Diabetic Retinopathy Screening Using Artificial Intelligence and Handheld Smartphone-Based Retinal Camera

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Malerbi, Fernando Korn ; Andrade, Rafael Ernane ; Morales, Paulo Henrique ; Stuchi, Jose Augusto ; Lencione, Diego ; de Paulo, Jean Vitor ; Carvalho, Mayana Pereira ; Nunes, Fabricia Silva ; Rocha, Roseanne Montargil ; Ferraz, Daniel A. ; Belfort, Rubens, Jr.

• Document type: Journal article
• Source: JOURNAL OF DIABETES SCIENCE AND TECHNOLOGY; v. 16, n. 3, p. 8-pg., 2022-05-01.

Abstract

Background: Portable retinal cameras and deep learning (DL) algorithms are novel tools adopted by diabetic retinopathy (DR) screening programs. Our objective is to evaluate the diagnostic accuracy of a DL algorithm and the performance of portable handheld retinal cameras in the detection of DR in a large and heterogenous type 2 diabetes population in a real-world, high burden setting. Method: Participants underwent fundus photographs of both eyes with a portable retinal camera (Phelcom Eyer). Classification of DR was performed by human reading and a DL algorithm (PhelcomNet), consisting of a convolutional neural network trained on a dataset of fundus images captured exclusively with the portable device; both methods were compared. We calculated the area under the curve (AUC), sensitivity, and specificity for more than mild DR. Results: A total of 824 individuals with type 2 diabetes were enrolled at Itabuna Diabetes Campaign, a subset of 679 (82.4%) of whom could be fully assessed. The algorithm sensitivity/specificity was 97.8 % (95% CI 96.7-98.9)/61.4 % (95% CI 57.7-65.1); AUC was 089. All false negative cases were classified as moderate non-proliferative diabetic retinopathy (NPDR) by human grading. Conclusions: The DL algorithm reached a good diagnostic accuracy for more than mild DR in a real-world, high burden setting. The performance of the handheld portable retinal camera was adequate, with over 80% of individuals presenting with images of sufficient quality. Portable devices and artificial intelligence tools may increase coverage of DR screening programs. (AU)

• FAPESP's process: 18/23331-6 - Wearable device for eye diseases diagnosis
• Grantee: Jean Vitor de Paulo
• Support Opportunities: Scholarships in Brazil - Technical Training Program - Technical Training
• FAPESP's process: 17/16014-1 - Wearable device for eye diseases diagnosis
• Grantee: José Augusto Stuchi
• Support Opportunities: Research Grants - Innovative Research in Small Business - PIPE