Deep Learning Applications to Classification and Detection of Age-Related Macular Degeneration on Optical Coherence Tomography Imaging: A Review

Neslihan Dilruba Koseoglu; Andrzej Grzybowski; T Y Alvin Liu

doi:10.1007/s40123-023-00775-0

Deep Learning Applications to Classification and Detection of Age-Related Macular Degeneration on Optical Coherence Tomography Imaging: A Review

Ophthalmol Ther. 2023 Oct;12(5):2347-2359. doi: 10.1007/s40123-023-00775-0. Epub 2023 Jul 26.

Authors

Neslihan Dilruba Koseoglu¹, Andrzej Grzybowski², T Y Alvin Liu³

Affiliations

¹ Wilmer Eye Institute, Johns Hopkins University, 600 N. Wolfe St., Maumenee 726, Baltimore, MD, 21287, USA.
² Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland.
³ Wilmer Eye Institute, Johns Hopkins University, 600 N. Wolfe St., Maumenee 726, Baltimore, MD, 21287, USA. tliu25@jhmi.edu.

Abstract

Age-related macular degeneration (AMD) is one of the leading causes of blindness in the elderly, more commonly in developed countries. Optical coherence tomography (OCT) is a non-invasive imaging device widely used for the diagnosis and management of AMD. Deep learning (DL) uses multilayered artificial neural networks (NN) for feature extraction, and is the cutting-edge technique for medical image analysis for diagnostic and prognostication purposes. Application of DL models to OCT image analysis has garnered significant interest in recent years. In this review, we aimed to summarize studies focusing on DL models used in classification and detection of AMD. Additionally, we provide a brief introduction to other DL applications in AMD, such as segmentation, prediction/prognostication, and models trained on multimodal imaging.

Keywords: Age-related macular degeneration; Classification; Deep learning; Optical coherence tomography; Prediction.

Publication types

Review