Convolutional neural network to identify symptomatic Alzheimer's disease using multimodal retinal imaging.

Abstract

BACKGROUND/AIMS:To develop a convolutional neural network (CNN) to detect symptomatic Alzheimer's disease (AD) using a combination of multimodal retinal images and patient data. METHODS:Colour maps of ganglion cell-inner plexiform layer (GC-IPL) thickness, superficial capillary plexus (SCP) optical coherence tomography angiography (OCTA) images, and ultra-widefield (UWF) colour and fundus autofluorescence (FAF) scanning laser ophthalmoscopy images were captured in individuals with AD or healthy cognition. A CNN to predict AD diagnosis was developed using multimodal retinal images, OCT and OCTA quantitative data, and patient data. RESULTS:284 eyes of 159 subjects (222 eyes from 123 cognitively healthy subjects and 62 eyes from 36 subjects with AD) were used to develop the model. Area under the receiving operating characteristic curve (AUC) values for predicted probability of AD for the independent test set varied by input used: UWF colour AUC 0.450 (95% CI 0.282, 0.592), OCTA SCP 0.582 (95% CI 0.440, 0.724), UWF FAF 0.618 (95% CI 0.462, 0.773), GC-IPL maps 0.809 (95% CI 0.700, 0.919). A model incorporating all images, quantitative data and patient data (AUC 0.836 (CI 0.729, 0.943)) performed similarly to models only incorporating all images (AUC 0.829 (95% CI 0.719, 0.939)). GC-IPL maps, quantitative data and patient data AUC 0.841 (95% CI 0.739, 0.943). CONCLUSION:Our CNN used multimodal retinal images to successfully predict diagnosis of symptomatic AD in an independent test set. GC-IPL maps were the most useful single inputs for prediction. Models including only images performed similarly to models also including quantitative data and patient data.

Department

Description

Provenance

Citation

Published Version (Please cite this version)

10.1136/bjophthalmol-2020-317659

Publication Info

Wisely, C Ellis, Dong Wang, Ricardo Henao, Dilraj S Grewal, Atalie C Thompson, Cason B Robbins, Stephen P Yoon, Srinath Soundararajan, et al. (2020). Convolutional neural network to identify symptomatic Alzheimer's disease using multimodal retinal imaging. The British journal of ophthalmology. 10.1136/bjophthalmol-2020-317659 Retrieved from https://hdl.handle.net/10161/21874.

This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.

Scholars@Duke

Grewal

Dilraj Singh Grewal

Associate Professor of Ophthalmology

Vitreoretinal and Uveitis Specialist

Dilraj Grewal, MD specializes in the medical and surgical management of patients with complex Vitreoretinal pathology and Uveitis. He joined the Duke Eye Center in December 2016 following completion of his Vitreoretinal Surgery fellowship at Duke and Uveitis fellowship training at Moorfields Eye Hospital in London. Dr. Grewal is excited about treating patients with several of the new diagnostic and therapeutic modalities available as well as several others in the pipeline to better help patients with these potentially blinding diseases.

He has been the recipient of numerous prestigious awards including the Ronald G. Michels Foundation Fellowship Award, the Heed Ophthalmic Foundation Fellowship Award, Senior Achievement Award from the American Academy of Ophthalmology and Rhett Buckler and Senior Honor Awards from American Society of Retina Specialists.

Dr. Grewal has authored over 100 publications in peer-reviewed medical journals and over 150 presentations at national and international meetings. His research interests span clinical research activities in advanced ocular imaging and clinical trials for both Retina and Uveitis. He also serves as Director of Grading at the Duke Reading Center, a comprehensive image reading center that specializes in systematic analysis of ophthalmic images captured by many different modalities in multicenter clinical trials. In addition, he participates in national and international clinical trials in retina and uveitis.

Yoon

Stephen Paul Yoon

Clinical Associate in the department of Ophthalmology

Unless otherwise indicated, scholarly articles published by Duke faculty members are made available here with a CC-BY-NC (Creative Commons Attribution Non-Commercial) license, as enabled by the Duke Open Access Policy. If you wish to use the materials in ways not already permitted under CC-BY-NC, please consult the copyright owner. Other materials are made available here through the author’s grant of a non-exclusive license to make their work openly accessible.