Development and validation of a deep learning algorithm based on fundus photographs for estimating the CAIDE dementia risk score. (19th December 2022) - Hua, Rong; Xiong, Jianhao; Li, Gail; Zhu, Yidan; Ge, Zongyuan; Ma, Yanjun; Fu, Meng; Li, Chenglong; Wang, Bin; Dong, Li; Zhao, Xin; Ma, Zhiqiang; et al.
Abstract: 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.