Automated diagnosis and staging of Fuchs' endothelial cell corneal dystrophy using deep learning

Taher Eleiwa; Amr Elsawy; Eyüp Özcan; Mohamed Abou Shousha

doi:10.1186/s40662-020-00209-z

Automated diagnosis and staging of Fuchs' endothelial cell corneal dystrophy using deep learning

Eye Vis (Lond). 2020 Sep 1:7:44. doi: 10.1186/s40662-020-00209-z. eCollection 2020.

Authors

Taher Eleiwa^{1

2}, Amr Elsawy^{1

3}, Eyüp Özcan^{1

4}, Mohamed Abou Shousha^{1

3

5}

Affiliations

¹ Bascom Palmer Eye Institute, Miller School of Medicine, University of Miami, Miami, Florida 33136 USA.
² Department of Ophthalmology, Faculty of Medicine, Benha University, Benha, Egypt.
³ Electrical and Computer Engineering, University of Miami, Coral Gables, Florida USA.
⁴ Net Eye Medical Center, Gaziantep, Turkey.
⁵ Biomedical Engineering, University of Miami, Coral Gables, Florida USA.

Abstract

Background: To describe the diagnostic performance of a deep learning algorithm in discriminating early-stage Fuchs' endothelial corneal dystrophy (FECD) without clinically evident corneal edema from healthy and late-stage FECD eyes using high-definition optical coherence tomography (HD-OCT).

Methods: In this observational case-control study, 104 eyes (53 FECD eyes and 51 healthy controls) received HD-OCT imaging (Envisu R2210, Bioptigen, Buffalo Grove, IL, USA) using a 6 mm radial scan pattern centered on the corneal vertex. FECD was clinically categorized into early (without corneal edema) and late-stage (with corneal edema). A total of 18,720 anterior segment optical coherence tomography (AS-OCT) images (9180 healthy; 5400 early-stage FECD; 4140 late-stage FECD) of 104 eyes (81 patients) were used to develop and validate a deep learning classification network to differentiate early-stage FECD eyes from healthy eyes and those with clinical edema. Using 5-fold cross-validation on the dataset containing 11,340 OCT images (63 eyes), the network was trained with 80% of these images (3420 healthy; 3060 early-stage FECD; 2700 late-stage FECD), then tested with 20% (720 healthy; 720 early-stage FECD; 720 late-stage FECD). Thereafter, a final model was trained with the entire dataset consisting the 11,340 images and validated with a remaining 7380 images of unseen AS-OCT scans of 41 eyes (5040 healthy; 1620 early-stage FECD 720 late-stage FECD). Visualization of learned features was done, and area under curve (AUC), specificity, and sensitivity of the prediction outputs for healthy, early and late-stage FECD were computed.

Results: The final model achieved an AUC of 0.997 ± 0.005 with 91% sensitivity and 97% specificity in detecting early-FECD; an AUC of 0.974 ± 0.005 with a specificity of 92% and a sensitivity up to 100% in detecting late-stage FECD; and an AUC of 0.998 ± 0.001 with a specificity 98% and a sensitivity of 99% in discriminating healthy corneas from all FECD.

Conclusion: Deep learning algorithm is an accurate autonomous novel diagnostic tool of FECD with very high sensitivity and specificity that can be used to grade FECD severity with high accuracy.

Keywords: Corneal endothelium; Descemet’s membrane; Fuchs endothelial dystrophy; Optical coherence tomography.

Abstract

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