The Machine and the Brain: Early Prediction of Dementia using Retinal Biomarkers by Artificial Intelligence

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Background: Structural and chemical alterations induced by dementia lead to neuronal loss and brain atrophy. Individuals with dementia experience a decline in cognitive abilities, which impairs their ability to carry out usual daily tasks unaided, hereby relying on caregivers for assistance. Conventional approaches to diagnose dementia although arguably reliable; however, they can be expensive, time-intensive, and moderately invasive. Nevertheless, due to the considerable anatomical resemblances between the retina and the brain, abnormalities in the retina identified through optical coherence tomography (OCT) and OCT angiography (OCTA) have been explored as a viable non-invasive method for diagnosing neurodegenerative conditions, especially Alzheimer's Disease (AD) and Mild Cognitive Impairment (MCI). To realize these retinal changes and extracting reliable retinal biomarkers from OCT/OCTA, an appropriate segmentation method must be considered. Objectives: The main objective of this study is to explore the relationship between cognitive decline and retinal biomarkers extracted from OCT/OCTA images as well as evaluating biomarkers' effectiveness in early detection of AD and MCI. Prior extracting retinal biomarkers, and to address the shortcomings of segmentation methods, another research objective is to develop deep learning (DL) based segmentation tools to automate the segmentation procedure efficiently/accurately, and hence, extracting more reliable retinal parameters. However, determining which parameters are more dominant in the classification task via feature selection algorithms is another objective of this project. Methods: A systematic search was conducted on PubMed, Web of Science, and Scopus until December 2022, resulted in 64 papers using agreed search keywords, and inclusion/exclusion criteria. Moreover, a dedicated dataset was collected by the National Institute IRCCS "Saverio De Bellis" Research Hospital - Castellana Grotte - Italy that contained: 155 HCs, 168 MCI, 66 AD, and 3 early AD (eAD) individuals that have been cognitively assessed mainly via MMSE and FAB scores. All participants underwent OCTA examination by RTVue XR 100 Avanti SD-OCT system (Optovue, Inc.) where OCT segmentation was performed using the AngioVue module present by commercial software. The OCT b-scans, OCTA en-face scans, and parameters automatically extracted by Optovue RTVue XR software were acquired for participants. The machine extracted parameters were statistically examined, via a well-structured statistical plan, to find associations with cognitive decline stages, mainly AD and MCI against HCs. Moreover, using publicly available datasets (ROSE and OCT-500), various DL models were trained to better segment vasculature and foveal avascular zone (FAZ) from OCTA en-face scans, followed by arguably new parameters extraction not previously provided by commercial solutions. Additionally, another complex dataset consisting of OCT images was acquired from three distinct OCT systems with manual delineation of retinal layers performed by expert annotators. This OCT dataset was used to train another DL model to automatically segment retinal layers specifically centred around the fovea. Results: The systematic review paper, successfully published, indicated the association between various neurodegenerative disorders and specific retinal biomarkers extracted by OCT/OCTA imaging modality. On the other hand, the statistical analysis performed on the extracted machine parameters demonstrated a significant vascular density reduction in different sections around the fovea for MCI and Dem compared against HCs. Conversely, some of the studied machine parameters provided dissimilar results to the literature, and hence, these finding were written in a form of another manuscript draft for the purpose of publishing another paper. On the other hand, the performance of developed DL models for retinal layers segmentation around fovea (from OCT