in patients with diabetes and (2) A prospective study comparing the diagnostic capacity of the AI algorithm with respect to the actual system of family physicians evaluating the images. The standard reference to compare with will be a blinded double reading conducted by retina specialists. For the development of the AI algorithm, different iterations and workouts will be performed on the same set of data. Before starting each new workout, the strategy of dividing the set date into 2 groups will be used randomly. A group with 80% of the images will be used during the training (training dataset), and the remaining 20% images will be used to validate the results (validation dataset) of each cycle (epoch). During the prospective study, true-positive, true-negative, false-positive, and false-negative values will be calculated again. From here, we will obtain the resulting confusion matrix and other indicators to measure the performance of the algorithm.

Results: Cession of the images began at the end of 2018. The development of the AI algorithm is calculated to last about 3 to 4 months. Inclusion of patients in the cohort will start in early 2019 and is expected to last 3 to 4 months. Preliminary results are expected to be published by the end of 2019.

Conclusions: The study will allow the development of an algorithm based on AI that can demonstrate an equal or superior performance, and that constitutes a complement or an alternative, to the current screening of DR in diabetic patients.

International registered report identifier (irrid): PRR1-10.2196/12539.

Keywords: artificial intelligence; computer assisted diagnosis; diabetes mellitus; diabetic retinopathy; fundus oculi; neural network computer.

©Josep Vidal-Alaball, Dídac Royo Fibla, Miguel A Zapata, Francesc X Marin-Gomez, Oscar Solans Fernandez. Originally published in JMIR Research Protocols (http://www.researchprotocols.org), 01.02.2019.