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

Background/AimsTo develop a convolutional neural network (CNN) to detect symptomatic Alzheimer’s disease (AD) using a combination of multimodal retinal images and patient data.MethodsColour maps of ganglion cell-inner plexiform layer (GC-IPL) thickness, superficial capillary plexus (SCP) optical coh...

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Bibliographic Details
Published in:	British journal of ophthalmology Vol. 106; no. 3; pp. 388 - 395
Main Authors:	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
Format:	Journal Article
Language:	English
Published:	BMA House, Tavistock Square, London, WC1H 9JR BMJ Publishing Group Ltd 01.03.2022 BMJ Publishing Group LTD
Subjects:	Alzheimer Disease - diagnostic imaging Alzheimer's disease Biomarkers Clinical science diagnostic tests/investigation Fluorescein Angiography - methods Humans imaging Machine learning Medical diagnosis Medical imaging Neural networks Neural Networks, Computer Patients Retina Retina - diagnostic imaging Retinal Vessels Tomography Tomography, Optical Coherence - methods United States > US California retina diagnostic tests/investigation imaging
ISSN:	0007-1161, 1468-2079, 1468-2079
Online Access:	Get full text
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