Beyond the Eye: A Relational Model for Early Dementia Detection Using Retinal OCTA Images

• URL: http://arxiv.org/abs/2408.05117v1
• Date: Fri, 9 Aug 2024 15:10:34 GMT
• Title: Beyond the Eye: A Relational Model for Early Dementia Detection Using Retinal OCTA Images
• Authors: Shouyue Liu, Jinkui Hao, Yonghuai Liu, Huazhu Fu, Xinyu Guo, Shuting Zhang, Yitian Zhao,
• Abstract summary: We present a novel PolarNet+ that uses retinal optical coherence tomography angiography ( OCTA) to discriminate early-onset Alzheimer's disease (AD) and mild cognitive impairment (MCI) subjects from controls. Our method first maps OCTA images from Cartesian coordinates to polar coordinates, allowing approximate sub-region calculation. We then introduce a multi-view module to serialize and analyze the images along three dimensions for comprehensive, clinically useful information extraction.
• Score: 42.75763279888966
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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