Accuracy of a Machine-Learning Algorithm for Detecting and Classifying Choroidal Neovascularization on Spectral-Domain Optical Coherence Tomography

Maunz, Andreas, Benmansour, Fethallah, Li, Yvonna, Albrecht, Thomas, Zhang, Yan-Ping, Arcadu, Filippo, Zheng, Yalin ORCID: 0000-0002-7873-0922, Madhusudhan, Savita and Sahni, Jayashree (2021) Accuracy of a Machine-Learning Algorithm for Detecting and Classifying Choroidal Neovascularization on Spectral-Domain Optical Coherence Tomography. JOURNAL OF PERSONALIZED MEDICINE, 11 (6). 524-.

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Official URL: http://dx.doi.org/10.3390/jpm11060524

Abstract

<h4>Background</h4>To evaluate the performance of a machine-learning (ML) algorithm to detect and classify choroidal neovascularization (CNV), secondary to age-related macular degeneration (AMD) on spectral-domain optical coherence tomography (SD-OCT) images.<h4>Methods</h4>Baseline fluorescein angiography (FA) and SD-OCT images from 1037 treatment-naive study eyes and 531 fellow eyes, without advanced AMD from the phase 3 HARBOR trial (NCT00891735), were used to develop, train, and cross-validate an ML pipeline combining deep-learning-based segmentation of SD-OCT B-scans and CNV classification, based on features derived from the segmentations, in a five-fold setting. FA classification of the CNV phenotypes from HARBOR was used for generating the ground truth for model development. SD-OCT scans from the phase 2 AVENUE trial (NCT02484690) were used to externally validate the ML model.<h4>Results</h4>The ML algorithm discriminated CNV absence from CNV presence, with a very high accuracy (area under the receiver operating characteristic [AUROC] = 0.99), and classified occult versus predominantly classic CNV types, per FA assessment, with a high accuracy (AUROC = 0.91) on HARBOR SD-OCT images. Minimally classic CNV was discriminated with significantly lower performance. Occult and predominantly classic CNV types could be discriminated with AUROC = 0.88 on baseline SD-OCT images of 165 study eyes, with CNV from AVENUE.<h4>Conclusions</h4>Our ML model was able to detect CNV presence and CNV subtypes on SD-OCT images with high accuracy in patients with neovascular AMD.

Item Type:	Article
Uncontrolled Keywords:	age-related macular degeneration, choroidal neovascularization, classification, machine learning, optical coherence tomography
Divisions:	Faculty of Health and Life Sciences Faculty of Health and Life Sciences > Institute of Life Courses and Medical Sciences
Depositing User:	Symplectic Admin
Date Deposited:	02 Aug 2021 07:37
Last Modified:	01 Feb 2024 13:54
DOI:	10.3390/jpm11060524
Open Access URL:	https://www.mdpi.com/2075-4426/11/6/524
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3132031