Using deep learning to detect diabetic retinopathy on handheld non-mydriatic retinal images acquired by field workers in community settings
Joan M. Nunez do Rio, Paul Nderitu, Rajiv Raman, Ramachandran Rajalakshmi, Kim Ramasamy, Padmaja Kumari Rani, Sobha Sivaprasad, Christos Bergeles, for the SMART India Study Group, Rajiv Raman, Pramod Bhende, Janani Surya, Lingam Gopal, Radha Ramakrishnan, Rupak Roy, Supita Das, George J. Manayath, T. P. Vignesh, Giridhar Anantharaman, Mahesh Gopalakrishnan, Sundaram Natarajan, Radhika Krishnan, Sheena Liz Mani, Manisha Agarwal, Umesh Chandra Behera, Harsha Bhattacharjee, Manabjyoti Barman, Alok Sen, Moneesh Saxena, Asim Sil, Subhratanu Chakabarty, Thomas Cherian, Reesha Jitesh, Rushikesh Naigaonkar, Abishek Desai, Sucheta Kulkarni
Abstract
Diabetic retinopathy (DR) at risk of vision loss (referable DR) needs to be identified by retinal screening and referred to an ophthalmologist. Existing automated algorithms have mostly been developed from images acquired with high cost mydriatic retinal cameras and cannot be applied in the settings used in most low- and middle-income countries. In this prospective multicentre study, we developed a deep learning system (DLS) that detects referable DR from retinal images acquired using handheld non-mydriatic fundus camera by non-technical field workers in 20 sites across India. Macula-centred and optic-disc-centred images from 16,247 eyes (9778 participants) were used to train and cross-validate the DLS and risk factor based logistic regression models. The DLS achieved an AUROC of 0.99 (1000 times bootstrapped 95% CI 0.98-0.99) using two-field retinal images, with 93.86 (91.34-96.08) sensitivity and 96.00 (94.68-98.09) specificity at the Youden's index operational point. With single field inputs, the DLS reached AUROC of 0.98 (0.98-0.98) for the macula field and 0.96 (0.95-0.98) for the optic-disc field. Intergrader performance was 90.01 (88.95-91.01) sensitivity and 96.09 (95.72-96.42) specificity. The image based DLS outperformed all risk factor-based models. This DLS demonstrated a clinically acceptable performance for the identification of referable DR despite challenging image capture conditions.