In a comparison with manual human grading, investigators find machine learning model produced no statically significant differences in Dice coefficients.
Ophthalmology could be the next specialty to look into utilizing new deep learning technology to screen and diagnose patients with ocular disorders.
Nihaal Mehta, MD
A team, led by Nihaal Mehta, MD, New England Eye Center, Tufts Medical Center, determined whether a model-to-data deep learning approach without needing to transfer any data can be applied in ophthalmology.
In the single-center cross-sectional study, the investigators examined patients with active exudative age-related macular degeneration (AMD) who underwent optical coherence tomography (OCT) at the New England Eye Center between August 2018 and February 2019.
The investigators sought main outcomes of the training of the deep learning model, using a model-to-data approach, and recognizing intraretinal fluid on OCT B-scans.
The model-to-data approach was taken by freezing the model parameters from a prior study where a deep learning model was trained to segment IRF on Heidelberg Spectralis OCT B-scans.
The model parameters, retraining code, data preprocessing, and code for evaluation were packaged from the University of Washington and transferred using GitHub.
The model was training with a learning curve Dice coefficient greater than 80% using 400 OCT B-scans from 128 patients, 69 of which were female. The mean age of the patient population was 77.5 years old.
The scan protocol consisted of 512 A-scans per B-scan and 128 B-scans per volume, while the spectral-domain OCT system has an 840 nm central wavelength, as well as 68 000 A-scans per second, an A-scan depth of 2.0 mm, an axial resolution of 5 μm, and a transverse resolution of 15 μm.
The investigators compared the model with manual human grading of IRF pockets and found no statistically significant difference in Dice coefficients or intersection over union scores (P > 0.05).
“A model-to-data approach to deep learning was demonstrated for the first time, to our knowledge, in ophthalmology,” the authors wrote. “Using this approach, the performance of the deep learning model was trained and showed no statistically significant difference in quantifying the intraretinal fluid pockets in OCT compared with human manual grading. Such a paradigm has the potential to more easily facilitate large-scale and multicenter deep learning studies.”
While more deep learning tools are being used in virtually every medical specialty, there remains concerns regarding data privacy, security, and sharing. However, by using a model-to-data approach, the model itself can be transferred rather than the data, circumventing many of the existing challenges.
This technique has been tried in other specialties, but has not yet been attempted in ophthalmology. However, this technology could be transformative in the space due to ophthalmology’s dependence on outpatient ancillary testing.
Machine learning and deep learning have already been applied in ophthalmology in a variety of contexts and to a range of clinical conditions, ranging from diabetic retinopathy, age-related macular degeneration,9 and glaucomato, Stargardt disease, and post–small incision lenticule extraction surgical outcomes.
The study, “Model-to-Data Approach for Deep Learning in Optical Coherence Tomography Intraretinal Fluid Segmentation,” was published online in JAMA Ophthalmology.