PT  - JOURNAL ARTICLE
AU  - C. Ellis Wisely
AU  - Dong Wang
AU  - Ricardo Henao
AU  - Dilraj S. Grewal
AU  - Atalie C. Thompson
AU  - Cason B. Robbins
AU  - Stephen P. Yoon
AU  - Srinath Soundararajan
AU  - Bryce W. Polascik
AU  - James R. Burke
AU  - Andy Liu
AU  - Lawrence Carin
AU  - Sharon Fekrat
TI  - Convolutional neural network to identify symptomatic Alzheimer’s disease using multimodal retinal imaging
AID  - 10.1136/bjophthalmol-2020-317659
DP  - 2022 Mar 01
TA  - British Journal of Ophthalmology
PG  - 388--395
VI  - 106
IP  - 3
4099  - http://bjo.bmj.com/content/106/3/388.short
4100  - http://bjo.bmj.com/content/106/3/388.full
SO  - Br J Ophthalmol2022 Mar 01; 106
AB  - 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.Data from this study is available upon reasonable request from our corresponding author, Dr. Sharon Fekrat, sharon.fekrat@duke.edu.