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

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 0·89. 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.

Keywords: Covid-19; artificial intelligence; diabetic retinopathy; mobile healthcare; point-of-care; screening; telemedicine.

Figure 1.

Example of heatmap visualization using gradient method. (a) Color fundus photograph depicting hard exudates and microaneurysms in the macular region, suggesting the possibility of diabetic macular oedema. (b) Overlay with the GradCam heatmap visualization can aid in making a diagnosis as the modifications are flagged in a color scale, from blue (low importance) to red (high importance).

Figure 2.

Waterfall diagram. Standards for Reporting of Diagnostic Accuracy Studies (STARD) diagram for the algorithm mtmDR output. mtmDR, more than mild diabetic retinopathy.

Figure 3.

Receiver operating characteristic (ROC) curve of the artificial intelligence device for detection of more than mild diabetic retinopathy (mtmDR). Area under the curve = 0.89.

Figure 4.

Example of a false positive case. (a) Color fundus photograph depicting inferior pigmentary change. (b) Overlay with the GradCam heatmap; the pigmentary alteration is flagged by the algorithm, leading to a false positive output.