Artificial Intelligence on FDG PET Images Identifies Mild Cognitive Impairment Patients with Neurodegenerative Disease.
The purpose of this project is to develop and validate a Deep Learning (DL) FDG PET imaging algorithm able to identify patients with any neurodegenerative diseases (Alzheimer's Disease (AD), Frontotemporal Degeneration (FTD) or Dementia with Lewy Bodies (DLB)) among patients with Mild Cognitive Impa...
| Publicado en: | Journal of Medical Systems Vol. 46; no. 8; pp. 1 - 14 |
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| Autores principales: | , , , , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2022
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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=158079696&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158079696 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Aug2022 vid: 46 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 158079696 158079696 158079696 10.1007/s10916-022-01836-w 158079696 ppf: 1 ppct: 13 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Artificial Intelligence on FDG PET Images Identifies Mild Cognitive Impairment Patients with Neurodegenerative Disease. aug: au: Prats-Climent, Joan Gandia-Ferrero, Maria Teresa Torres-Espallardo, Irene Álvarez-Sanchez, Lourdes Martínez-Sanchis, Begoña Cháfer-Pericás, Consuelo Gómez-Rico, Ignacio Cerdá-Alberich, Leonor Aparici-Robles, Fernando Baquero-Toledo, Miquel Rodríguez-Álvarez, María José Martí-Bonmatí, Luis affil: Instituto de Instrumentación Para Imagen Molecular (I3M), Universitat Politècnica de València (UPV), Camí de Vera, s/n, 46022, Valencia, Spain sug: subj: Mild Cognitive Impairment Diagnosis Neurodegenerative Diseases Diagnosis Positron-Emission Tomography Artificial Intelligence Deep Learning Fludeoxyglucose F 18 Program Development Program Evaluation Psychiatric Patients Human Alzheimer's Disease Diagnosis Frontotemporal Lobar Degeneration Diagnosis Lewy Body Disease Diagnosis Neural Networks (Computer) Sensitivity and Specificity ROC Curve Prospective Studies Scales Psychological Tests Male Female Aged Aged, 80 and Over Descriptive Statistics Data Analysis Software Aged: 65+ years Aged, 80 & over Male Female ab: The purpose of this project is to develop and validate a Deep Learning (DL) FDG PET imaging algorithm able to identify patients with any neurodegenerative diseases (Alzheimer's Disease (AD), Frontotemporal Degeneration (FTD) or Dementia with Lewy Bodies (DLB)) among patients with Mild Cognitive Impairment (MCI). A 3D Convolutional neural network was trained using images from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The ADNI dataset used for the model training and testing consisted of 822 subjects (472 AD and 350 MCI). The validation was performed on an independent dataset from La Fe University and Polytechnic Hospital. This dataset contained 90 subjects with MCI, 71 of them developed a neurodegenerative disease (64 AD, 4 FTD and 3 DLB) while 19 did not associate any neurodegenerative disease. The model had 79% accuracy, 88% sensitivity and 71% specificity in the identification of patients with neurodegenerative diseases tested on the 10% ADNI dataset, achieving an area under the receiver operating characteristic curve (AUC) of 0.90. On the external validation, the model preserved 80% balanced accuracy, 75% sensitivity, 84% specificity and 0.86 AUC. This binary classifier model based on FDG PET images allows the early prediction of neurodegenerative diseases in MCI patients in standard clinical settings with an overall 80% classification balanced accuracy. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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