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Accuracy of a Machine-Learning Algorithm for Detecting and Classifying Choroidal Neovascularization on Spectral-Domain Optical Coherence Tomography
| Content Provider | MDPI |
|---|---|
| Author | Maunz, Andreas Benmansour, Fethallah Li, Yvonna Albrecht, Thomas Zhang, Yan-Ping Arcadu, Filippo Zheng, Yalin Madhusudhan, Savita Sahni, Jayashree |
| Copyright Year | 2021 |
| Description | Background: 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. Methods: 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. Results: 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. Conclusions: Our ML model was able to detect CNV presence and CNV subtypes on SD-OCT images with high accuracy in patients with neovascular AMD. |
| Starting Page | 524 |
| e-ISSN | 20754426 |
| DOI | 10.3390/jpm11060524 |
| Journal | Journal of Personalized Medicine |
| Issue Number | 6 |
| Volume Number | 11 |
| Language | English |
| Publisher | MDPI |
| Publisher Date | 2021-06-08 |
| Access Restriction | Open |
| Subject Keyword | Journal of Personalized Medicine Ophthalmology Age-related Macular Degeneration Choroidal Neovascularization Classification Machine Learning Optical Coherence Tomography |
| Content Type | Text |
| Resource Type | Article |
