Automated diagnosis of Alzheimer's Disease using OCT and OCTA: a systematic review

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dc.contributor.authorTurkan, Yasemin
dc.contributor.authorBoray Tek, F.
dc.contributor.authorArpaci, Fatih
dc.contributor.authorArslan, Ozan
dc.contributor.authorToslak, Devrim
dc.contributor.authorBulut, Mehmet
dc.contributor.authorYaman, Aylin
dc.date.accessioned2024-08-20T20:29:21Z
dc.date.available2024-08-20T20:29:21Z
dc.date.issued2024
dc.departmentAntalya Belek Üniversitesien_US
dc.description.abstractRetinal 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.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [122E509]; TUBITAKen_US
dc.description.sponsorshipThis study was supported by Scientific and Technological Research Council of Turkey (TUBITAK) under the Grant Number 122E509.The authors thank to TUBITAK for their supports.en_US
dc.identifier.doi10.1109/ACCESS.2024.3434670
dc.identifier.endpage104051en_US
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-85200200309en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage104031en_US
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2024.3434670
dc.identifier.urihttps://hdl.handle.net/20.500.14591/116
dc.identifier.volume12en_US
dc.identifier.wosWOS:001286627200001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INCen_US
dc.relation.ispartofIeee Accessen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectAlzheimer's diseaseen_US
dc.subjectcognitive impairmenten_US
dc.subjectdeep learningen_US
dc.subjectdementiaen_US
dc.subjectneural networksen_US
dc.subjectoptical coherence tomographyen_US
dc.subjectoptical coherence tomography angiographyen_US
dc.subjectretinal imagingen_US
dc.titleAutomated diagnosis of Alzheimer's Disease using OCT and OCTA: a systematic reviewen_US
dc.typeArticleen_US

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