Deep Learning in neuroimaging for neurodegenerative diseases: State-of-the art, Challenges, and Opportunities

Basit Öğe Kaydı
dc.authorid0000-0001-7822-1549
dc.contributor.authorAkan, Taymaz
dc.contributor.authorAlp, Sait
dc.contributor.authorLedbetter, Christina Raye
dc.contributor.authorTafti, Ahmad P.
dc.contributor.authorArevalo, Octavio
dc.contributor.authorBhuiyan, Mohammad Alfrad Nobel
dc.date.accessioned2025-11-25T12:05:56Z
dc.date.available2025-11-25T12:05:56Z
dc.date.issued2025
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractNeuroimaging is commonly used to diagnose neurodegenerative diseases (NDDs), providing crucial insights into brain changes before clinical symptoms manifest. Deep learning (DL) for neuroimaging can improve early diagnosis and disease monitoring. Clinical implementation of DL faces challenges in accurately representing realworld data. Recent models, particularly those focused on diagnostic categorization, have achieved high accuracy, but their applicability to patients is limited. Conflicting inferences have been reported, with findings from small cohorts generalizing conclusions without considering inter-scanner, intra- and inter-site variations. A theoretically feasible method involves gathering a comprehensive dataset that encompasses all patient demographics, but this presents practical challenges including harmonization, data incompleteness, class imbalance, and substantial costs. Existing research has also mostly focused on common NDDs like Alzheimer's Disease (AD) and Parkinson's Disease (PD). This contribution expands the literature by looking at a wider range of NDDs, exploring the latest advancements in applying deep learning algorithms to neuroimaging analysis for the diagnosis and monitoring of NDDs, including AD, Frontotemporal Dementia (FTD), Lewy Body Dementia, PD, Huntington's Disease, Amyotrophic Lateral Sclerosis, and Multiple Sclerosis. We emphasize how these approaches are handling spatial/temporal information available in brain volume imaging data. We conclude by discussing the challenges associated with the use of voxel-based, patch-based, ROI-based, and slice-based approaches in brain volume imaging. These challenges are further compounded by issues such as inter-site and inter-scanner variability, class imbalances in medical datasets, and the scarcity of accurately annotated data, all of which impact the performance and generalizability of deep learning models.
dc.identifier.citationTaymaz Akan, Akan, S., Sait Alp, Ledbetter, C. R., Tafti, A. P., Arevalo, O., & Alfrad, M. (2025). Deep Learning in neuroimaging for neurodegenerative diseases: State-of-the art, Challenges, and Opportunities. Journal of the Neurological Sciences, 478, 123735–123735. ‌
dc.identifier.doi10.1016/j.jns.2025.123735
dc.identifier.endpage21
dc.identifier.issn0022510X
dc.identifier.issue478
dc.identifier.pmid41176929
dc.identifier.scopus2-s2.0-105020663793
dc.identifier.scopusqualityQ2
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttps://hdl.handle.net/20.500.12941/347
dc.identifier.wosWOS:001610474800001
dc.identifier.wosqualityQ2
dc.identifier.wosqualityQ2
dc.indekslendigikaynakScopus
dc.indekslendigikaynakScopus
dc.indekslendigikaynakWeb of Science
dc.institutionauthorAkan, Sara
dc.institutionauthorid0000-0001-7822-1549
dc.language.isoen
dc.publisherElsevier B.V.
dc.relation.ispartofJournal of the Neurological Sciences
dc.relation.ispartofseries123735
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subject3D brain scans
dc.subjectBrain disorders
dc.subjectBrain volume
dc.subjectCNN
dc.subjectDeep learning
dc.subjectEarly diagnosis
dc.subjectNeurodegenerative diseases
dc.subjectNeuroimaging modalities
dc.subjectTransformers
dc.subject3D beyin taramaları
dc.subjectBeyin bozuklukları
dc.subjectBeyin hacmi
dc.subjectDerin öğrenme
dc.subjectErken tanı
dc.subjectNörodejeneratif hastalıklar
dc.subjectNörogörüntüleme yöntemleri
dc.subjectTransformatörler
dc.titleDeep Learning in neuroimaging for neurodegenerative diseases: State-of-the art, Challenges, and Opportunities
dc.typeArticle

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
Akan Sara.pdf
Boyut:
2.13 MB
Biçim:
Adobe Portable Document Format
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.17 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Bilgisayar Mühendisliği Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu