Graph-Based Deep Learning for Non-invasive Dementia Screening via  Retinal Vascular Topology: A Proof-of-Concept Study

Sayantan Mukherjee, Anurag Shrivastava

Graph-Based Deep Learning for Non-invasive Dementia Screening via Retinal Vascular Topology: A Proof-of-Concept Study

Authors

Sayantan Mukherjee, Anurag Shrivastava

Keywords:

Retinal imaging; graph neural networks; vascular topology; dementia screening; GCN; GAT; microvascular pathology; APTOS 2019; DRIVE; STARE; deep learning; cross-device validation.

Abstract

Retinal fundus imaging provides a non-invasive, scalable window into systemicmicrovascular health, and growing evidence links retinal vascular architecture to cerebrovascular changes associated with cognitive decline and dementia. Conventionaldeep learning approaches process retinal images

References

M. Prince, A. Wimo, M. Guerchet, G. Ali, Y.-T. Wu, and M. Prina, “World Alzheimer Report 2015: The Global Impact of Dementia,” Alzheimer’s Disease International, London, 2016.

B. Dubois et al., “Preclinical Alzheimer’s disease: Definition, natural history, and diagnostic criteria,” Alzheimer’s Dement., vol. 12, no. 3, pp. 292–323, 2016. doi: 10.1016/j.jalz.2016.02.002

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Published

2024-02-15

How to Cite

Sayantan Mukherjee, Anurag Shrivastava. (2024). Graph-Based Deep Learning for Non-invasive Dementia Screening via Retinal Vascular Topology: A Proof-of-Concept Study. Journal of Computational Analysis and Applications (JoCAAA), 33(2), 1275–1292. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/5557