Automated diagnosis of Alzheimer's Disease using OCT and OCTA: a systematic review
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Access Rights
info:eu-repo/semantics/openAccess
Abstract
Retinal optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) have emerged as promising, non-invasive, and cost-effective modalities for the early diagnosis of Alzheimer's disease (AD). However, a comprehensive review of automated deep learning techniques for diagnosing AD or mild cognitive impairment (MCI) using OCT/OCTA data is lacking. We addressed this gap by conducting a systematic review using the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. We systematically searched databases, including Scopus, PubMed, and Web of Science, and identified 16 important studies from an initial set of 4006 references. We then analyzed these studies through a structured framework, focusing on the key aspects of deep learning workflows for AD/MCI diagnosis using OCT-OCTA. This included dataset curation, model training, and validation methodologies. Our findings indicate a shift towards employing end-to-end deep learning models to directly analyze OCT/OCTA images in diagnosing AD/MCI, moving away from traditional machine learning approaches. However, we identified inconsistencies in the data collection methods across studies, leading to varied outcomes. We emphasize the need for longitudinal studies on early AD and MCI diagnosis, along with further research on interpretability tools to enhance model accuracy and reliability for clinical translation.
Description
Keywords
Alzheimer's disease, cognitive impairment, deep learning, dementia, neural networks, optical coherence tomography, optical coherence tomography angiography, retinal imaging
Journal or Series
Ieee Access
WoS Q Value
N/A
Scopus Q Value
Q1
Volume
12
