Advancing healthcare with artificial intelligence: diagnostic accuracy of machine learning algorithm in diagnosis of diabetic retinopathy in the Brazilian population
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13098_2024_Article_1447.pdf
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Author(s)
dos Reis, Mateus A.
Künas, Cristiano A.
da Silva Araújo, Thiago
Schneiders, Josiane
de Azevedo, Pietro B.
Nakayama, Luis F.
Rados, Dimitris R. V.
Umpierre, Roberto N.
Berwanger, Otávio
Lavinsky, Daniel
Date Issued
August 29, 2024
Journal
Diabetology & Metabolic Syndrome
Publisher
BioMed Central
Citation
dos Reis, M.A., Künas, C.A., da Silva Araújo, T. et al. Advancing healthcare with artificial intelligence: diagnostic accuracy of machine learning algorithm in diagnosis of diabetic retinopathy in the Brazilian population. Diabetol Metab Syndr 16, 209 (2024).
Version
Final published version
Abstract
In healthcare systems in general, access to diabetic retinopathy (DR) screening is limited. Artificial intelligence has the potential to increase care delivery. Therefore, we trained and evaluated the diagnostic accuracy of a machine learning algorithm for automated detection of DR.
Methods
We included color fundus photographs from individuals from 4 databases (primary and specialized care settings), excluding uninterpretable images. The datasets consist of images from Brazilian patients, which differs from previous work. This modification allows for a more tailored application of the model to Brazilian patients, ensuring that the nuances and characteristics of this specific population are adequately captured. The sample was fractionated in training (70%) and testing (30%) samples. A convolutional neural network was trained for image classification. The reference test was the combined decision from three ophthalmologists. The sensitivity, specificity, and area under the ROC curve of the algorithm for detecting referable DR (moderate non-proliferative DR; severe non-proliferative DR; proliferative DR and/or clinically significant macular edema) were estimated.
Results
A total of 15,816 images (4590 patients) were included. The overall prevalence of any degree of DR was 26.5%. Compared with human evaluators (manual method of diagnosing DR performed by an ophthalmologist), the deep learning algorithm achieved an area under the ROC curve of 0.98 (95% CI 0.97–0.98), with a specificity of 94.6% (95% CI 93.8–95.3) and a sensitivity of 93.5% (95% CI 92.2–94.9) at the point of greatest efficiency to detect referable DR.
Conclusions
A large database showed that this deep learning algorithm was accurate in detecting referable DR. This finding aids to universal healthcare systems like Brazil, optimizing screening processes and can serve as a tool for improving DR screening, making it more agile and expanding care access.
MIT Department
Harvard--MIT Program in Health Sciences and Technology. Laboratory for Computational Physiology
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.1186/s13098-024-01447-0
