Beyond the Eye: A Relational Model for Early Dementia Detection Using Retinal OCTA Images
<|reference_start|>Beyond the Eye: A Relational Model for Early Dementia Detection Using Retinal OCTA Images: Early detection of dementia, such as Alzheimer's disease (AD) or mild cognitive impairment (MCI), is essential to enable timely intervention and potential treatment. Accurate detection of AD/MCI is challenging due to the high complexity, cost, and often invasive nature of current diagnostic techniques, which limit their suitability for large-scale population screening. Given the shared embryological origins and physiological characteristics of the retina and brain, retinal imaging is emerging as a potentially rapid and cost-effective alternative for the identification of individuals with or at high risk of AD. In this paper, we present a novel PolarNet+ that uses retinal optical coherence tomography angiography (OCTA) to discriminate early-onset AD (EOAD) and MCI subjects from controls. Our method first maps OCTA images from Cartesian coordinates to polar coordinates, allowing approximate sub-region calculation to implement the clinician-friendly early treatment of diabetic retinopathy study (ETDRS) grid analysis. We then introduce a multi-view module to serialize and analyze the images along three dimensions for comprehensive, clinically useful information extraction. Finally, we abstract the sequence embedding into a graph, transforming the detection task into a general graph classification problem. A regional relationship module is applied after the multi-view module to excavate the relationship between the sub-regions. Such regional relationship analyses validate known eye-brain links and reveal new discriminative patterns.<|reference_end|>
@article{liu2024beyond,
    title={Beyond the Eye: A Relational Model for Early Dementia Detection Using
  Retinal OCTA Images},
    author={Shouyue Liu, Jinkui Hao, Yonghuai Liu, Huazhu Fu, Xinyu Guo, Shuting
  Zhang and Yitian Zhao},
    journal={arXiv preprint arXiv:2408.05117},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.05117},
    primaryClass={eess.IV cs.AI cs.CV}
}