Macular edema degeneration classification on OCT and fundus images with portable platform based on artificial intelligence methods

Loredana Buzura; Monica Loredana Budileanu; Radu Papara; Horea Demea; Ramona Galatus

doi:10.1117/12.2617520

27 May 2022 Macular edema degeneration classification on OCT and fundus images with portable platform based on artificial intelligence methods

Loredana Buzura, Monica Loredana Budileanu, Radu Papara, Horea Demea, Ramona Galatus

Author Affiliations +

Proceedings Volume 12144, Biomedical Spectroscopy, Microscopy, and Imaging II; 121440L (2022) https://doi.org/10.1117/12.2617520
Event: SPIE Photonics Europe, 2022, Strasbourg, France

Conference Poster

Abstract

Age-related macular degeneration (AMD) is the leading cause of permanent vision loss and visual impairment in people over 60. Early detection of the disease is essential to prevent the evolution of the disease into an advanced stage. An eye care specialist has to perform a dilated eye exam, fundoscopy, a visual acuity test, and fundus photography to determine if a patient has macular degeneration and the stage of the disease. Most of the equipment used nowadays in eye care clinics is equipped in one system with both fundus camera and OCT technology that provides more comprehensive clinical evaluations. In most countries the healthcare system suffers from a low doctor to patient ratio; due to it, diagnosis can become time-consuming and error-prone. To minimize this downfall, a computer-aided diagnosis (CAD) strategy is proposed using machine learning techniques to predict the presence of age-related macular degeneration using both OCT and fundus images. The computer-aided diagnosis (CAD) is using a portable device, Jetson TX2 board, a powerful AI computer device, to predict the presence of an abnormality in the retina. A dataset composed of three categories: normal retina, dry AMD, and wet AMD from both OCT and fundus images have been used to evaluate the performance of different neuronal networks. Cost reduction and system portability are implemented with the proposed system for point of care in ophthalmology applications.

Conference Presentation

Citation Download Citation

Loredana Buzura, Monica Loredana Budileanu, Radu Papara, Horea Demea, and Ramona Galatus "Macular edema degeneration classification on OCT and fundus images with portable platform based on artificial intelligence methods", Proc. SPIE 12144, Biomedical Spectroscopy, Microscopy, and Imaging II, 121440L (27 May 2022); https://doi.org/10.1117/12.2617520

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