Abstract
Purpose :
Amidst intense interest in deep learning in medicine, 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 tested in ophthalmology. The objective of our study was to determine whether a model-to-data deep learning approach (i.e. validation of the algorithm without any data transfer) can be successfully applied for the first time to deep learning in ophthalmology.
Methods :
This is a cross sectional study in which a deep learning algorithm model developed at the University of Washington was trained using a model to data approach, to identify intraretinal fluid on OCT scans at the New England Eye Center (NEEC). Patients with active exudative age-related macular degeneration(AMD) imaged at the NEEC between August 2018 and February 2019 were enrolled. OCT images were extracted and labeled. This dataset was then randomly divided into a training dataset (400 eyes) and a testing dataset (70 eyes). The deep learning model was trained in recognizing intra-retinal fluid (IRF) on OCT B-scans. It was then tested using the testing data set and subsequently compared to human grading of intraretinal fluid
Results :
The model was successfully trained (learning curve Dice coefficient > 80%) using 400 OCT B-scans. In comparing the model to manual human grading of IRF pockets, with the exception of one comparison, there was no statistically significant difference in Dice or intersection over union scores (P ≥ 0.05).
Conclusions :
A model-to-data approach to deep learning was successfully applied for the first time in ophthalmology. Without any data transfer, we successfully trained, validated and tested a previously reported DL model that quantifies the area of intraretinal fluid in OCT. This proof-of-concept suggests that such a paradigm should be further examined in larger-scale, multi-center deep learning studies.
This is a 2020 Imaging in the Eye Conference abstract.