Ai-Powered Automated and Portable Device for Retinal Health Assessment

L. Sakthi Kaviya, R. Praveen Kumar, B. Santhosh and Dr. J. Sudhakar
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L. Sakthi Kaviya: UG Student, Department of Biomedical Engineering, Karpaga vinayaga college of Engineering and Tecnology, Chengalpattu, Tamil Nadu, India
R. Praveen Kumar: UG Student, Department of Biomedical Engineering, Karpaga vinayaga college of Engineering and Tecnology, Chengalpattu, Tamil Nadu, India
B. Santhosh: UG Student, Department of Biomedical Engineering, Karpaga vinayaga college of Engineering and Tecnology, Chengalpattu, Tamil Nadu, India
Dr. J. Sudhakar: UG Student, Department of Biomedical Engineering, Karpaga vinayaga college of Engineering and Tecnology, Chengalpattu, Tamil Nadu, India

International Journal of Research and Scientific Innovation, 2025, vol. 12, issue 5, 383-389

Abstract: In recent years, advancements in Artificial Intelligence (AI) and deep learning have opened up new possibilities for automated, accurate, and faster detection of eye diseases, particularly glaucoma. This paper presents a smart, low-cost, and portable solution using a 20D Ophthalmology Lens attached to a smartphone via a PVC (Polyvinyl Chloride) pipe adapter. The device is capable of capturing clear fundus images, which are then analysed using Convolutional Neural Networks (CNNs) and other deep learning models to detect early signs of retinal diseases.This article describes the method to early diagnosis and monitoring of glaucoma through non-invasive, smartphone-assisted fundus imaging. This project integrates a 20D lens with a smartphone camera to capture high-resolution central retinal as well as the peripheral retina up to the pars plana. These images are processed using advanced machine learning algorithms to detect signs of glaucoma, such as optic disc cupping and nerve fiber layer thinning. It is a cost-effective alternative to the fundus camera. Glaucoma is one of the major causes of irreversible blindness across the world, especially in countries like India where early detection is often missed due to limited access to specialised eye care. Traditional methods of diagnosing glaucoma rely heavily on manual evaluation by ophthalmologists, which can be both time-consuming and subjective

Date: 2025
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