Title:Diabetic Retinopathy Diagnosis based on Convolutional Neural Network in
the Russian Population: A Multicenter Prospective Study
Volume: 20
Issue: 8
Author(s): Daria Gognieva*, Madina Durzhinskaya, Irina Vorobyeva, Petr Chomakhidze, Alexander Suvorov, Natalia Kuznetsova, Alina Bektimirova, Baraah Al-Dwa, Magomed Abdullaev, Yusef Yusef, Vladislav Pavlov, Maria Budzinskaya, Dmitry Sychev, Larisa Moshetova and Philipp Kopylov
Affiliation:
- World-Class Research Center "Digital Biodesign and Personalized Healthcare", I.M. Sechenov First Moscow State
Medical University (Sechenov University), Moscow, Russia
Keywords:
Diabetes mellitus, diabetic retinopathy, diagnostics, screening, neural networks, machine learning.
Abstract:
Background: Diabetic retinopathy is the most common complication of diabetes
mellitus and is one of the leading causes of vision impairment globally, which is also relevant for
the Russian Federation.
Objective: To evaluate the diagnostic efficiency of a convolutional neural network trained for the
detection of diabetic retinopathy and estimation of its severity in fundus images of the Russian
population.
Methods: In this cross-sectional multicenter study, the training data set was obtained from an
open source and relabeled by a group of independent retina specialists; the sample size was
60,000 eyes. The test sample was recruited prospectively, 1186 fundus photographs of 593 patients
were collected. The reference standard was the result of independent grading of the diabetic
retinopathy stage by ophthalmologists.
Results: Sensitivity and specificity were 95.0% (95% CI; 90.8-96.4) and 96.8% (95% CI; 95.5-
99.0), respectively; positive predictive value – 98.8% (95% CI; 97.6-99.2); negative predictive
value – 87.1% (95% CI, 83.4-96.5); accuracy – 95.9% (95% CI; 93.3-97.1); Kappa score – 0.887
(95% CI; 0.839-0.946); F1score – 0.909 (95% CI; 0.870-0.957); area under the ROC-curve –
95.9% (95% CI; 93.3-97.1). There was no statistically significant difference in diagnostic accuracy
between the group with isolated diabetic retinopathy and those with hypertensive retinopathy
as a concomitant diagnosis.
Conclusion: The method for diagnosing DR presented in this article has shown its high accuracy,
which is consistent with the existing world analogues, however, this method should prove its
clinical efficiency in large multicenter multinational controlled randomized studies, in which the
reference diagnostic method would be unified and less subjective than an ophthalmologist.
