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- Title
Model-to-Data Approach for Deep Learning in Optical Coherence Tomography Intraretinal Fluid Segmentation.
- Authors
Mehta, Nihaal; Lee, Cecilia S.; Mendonça, Luísa S. M.; Raza, Khadija; Braun, Phillip X.; Duker, Jay S.; Waheed, Nadia K.; Lee, Aaron Y.
- Abstract
<bold>Importance: </bold>Amid an explosion of interest in deep learning in medicine, including within ophthalmology, concerns regarding data privacy, security, and sharing are of increasing importance. A model-to-data approach, in which the model itself is transferred rather than data, can circumvent many of these challenges but has not been previously demonstrated in ophthalmology.<bold>Objective: </bold>To determine whether a model-to-data deep learning approach (ie, validation of the algorithm without any data transfer) can be applied in ophthalmology.<bold>Design, Setting, and Participants: </bold>This single-center cross-sectional study included patients with active exudative age-related macular degeneration undergoing optical coherence tomography (OCT) at the New England Eye Center from August 1, 2018, to February 28, 2019. Data were primarily analyzed from March 1 to June 20, 2019.<bold>Main Outcomes and Measures: </bold>Training of the deep learning model, using a model-to-data approach, in recognizing intraretinal fluid (IRF) on OCT B-scans.<bold>Results: </bold>The model was trained (learning curve Dice coefficient, >80%) using 400 OCT B-scans from 128 participants (69 female [54%] and 59 male [46%]; mean [SD] age, 77.5 [9.1] years). In comparing the model with manual human grading of IRF pockets, no statistically significant difference in Dice coefficients or intersection over union scores was found (P > .05).<bold>Conclusions and Relevance: </bold>A model-to-data approach to deep learning applied in ophthalmology avoided many of the traditional hurdles in large-scale deep learning, including data sharing, security, and privacy concerns. Although the clinical relevance of these results is limited at this time, this proof-of-concept study suggests that such a paradigm should be further examined in larger-scale, multicenter deep learning studies.
- Subjects
RESEARCH; RETINAL degeneration; RESEARCH evaluation; CROSS-sectional method; RESEARCH methodology; EVALUATION research; MEDICAL cooperation; COMPARATIVE studies; OPTICAL coherence tomography; EXUDATES &; transudates; RESEARCH funding; ALGORITHMS
- Publication
JAMA Ophthalmology, 2020, Vol 138, Issue 10, p1017
- ISSN
2168-6165
- Publication type
journal article
- DOI
10.1001/jamaophthalmol.2020.2769