Purchase this article with an account.
Xinle Liu, Preeti Singh, Paisan Ruamviboonsuk, Peranut Chotcomwongse, Reena Chopra, Pearse A. Keane, Josef Christian Huemer, Jorge Cuadros, Avinash V. Varadarajan, Naama Hammel, Siva Balasubramanian, Tayyeba K. Ali, Pinal Bavishi; A Deep Learning Model to Detect Center-Involved Diabetic Macular Edema from Fundus Images. Invest. Ophthalmol. Vis. Sci. 2020;61(7):1619.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
To develop a deep learning (DL) model to detect center-involved diabetic macular edema (ci-DME) from color fundus photographs (CFPs), assess its performance on multiple datasets and compare it with human graders, against a ground truth obtained from optical coherence tomography (OCT) scans.
We developed a DL model to detect ci-DME using a retrospective development dataset of 7341 CFPs from 4682 patients as input and the corresponding ci-DME grades based on retinal thicknesses from OCT macular scans as labels. Three validation datasets from three different countries were assessed by human graders for the presence of ci-DME, using the proxy criteria of presence of hard exudates within one optic disc diameter of the center of the macula (Thailand: 1033 CFPs, graded by 3 retina specialists; US: 1976 CFPs, graded by 1 certified grader; and UK: 1602 CFPs, graded by 1 certified grader). ci-DME from OCT was defined as eyes with ≥ 250 μm center point thickness in the Thailand dataset and ≥ 300 μm central subfield thickness in the US and UK datasets. We evaluated the model on these datasets and compared its performance to human graders, using ci-DME ground truth derived from OCT.
As shown in Figure 1, on Thailand, US, and UK validation sets, the model achieved area under the curves (AUCs) of 0.880 (95% confidence interval (CI): [0.86,0.90]), 0.875 (95% CI: [0.83,0.92]) and 0.819 (95% CI: [0.78,0.85]) respectively. When matched with the sensitivities of approximately 85%, 40% and 93% with corresponding specificities of 45%, 94% and 32% of human graders, the model had specificities of 73%, 97% and 44% respectively (p < 0.01 for each comparison).
Our DL model achieved good performance in detecting ci-DME from CFPs in three validation sets from three different countries, with higher specificities when matching the sensitivities of human graders. Such a model could potentially be of clinical value in reducing both false positive referrals and false negative diagnoses for ci-DME in DR screening programs worldwide.
This is a 2020 ARVO Annual Meeting abstract.
Figure 1: Receiver operating characteristics (ROC) curves for the ci-DME model evaluated on Thailand, US and UK validation datasets, together with the AUCs and graders' performance.
This PDF is available to Subscribers Only