Development and validation of a deep learning algorithm based on fundus photographs for estimating the CAIDE dementia risk score

The Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) dementia risk score is a recognized tool for dementia risk stratification. However, its application is limited due to the requirements for multidimensional information and fasting blood draw. Consequently, effective, convenient and noninvasive tool for screening individuals with high dementia risk in large population-based settings is urgently needed. 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). We found that 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. In conclusion, this algorithm trained via fundus photographs could well identify individuals with high dementia risk in a population setting. Therefore, it has potential to be utilized as a noninvasive 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.