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

Wisely, C Ellis

Wang, Dong

Henao, Ricardo

Grewal, Dilraj S

Thompson, Atalie C

Robbins, Cason B

Yoon, Stephen P

Soundararajan, Srinath

Polascik, Bryce W

Burke, James R

Liu, Andy

Carin, Lawrence

Fekrat, Sharon

2020-12-08T13:29:14Z

2020-12-08T13:29:14Z

2020-11-26

2020-12-08T13:29:12Z

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.

bjophthalmol-2020-317659

0007-1161

1468-2079

https://hdl.handle.net/10161/21874

eng

BMJ

The British journal of ophthalmology

10.1136/bjophthalmol-2020-317659

diagnostic tests/investigation

imaging

retina

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

Journal article

Henao, Ricardo|0000-0003-4980-845X

Grewal, Dilraj S|0000-0002-2229-5343

Burke, James R|0000-0002-3408-7787

Liu, Andy|0000-0003-3096-1788

Fekrat, Sharon|0000-0003-4403-5996

School of Medicine

Ophthalmology, Vitreoretinal Diseases & Surgery

Duke

Ophthalmology

Clinical Science Departments

Electrical and Computer Engineering

Duke Clinical Research Institute

Biostatistics & Bioinformatics

Pratt School of Engineering

Institutes and Centers

Basic Science Departments

Neurology, Behavioral Neurology

Neurology

Published