Deep learning for FDG-PET classification in patients with Alzheimer's disease, dementia with Lewy bodies and their mixed pathology: a solution for diagnostic heterogeneity.
Introduction: Mixed pathology of Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) are frequently observed in patients with cognitive impairment, and complicate clinical diagnosis. We aimed to develop a classification model using 18F-fluorodeoxyglucose (FDG) positron emission tomography (...
| Publicado en: | Frontiers in Aging Neuroscience pp. 1 - 13 |
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| Autores principales: | , , , , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Frontiers Media S.A.
2026
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=192381985&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 192381985 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16634365 BG2U jtl: Frontiers in Aging Neuroscience issn: 16634365 maglogo: N pubinfo: dt: 2026 pid: 40038 pub: Frontiers Media S.A. artinfo: ui: 192381985 192381985 192381985 10.3389/fnagi.2026.1780858 192381985 ppf: 1 ppct: 12 formats: tig: atl: Deep learning for FDG-PET classification in patients with Alzheimer's disease, dementia with Lewy bodies and their mixed pathology: a solution for diagnostic heterogeneity. aug: au: Kim, Seonggyu Jeon, Seun Cho, Kwonhwi Kang, Sungwoo Bang, Sungkyu Ye, Byoung Seok Lee, Jong-Min affil: Department of Electronic Engineering, Hanyang University, Seoul, Republic of Korea sug: subj: Fludeoxyglucose F 18 Positron Emission Tomography Computed Tomography Deep Learning Alzheimer's Disease Classification Dementia Classification Lewy Body Disease Classification Classification Algorithms Human Funding Source Female Male Middle Age Aged Aged, 80 and Over Retrospective Design Descriptive Statistics Data Analysis Software ROC Curve Confidence Intervals Chi Square Test Immunoassay Magnetic Resonance Imaging Image Processing, Computer Assisted Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Female Male ab: Introduction: Mixed pathology of Alzheimer's disease (AD) and dementia with Lewy bodies (DLB) are frequently observed in patients with cognitive impairment, and complicate clinical diagnosis. We aimed to develop a classification model using 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET) to improve diagnostic accuracy for these challenging cases. Methods: We analyzed FDG-PET images from 277 participants who were categorized into AD, DLB, mixed disease, and healthy control (HC) groups. Deep learning-based classification models were trained on seven binary classification tasks and one multiclass classification task and subsequently integrated into an ensemble model to predict AD, DLB, mixed disease or HC groups. Results: The model achieved an AUROC of 0.73 (95% CI, 0.69–0.78) for AD, 0.90 (95% CI, 0.89–0.91) for DLB, 0.71 (95% CI, 0.66–0.75) for Mixed, and 0.87 (95% CI, 0.84–0.89) for HC. Discussion: The model represents the state-of-the-art in automatic FDG-PET-based classification of AD, DLB, Mixed, and HC. This study highlights the utility of FDG-PET as a biomarker for differentiating AD, DLB, Mixed, and HC groups, resolving diagnostic challenges caused by overlapping clinical features. pubtype: Academic Journal doctype: diagnostic images pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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