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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Vydáno v:	British journal of ophthalmology Ročník 106; číslo 3; s. 388 - 395
Hlavní autoři:	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
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	BMA House, Tavistock Square, London, WC1H 9JR BMJ Publishing Group Ltd 01.03.2022 BMJ Publishing Group LTD
Témata:	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
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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.
AbstractList	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. To develop a convolutional neural network (CNN) to detect symptomatic Alzheimer's disease (AD) using a combination of multimodal retinal images and patient data. 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. 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). 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. To develop a convolutional neural network (CNN) to detect symptomatic Alzheimer's disease (AD) using a combination of multimodal retinal images and patient data.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.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.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.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).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).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.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.
Author	Grewal, Dilraj S. Burke, James R. Wang, Dong Henao, Ricardo Yoon, Stephen P. Polascik, Bryce W. Liu, Andy Carin, Lawrence Fekrat, Sharon Wisely, C. Ellis Robbins, Cason B. Soundararajan, Srinath Thompson, Atalie C.
Author_xml	– sequence: 1 givenname: C. Ellis orcidid: 0000-0001-7675-689X surname: Wisely fullname: Wisely, C. Ellis organization: Department of Ophthalmology, Duke University Health System, Durham, NC, USA – sequence: 2 givenname: Dong surname: Wang fullname: Wang, Dong organization: Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, USA – sequence: 3 givenname: Ricardo orcidid: 0000-0003-4980-845X surname: Henao fullname: Henao, Ricardo organization: Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA – sequence: 4 givenname: Dilraj S. orcidid: 0000-0002-2229-5343 surname: Grewal fullname: Grewal, Dilraj S. organization: Department of Ophthalmology, Duke University Health System, Durham, NC, USA – sequence: 5 givenname: Atalie C. surname: Thompson fullname: Thompson, Atalie C. organization: Department of Ophthalmology, Duke University Health System, Durham, NC, USA – sequence: 6 givenname: Cason B. orcidid: 0000-0001-7909-510X surname: Robbins fullname: Robbins, Cason B. organization: Department of Ophthalmology, Duke University Health System, Durham, NC, USA – sequence: 7 givenname: Stephen P. surname: Yoon fullname: Yoon, Stephen P. organization: Department of Ophthalmology, Duke University Health System, Durham, NC, USA – sequence: 8 givenname: Srinath surname: Soundararajan fullname: Soundararajan, Srinath organization: Department of Ophthalmology, Duke University Health System, Durham, NC, USA – sequence: 9 givenname: Bryce W. surname: Polascik fullname: Polascik, Bryce W. organization: Department of Ophthalmology, Duke University Health System, Durham, NC, USA – sequence: 10 givenname: James R. surname: Burke fullname: Burke, James R. organization: Department of Neurology, Duke University Health System, Durham, NC, USA – sequence: 11 givenname: Andy surname: Liu fullname: Liu, Andy organization: Department of Neurology, Duke University Health System, Durham, NC, USA – sequence: 12 givenname: Lawrence surname: Carin fullname: Carin, Lawrence organization: Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina, USA – sequence: 13 givenname: Sharon orcidid: 0000-0003-4403-5996 surname: Fekrat fullname: Fekrat, Sharon email: sharon.fekrat@duke.edu organization: Department of Ophthalmology, Duke University Health System, Durham, NC, USA
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Snippet	Background/AimsTo develop a convolutional neural network (CNN) to detect symptomatic Alzheimer’s disease (AD) using a combination of multimodal retinal images... To develop a convolutional neural network (CNN) to detect symptomatic Alzheimer's disease (AD) using a combination of multimodal retinal images and patient...
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SubjectTerms	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
Title	Convolutional neural network to identify symptomatic Alzheimer’s disease using multimodal retinal imaging
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