Open AccessAssessing the Clinical Utility of Expanded Macular OCTs Using Machine Learning
Author(s) -
Alex T.L. Lin,
Cecilia S. Lee,
Marian Blazes,
Aaron Lee,
Michael B. Gorin
Publication year - 2021
Publication title -
translational vision science and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.508
H-Index - 21
ISSN - 2164-2591
DOI - 10.1167/tvst.10.6.32
Subject(s) - optical coherence tomography , macular degeneration , medicine , ophthalmology , receiver operating characteristic , macular edema , diabetic retinopathy , foveal , optometry , retinal , glaucoma , diabetic macular edema , diabetes mellitus , endocrinology
Purpose Optical coherence tomography (OCT) is widely used in the management of retinal pathologies, including age-related macular degeneration (AMD), diabetic macular edema (DME), and primary open-angle glaucoma (POAG). We used machine learning techniques to understand diagnostic performance gains from expanding macular OCT B-scans compared with foveal-only OCT B-scans for these conditions. Methods Electronic medical records were extracted to obtain 61 B-scans per eye from patients with AMD, diabetic retinopathy, or POAG. We constructed deep neural networks and random forest ensembles and generated area under the receiver operating characteristic (AUROC) and area under the precision recall (AUPR) curves. Results After extracting 630,000 OCT images, we achieved improved AUROC and AUPR curves when comparing the central image (one B-scan) to all images (61 B-scans). The AUROC and AUPR points of diminishing return for diagnostic accuracy for macular OCT coverage were found to be within 2.75 to 4.00 mm (14–19 B-scans), 4.25 to 4.50 mm (20–21 B-scans), and 4.50 to 6.25 mm (21–28 B-scans) for AMD, DME, and POAG, respectively. All models with >0.25 mm of coverage had statistically significantly improved AUROC/AUPR curves for all diseases ( P < 0.05). Conclusions Systematically expanded macular coverage models demonstrated significant differences in total macular coverage required for improved diagnostic accuracy, with the largest macular area being relevant in POAG followed by DME and then AMD. These findings support our hypothesis that the extent of macular coverage by OCT imaging in the clinical setting, for any of the three major disorders, has a measurable impact on the functionality of artificial intelligence decision support. Translational Relevance We used machine learning techniques to improve OCT imaging standards for common retinal disease diagnoses.