A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimer's disease, and mild cognitive impairment using brain 18F-FDG PET.
Purpose: The purpose of this study is to develop and validate a 3D deep learning model that predicts the final clinical diagnosis of Alzheimer's disease (AD), dementia with Lewy bodies (DLB), mild cognitive impairment due to Alzheimer's disease (MCI-AD), and cognitively normal (CN) using fluorine 18...
| Publicado en: | European Journal of Nuclear Medicine & Molecular Imaging Vol. 49; no. 2; pp. 563 - 585 |
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| Autores principales: | , , , , , , , , , , , , , , , , , , , |
| Formato: | diagnostic images pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jan2022
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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=154982384&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 154982384 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16197070 NPC jtl: European Journal of Nuclear Medicine & Molecular Imaging issn: 16197070 maglogo: N pubinfo: dt: Jan2022 vid: 49 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 154982384 151658823 154982384 154982384 10.1007/s00259-021-05483-0 154982384 ppf: 563 ppct: 22 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimer's disease, and mild cognitive impairment using brain 18F-FDG PET. aug: au: Etminani, Kobra Soliman, Amira Davidsson, Anette Chang, Jose R. Martínez-Sanchis, Begoña Byttner, Stefan Camacho, Valle Bauckneht, Matteo Stegeran, Roxana Ressner, Marcus Agudelo-Cifuentes, Marc Chincarini, Andrea Brendel, Matthias Rominger, Axel Bruffaerts, Rose Vandenberghe, Rik Kramberger, Milica G. Trost, Maja Nicastro, Nicolas Frisoni, Giovanni B. affil: Center for Applied Intelligent Systems Research (CAISR), Halmstad University, Halmstad, Sweden sug: subj: Deep Learning Methods Imaging, Three-Dimensional Dementia Diagnosis Lewy Body Disease Diagnosis Alzheimer's Disease Diagnosis Mild Cognitive Impairment Fludeoxyglucose F 18 Brain Pathology Positron-Emission Tomography Brain Radiography Nuclear Medicine Physician Assistants Neuroradiography Neural Networks (Computer) Sensitivity and Specificity Precision ROC Curve Confidence Intervals Image Processing, Computer Assisted Cerebral Cortex Neurodegenerative Diseases Human Male Female Middle Age Aged Aged, 80 and Over Data Analysis Software Image Interpretation, Computer Assisted Middle Aged: 45-64 years Aged: 65+ years Aged, 80 & over Male Female ab: Purpose: The purpose of this study is to develop and validate a 3D deep learning model that predicts the final clinical diagnosis of Alzheimer's disease (AD), dementia with Lewy bodies (DLB), mild cognitive impairment due to Alzheimer's disease (MCI-AD), and cognitively normal (CN) using fluorine 18 fluorodeoxyglucose PET (18F-FDG PET) and compare model's performance to that of multiple expert nuclear medicine physicians' readers. Materials and methods: Retrospective 18F-FDG PET scans for AD, MCI-AD, and CN were collected from Alzheimer's disease neuroimaging initiative (556 patients from 2005 to 2020), and CN and DLB cases were from European DLB Consortium (201 patients from 2005 to 2018). The introduced 3D convolutional neural network was trained using 90% of the data and externally tested using 10% as well as comparison to human readers on the same independent test set. The model's performance was analyzed with sensitivity, specificity, precision, F1 score, receiver operating characteristic (ROC). The regional metabolic changes driving classification were visualized using uniform manifold approximation and projection (UMAP) and network attention. Results: The proposed model achieved area under the ROC curve of 96.2% (95% confidence interval: 90.6–100) on predicting the final diagnosis of DLB in the independent test set, 96.4% (92.7–100) in AD, 71.4% (51.6–91.2) in MCI-AD, and 94.7% (90–99.5) in CN, which in ROC space outperformed human readers performance. The network attention depicted the posterior cingulate cortex is important for each neurodegenerative disease, and the UMAP visualization of the extracted features by the proposed model demonstrates the reality of development of the given disorders. Conclusion: Using only 18F-FDG PET of the brain, a 3D deep learning model could predict the final diagnosis of the most common neurodegenerative disorders which achieved a competitive performance compared to the human readers as well as their consensus. 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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