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Last Posted: Oct 03, 2022
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A deep learning model for detection of Alzheimer's disease based on retinal photographs: a retrospective, multicentre case-control study
CY Cheung et al, The Lancet Digital Health, September 30, 2022

12?949 retinal photographs from 648 patients with Alzheimer's disease and 3240 people without the disease were used to train, validate, and test the deep learning model. In the internal validation dataset, the deep learning model had 83·6% (SD 2·5) accuracy, 93·2% (SD 2·2) sensitivity, 82·0% (SD 3·1) specificity, and an area under the receiver operating characteristic curve (AUROC) of 0·93 (0·01) for detecting Alzheimer's disease-dementia.

Artificial intelligence for detection of Alzheimer's disease: demonstration of real-world value is required to bridge the translational gap
CR Marshall et al, The Lancet Digital Health, September 30, 2022

The wide availability of retinal photography could, in principle, support detection of Alzheimer's disease at population level, allowing earlier access to support and treatment. This raises important questions that have yet to be resolved around what constitutes a timely diagnosis of Alzheimer's disease, and how effectively earlier detection improves quality of life, prognosis, and future health-care resource requirements.

Data-driven causal model discovery and personalized prediction in Alzheimer's disease.
Zheng Haoyang et al. NPJ digital medicine 2022 9 (1) 137

We develop an innovative data-driven modeling approach to build and parameterize a causal model to characterize the trajectories of AD biomarkers. This approach integrates causal model learning, population parameterization, parameter sensitivity analysis, and personalized prediction. By applying this integrated approach to a large multicenter database of AD biomarkers, the Alzheimer’s Disease Neuroimaging Initiative, several causal models for different AD stages are revealed. In addition, personalized models for each subject are calibrated and provide accurate predictions of future cognitive status.

Genetics of the human microglia regulome refines Alzheimer's disease risk loci.
Kosoy Roman et al. Nature genetics 2022 8 (8) 1145-1154

Here, we performed transcriptome and chromatin accessibility profiling in primary human microglia from 150 donors to identify genetically driven variation and cell-specific enhancer–promoter (E-P) interactions. Integrative fine-mapping analysis identified putative regulatory mechanisms for 21 AD risk loci, of which 18 were refined to a single gene, including 3 new candidate risk genes (KCNN4, FIBP and LRRC25). Transcription factor regulatory networks captured AD risk variation and identified SPI1 as a key putative regulator of microglia expression and AD risk.

Disclaimer: Articles listed in the Public Health Genomics and Precision Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.