Early detection of dementia through retinal imaging and trustworthy AI

dc.contributor.authorHao, Jinkui
dc.contributor.authorKwapong, William R.
dc.contributor.authorShen, Ting
dc.contributor.authorFu, Huazhu
dc.contributor.authorXu, Yanwu
dc.contributor.authorLu, Qinkang
dc.contributor.authorLiu, Shouyue
dc.contributor.authorZhang, Jiong
dc.contributor.authorLiu, Yonghuai
dc.contributor.authorZhao, Yifan
dc.contributor.authorZheng, Yalin
dc.contributor.authorFrangi, Alejandro F.
dc.contributor.authorZhang, Shuting
dc.contributor.authorQi, Hong
dc.contributor.authorZhao, Yitian
dc.date.accessioned2024-10-31T09:55:19Z
dc.date.available2024-10-31T09:55:19Z
dc.date.freetoread2024-10-31
dc.date.issued2024-10-04
dc.date.pubOnline2024-10-20
dc.description.abstractAlzheimer's disease (AD) is a global healthcare challenge lacking a simple and affordable detection method. We propose a novel deep learning framework, Eye-AD, to detect Early-onset Alzheimer's Disease (EOAD) and Mild Cognitive Impairment (MCI) using OCTA images of retinal microvasculature and choriocapillaris. Eye-AD employs a multilevel graph representation to analyze intra- and inter-instance relationships in retinal layers. Using 5751 OCTA images from 1671 participants in a multi-center study, our model demonstrated superior performance in EOAD (internal data: AUC = 0.9355, external data: AUC = 0.9007) and MCI detection (internal data: AUC = 0.8630, external data: AUC = 0.8037). Furthermore, we explored the associations between retinal structural biomarkers in OCTA images and EOAD/MCI, and the results align well with the conclusions drawn from our deep learning interpretability analysis. Our findings provide further evidence that retinal OCTA imaging, coupled with artificial intelligence, will serve as a rapid, noninvasive, and affordable dementia detection.
dc.description.journalNamenpj Digital Medicine
dc.description.sponsorshipEngineering and Physical Sciences Research Council (EPSRC)
dc.description.sponsorshipThis work was supported in part by the National Science Foundation Program of China (62422122, 62272444, 62371442, 62302488), in part by the Youth Innovation Promotion Association CAS (2021298), in part by the Zhejiang Provincial Natural Science Foundation of China (LR22F020008, LQ23F010007, LR24F010002, LZ23F010002), in part by Key research and development program of Zhejiang Province (2024C03101, 2024C03204) and Key Project of Ningbo Public Welfare Science and Technology (2023S012). AFF acknowledges the support of the Royal Academy of Engineering Chair INSILEX (CiET1819\9), the UKRI Frontier Research Guarantee INSILICO (EP\Y030494\1). The research of AFF was carried out at the National Institute for Health and Care Research (NIHR) Manchester Biomedical Research Centre (BRC) (NIHR203308).
dc.format.mediumElectronic
dc.identifier.citationHao J, Kwapong WR, Shen T, et al., (2024) Early detection of dementia through retinal imaging and trustworthy AI. npj Digital Medicine, Volume 7, October 2024, Article number 294
dc.identifier.eissn2398-6352
dc.identifier.elementsID555598
dc.identifier.issn2398-6352
dc.identifier.issueNo1
dc.identifier.paperNo294
dc.identifier.urihttps://doi.org/10.1038/s41746-024-01292-5
dc.identifier.urihttps://dspace.lib.cranfield.ac.uk/handle/1826/23146
dc.identifier.volumeNo7
dc.languageEnglish
dc.language.isoen
dc.publisherSpringer
dc.publisher.urihttps://www.nature.com/articles/s41746-024-01292-5
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject4203 Health Services and Systems
dc.subject42 Health Sciences
dc.subjectNeurodegenerative
dc.subjectBioengineering
dc.subjectBrain Disorders
dc.subjectNetworking and Information Technology R&D (NITRD)
dc.subjectClinical Research
dc.subjectAlzheimer's Disease
dc.subjectNeurosciences
dc.subjectAging
dc.subjectDementia
dc.subjectAlzheimer's Disease including Alzheimer's Disease Related Dementias (AD/ADRD)
dc.subjectEye Disease and Disorders of Vision
dc.subjectMachine Learning and Artificial Intelligence
dc.subjectPrevention
dc.subjectAcquired Cognitive Impairment
dc.subject4.1 Discovery and preclinical testing of markers and technologies
dc.subjectEye
dc.subjectNeurological
dc.subject4203 Health services and systems
dc.titleEarly detection of dementia through retinal imaging and trustworthy AI
dc.typeArticle
dc.type.subtypeArticle
dcterms.coverageEngland
dcterms.dateAccepted2024-10-04

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