Epiretinal membrane detection at the ophthalmologist level using deep learning of optical coherence tomography
Date
2020Author
Lo, Ying-Chih
Lin, Keng-Hung
Bair, Henry
Huey-Herng Sheu, Wayne
Chang, Chi-Sen
Shen, Ying-Cheng
Hung, Che-Lun
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Abstract
Purpose: Previous deep learning studies on optical coherence tomography (OCT) mainly focused on
diabetic retinopathy and age-related macular degeneration. We proposed a deep learning model that
can identify epiretinal membrane (ERM) in OCT with ophthalmologist-level performance. Design: Crosssectional study. Participants: A total of 3,618 central fovea cross section OCT images from 1,475 eyes of
964 patients. Methods: We retrospectively collected 7,652 OCT images from 1,197 patients. From these
images, 2,171 were normal and 1,447 were ERM OCT. A total of 3,141 OCT images was used as training
dataset and 477 images as testing dataset. DL algorithm was used to train the interpretation model.
Diagnostic results by four board-certifed non-retinal specialized ophthalmologists on the testing
dataset were compared with those generated by the DL model. Main Outcome Measures: We calculated
for the derived DL model the following characteristics: sensitivity, specifcity, F1 score and area under
curve (AUC) of the receiver operating characteristic (ROC) curve. These were calculated according
to the gold standard results which were parallel diagnoses of the retinal specialist. Performance of
the DL model was fnally compared with that of non-retinal specialized ophthalmologists. Results:
Regarding the diagnosis of ERM in OCT images, the trained DL model had the following characteristics
in performance: sensitivity: 98.7%, specifcity: 98.0%, and F1 score: 0.945. The accuracy on the training
dataset was 99.7% (95% CI: 99.4 - 99.9%), and for the testing dataset, diagnostic accuracy was 98.1%
(95% CI: 96.5 - 99.1%). AUC of the ROC curve was 0.999. The DL model slightly outperformed the
average non-retinal specialized ophthalmologists. Conclusions: An ophthalmologist-level DL model was
built here to accurately identify ERM in OCT images. The performance of the model was slightly better
than the average non-retinal specialized ophthalmologists. The derived model may play a role to assist
clinicians to promote the efciency and safety of healthcare in the future.
Palabras clave
COVID-19; Ophthalmologist; Optical coherenceLink to resource
https://doi.org/10.1038/s41598-020-65405-2Collections
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