A Deep Learning Method for Automatic Identification of Drusen and Macular Hole from Optical Coherence Tomography

Abstract

Deep Learning methods have become dominant in various fields of medical imaging, including ophthalmology. In this preliminary study, we investigated a method based on Convolutional Neural Network for the identification of drusen and macular hole from Optical Coherence Tomography scans with the aim to assist ophthalmologists in diagnosing and assessing retinal diseases.

Self-supervised contrastive learning improves machine learning discrimination of full thickness macular holes from epiretinal membranes in retinal OCT scans

“…Owing to the paucity of labeled FTMH data, the majority of these models are pre-trained on ImageNet, and subsequently fine-tuned on a small amount of labeled FTMH OCT images. This transfer learning scheme was adopted in developing the classification model by Pace et al [16], which achieves 95% accuracy in distinguishing between normal, Drusen, and FTMH images. Carvalho et al [17], demonstrated an accuracy of 90.6% for FTMH identification, although modeling specifics are not provided.…”

“…The last several years have seen an explosion of deep learning models applied to ophthalmic clinical technologies including OCT and fundus imaging. These applications may be divided into the broad areas of classification/diagnosis [8][9][10][11][12][13][14][15][16][17][18], segmentation [19][20][21][22][23][24][25][26], image quality [27], and demographics prediction [28]. The current ophthalmic deep learning models focus primarily on diabetic retinopathy, age-related macular degeneration, retinopathy of prematurity, and glaucoma [29][30][31].…”

“…Deep learning based OCT image analysis for FTMH has also received attention lately, with models for classification [16,17], segmentation [23][24][25][26], and prognosis of success for FTMH corrective surgery [18,32,33]. A review is also available [34].…”