Comparison of Autonomous AS-OCT Deep Learning Algorithm and Clinical Dry Eye Tests in Diagnosis of Dry Eye Disease
Collin Chase,1 Amr Elsawy,2 Taher Eleiwa,3 Eyup Ozcan,4 Mohamed Tolba,2 Mohamed Abou Shousha2 1Morsani College of Medicine, University of South Florida, Tampa, FL, USA; 2Cornea Department, Bascom Palmer Eye Institute, Miller School of Medicine, University of Miami, Miami, FL, USA; 3Department of Oph...
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| Formato: | article |
| Lenguaje: | EN |
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Dove Medical Press
2021
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| Acceso en línea: | https://doaj.org/article/cf9b8e9baa154d3f9f79a9b37139f575 |
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| Sumario: | Collin Chase,1 Amr Elsawy,2 Taher Eleiwa,3 Eyup Ozcan,4 Mohamed Tolba,2 Mohamed Abou Shousha2 1Morsani College of Medicine, University of South Florida, Tampa, FL, USA; 2Cornea Department, Bascom Palmer Eye Institute, Miller School of Medicine, University of Miami, Miami, FL, USA; 3Department of Ophthalmology, Faculty of Medicine, Benha University, Benha, Egypt; 4Department of Ophthalmology, Net Eye Medical Center, Gaziantep, TurkeyCorrespondence: Mohamed Abou ShoushaCornea Department, Bascom Palmer Eye Institute, Miller School of Medicine, University of Miami, 900 NW 17th Street, Miami, FL, 33136, USATel +1 305-326-6000Email Mshousha@med.miami.eduObjective: To evaluate a deep learning-based method to autonomously detect dry eye disease (DED) in anterior segment optical coherence tomography (AS-OCT) images compared to common clinical dry eye tests.Methods: In this study, 27,180 AS-OCT images were prospectively collected from 151 eyes of 91 patients. Images were used to train and test the deep learning model. Masked cornea specialist ophthalmologist diagnoses were used as the gold standard. Clinical dry eye tests were performed on patients in the DED group to compare the results of the model. The dry eye tests performed were tear break-up time (TBUT), Schirmer’s test, corneal staining, conjunctival staining, and Ocular Surface Disease Index (OSDI).Results: Our deep learning model achieved an accuracy of 84.62%, sensitivity of 86.36%, and specificity of 82.35% in the diagnosis of DED. The positive likelihood ratio was 4.89, and the negative likelihood ratio was 0.17. The mean DED probability score was 0.81 ± 0.23 in the DED group and 0.20 ± 0.27 in the healthy group (P < 0.01). The deep learning model accuracy in the diagnosis of DED was significantly better than that of corneal staining, conjunctival staining, and Schirmer’s test (P < 0.05). There was no significant difference between the deep learning diagnostic accuracy and that of the OSDI and TBUT.Conclusion: Based on preliminary results, reliable autonomous diagnosis of DED with our deep learning model was achieved, when compared with standard dry eye clinical tests that correlated significantly more or similarly to diagnoses made by cornea specialist ophthalmologists.Keywords: dry eye disease, artificial intelligence, optical coherence tomography |
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