Creating a retinal image database to develop an automated screening tool for diabetic retinopathy in India - Ramachandran Rajalakshmi; Thyparambil Aravindakshan PramodKumar; Ashis Kumar Dhara; Geetha Kumar; Naziya Gulnaaz; Shramana Dey; Sourav Basak; B Uma Shankar; Raka Goswami; Raja Mohammed; Suchetha Manikandan; Sushmita Mitra; et al. (Scientific Reports)
Abstract Diabetic retinopathy (DR), a prevalent microvascular complication of diabetes, is the fifth leading cause of blindness worldwide. Given the critical nature of the disease, it is paramount that individuals with diabetes undergo annual screening for early and timely detection of DR, facilitating prompt ophthalmic assessment and intervention. However, screening for DR, which involves assessing visual acuity and retinal examination through ophthalmoscopy or retinal photography, presents a significant global challenge due to the massive volume of individuals requiring annual reviews. To counter this challenge, there has been an increasing interest in the potential of artificial intelligence (AI) tools for automated diagnosis of DR. The AI tools primarily utilize deep learning (DL) techniques and are tailored to analyse extensive medical image data and provide diagnostic outputs, essentially streamline the DR screening process. However, the development of such AI tools requires access to a comprehensive retinal image database with a plethora of high-resolution fundus images from various cameras, covering all DR lesions. Additionally, the accurate training of these AI algorithms necessitates skilled professionals, such as optometrists or ophthalmologists, to provide reliable ground truths that ensure the precision of the diagnostic outputs. To address these prerequisites, we have initiated a study involving multiple institutions to establish a large-scale online 'Retinal Image Database’ in India, aiming to contribute significantly to DR research. This paper delineates the methodology employed for this significant undertaking, detailing the steps taken to create the large retinal image database, as well as the framework for developing a cost-effective, robust AI-based DR diagnostic tool. Our work is expected to mark a significant stride in DR detection and management, promising a more efficient and scalable solution for tackling this global health challenge.