Development and validation of a deep learning algorithm based on fundus photographs for estimating the CAIDE dementia risk score.
Background the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) dementia risk score is a recognised tool for dementia risk stratification. However, its application is limited due to the requirements for multidimensional information and fasting blood draw. Consequently, an effect...
| Publicado en: | Age & Ageing Vol. 51; no. 12; pp. 1 - 10 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
| Formato: | Artículo |
| Publicado: |
Oxford University Press / USA
Dec2022
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=161116518&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 161116518 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 00020729 AGA jtl: Age & Ageing issn: 00020729 maglogo: N pubinfo: dt: Dec2022 vid: 51 iid: 12 pid: 622 pub: Oxford University Press / USA artinfo: ui: 161116518 10.1093/ageing/afac282 ppf: 1 ppct: 9 formats: tig: atl: Development and validation of a deep learning algorithm based on fundus photographs for estimating the CAIDE dementia risk score. aug: au: Hua, Rong Xiong, Jianhao Li, Gail Zhu, Yidan Ge, Zongyuan Ma, Yanjun Fu, Meng Li, Chenglong Wang, Bin Dong, Li Zhao, Xin Ma, Zhiqiang Chen, Jili Gao, Xinxiao He, Chao Wang, Zhaohui Wei, Wenbin Wang, Fei Gao, Xiangyang Chen, Yuzhong affil: Peking University Clinical Research Institute, Peking University First Hospital , Beijing 100191 , China PUCRI Heart and Vascular Health Research Center at Peking University Shougang Hospital , Beijing , China Beijing Airdoc Technology Co., Ltd. , Beijing , China Departments of Psychiatry and Behavioral Sciences, University of Washington , Seattle, WA , USA Division of Gerontology and Geriatric Medicine, University of Washington , Seattle, WA , USA PUCRI Heart and Vascular Health Research Center at Peking University Shougang Hospital , Beijing, China Beijing Airdoc Technology Co., Ltd. , Beijing, China Beijing Tongren Eye Center, Beijing Tongren Hospital , Beijing, China iKang Guobin Healthcare Group Co., Ltd. , Beijing, China Shibei Hospital , Jingan District, Shanghai, China Department of Ophthalmology, Beijing Anzhen Hospital, Capital Medical University , Beijing, China Health Management Institute, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital , Beijing 100853 , China su: China Cross-sectional method Cognition Aging Old age Dementia risk factors Deep learning Cardiovascular diseases risk factors Confidence intervals Research methodology evaluation Research methodology Photography Sensitivity & specificity (Statistics) Algorithms sug: subj: Cross-sectional method Cognition Aging Old age China Dementia risk factors Deep learning Cardiovascular diseases risk factors Confidence intervals Research methodology evaluation Research methodology Photography Sensitivity & specificity (Statistics) Algorithms keyword: CAIDE dementia risk score deep learning dementia fundus photographs older people CAIDE dementia risk score deep learning dementia fundus photographs older people ab: Background the Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) dementia risk score is a recognised tool for dementia risk stratification. However, its application is limited due to the requirements for multidimensional information and fasting blood draw. Consequently, an effective and non-invasive tool for screening individuals with high dementia risk in large population-based settings is urgently needed. Methods a deep learning algorithm based on fundus photographs for estimating the CAIDE dementia risk score was developed and internally validated by a medical check-up dataset included 271,864 participants in 19 province-level administrative regions of China, and externally validated based on an independent dataset included 20,690 check-up participants in Beijing. The performance for identifying individuals with high dementia risk (CAIDE dementia risk score ≥ 10 points) was evaluated by area under the receiver operating curve (AUC) with 95% confidence interval (CI). Results the algorithm achieved an AUC of 0.944 (95% CI: 0.939–0.950) in the internal validation group and 0.926 (95% CI: 0.913–0.939) in the external group, respectively. Besides, the estimated CAIDE dementia risk score derived from the algorithm was significantly associated with both comprehensive cognitive function and specific cognitive domains. Conclusions this algorithm trained via fundus photographs could well identify individuals with high dementia risk in a population setting. Therefore, it has the potential to be utilised as a non-invasive and more expedient method for dementia risk stratification. It might also be adopted in dementia clinical trials, incorporated as inclusion criteria to efficiently select eligible participants. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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