Russakoff, DB;
Lamin, A;
Oakley, JD;
Dubis, AM;
Sivaprasad, S;
(2019)
Deep Learning for Prediction of AMD Progression: A Pilot Study.
Investigative Ophthalmology & Visual Science
, 60
(2)
pp. 712-722.
10.1167/iovs.18-25325.
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Abstract
Purpose: To develop and assess a method for predicting the likelihood of converting from early/intermediate to advanced wet age-related macular degeneration (AMD) using optical coherence tomography (OCT) imaging and methods of deep learning. Methods: Seventy-one eyes of 71 patients with confirmed early/intermediate AMD with contralateral wet AMD were imaged with OCT three times over 2 years (baseline, year 1, year 2). These eyes were divided into two groups: eyes that had not converted to wet AMD (n = 40) at year 2 and those that had (n = 31). Two deep convolutional neural networks (CNN) were evaluated using 5-fold cross validation on the OCT data at baseline to attempt to predict which eyes would convert to advanced AMD at year 2: (1) VGG16, a popular CNN for image recognition was fine-tuned, and (2) a novel, simplified CNN architecture was trained from scratch. Preprocessing was added in the form of a segmentation-based normalization to reduce variance in the data and improve performance. Results: Our new architecture, AMDnet, with preprocessing, achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.89 at the B-scan level and 0.91 for volumes. Results for VGG16, an established CNN architecture, with preprocessing were 0.82 for B-scans/0.87 for volumes versus 0.66 for B-scans/0.69 for volumes without preprocessing. Conclusions: A CNN with layer segmentation-based preprocessing shows strong predictive power for the progression of early/intermediate AMD to advanced AMD. Use of the preprocessing was shown to improve performance regardless of the network architecture.
| Type: | Article |
|---|---|
| Title: | Deep Learning for Prediction of AMD Progression: A Pilot Study |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1167/iovs.18-25325 |
| Publisher version: | https://doi.org/10.1167/iovs.18-25325 |
| Language: | English |
| Additional information: | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. |
| Keywords: | Aged, Aged, 80 and over, Deep Learning, Diagnosis, Computer-Assisted, Disease Progression, Female, Follow-Up Studies, Humans, Machine Learning, Male, Middle Aged, Neural Networks, Computer, Pilot Projects, ROC Curve, Tomography, Optical Coherence, Wet Macular Degeneration |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Ophthalmology |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10095370 |
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