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Convolutional neural network to identify symptomatic Alzheimer's disease using multimodal retinal imaging
- Source :
- The British journal of ophthalmology. 106(3)
- Publication Year :
- 2020
-
Abstract
- 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 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.Results284 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).ConclusionOur 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.
- Subjects :
- medicine.medical_specialty
Capillary plexus
Convolutional neural network
Retina
03 medical and health sciences
Cellular and Molecular Neuroscience
chemistry.chemical_compound
0302 clinical medicine
Alzheimer Disease
Ophthalmology
medicine
Humans
Fluorescein Angiography
business.industry
Healthy subjects
Retinal Vessels
Retinal
Patient data
Sensory Systems
Scanning laser ophthalmoscopy
medicine.anatomical_structure
chemistry
030221 ophthalmology & optometry
Retinal imaging
Neural Networks, Computer
business
030217 neurology & neurosurgery
Tomography, Optical Coherence
Subjects
Details
- ISSN :
- 14682079
- Volume :
- 106
- Issue :
- 3
- Database :
- OpenAIRE
- Journal :
- The British journal of ophthalmology
- Accession number :
- edsair.doi.dedup.....030e67c88ea97688ac066b6308fdd273