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  • Etminani, Kobra,1984-Högskolan i Halmstad,Akademin för informationsteknologi,Halmstad Univ, Sweden (författare)

A 3D deep learning model to predict the diagnosis of dementia with Lewy bodies, Alzheimers disease, and mild cognitive impairment using brain 18F-FDG PET

  • Artikel/kapitelEngelska2022

Förlag, utgivningsår, omfång ...

  • 2021-07-30
  • New York :Springer,2022
  • electronicrdacarrier

Nummerbeteckningar

  • LIBRIS-ID:oai:DiVA.org:liu-178413
  • https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-178413URI
  • https://doi.org/10.1007/s00259-021-05483-0DOI
  • http://kipublications.ki.se/Default.aspx?queryparsed=id:147230540URI
  • https://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-45392URI

Kompletterande språkuppgifter

  • Språk:engelska
  • Sammanfattning på:engelska

Ingår i deldatabas

Klassifikation

  • Ämneskategori:ref swepub-contenttype
  • Ämneskategori:art swepub-publicationtype

Anmärkningar

  • Funding Agencies|Halmstad University; Analytic Imaging Diagnostics Arena (AIDA) initiative - VINNOVA [2017-02447]; Analytic Imaging Diagnostics Arena (AIDA) initiative - Formas; Analytic Imaging Diagnostics Arena (AIDA) initiative - Swedish Energy Agency; Swiss National Science FoundationSwiss National Science Foundation (SNSF)European Commission [320030_169876, 320030_185028]; Velux Foundation [1123]; Flanders Research FoundationFWO [FWO 12I2121N]
  • Published online 30 July 2021. Funding text 1 Part of data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12–2-0012). Funding text 2 Open access funding provided by Halmstad University. This study was part of a collaborative project between Center for Applied Intelligent System Research (CAISR) at Halmstad University, Sweden, and Department of Clinical Physiology, Department of Radiology and the Center for Medical Imaging Visualization (CMIV) at Linköping University Hospital, Sweden, and the European DLB consortium, which was funded by Analytic Imaging Diagnostics Arena (AIDA) initiative, jointly supported by VINNOVA (Grant 2017–02447), Formas and the Swedish Energy Agency. VG was supported by the Swiss National Science Foundation (projects 320030_169876, 320030_185028) and the Velux Foundation (project 1123). RB is a senior postdoctoral fellow of the Flanders Research Foundation (FWO 12I2121N).
  • Purpose The purpose of this study is to develop and validate a 3D deep learning model that predicts the final clinical diagnosis of Alzheimers disease (AD), dementia with Lewy bodies (DLB), mild cognitive impairment due to Alzheimers disease (MCI-AD), and cognitively normal (CN) using fluorine 18 fluorodeoxyglucose PET (18F-FDG PET) and compare models 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 Alzheimers 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 models 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.

Ämnesord och genrebeteckningar

Biuppslag (personer, institutioner, konferenser, titlar ...)

  • Soliman, Amira,1980-Högskolan i Halmstad,Akademin för informationsteknologi,Halmstad Univ, Sweden(Swepub:hh)amisol (författare)
  • Davidsson, AnetteLinköpings universitet,Avdelningen för diagnostik och specialistmedicin,Medicinska fakulteten,Region Östergötland, Fysiologiska kliniken US(Swepub:liu)aneda83 (författare)
  • Chang, Jose R.Halmstad Univ, Sweden; Natl Cheng Kung Univ Tainan, Taiwan (författare)
  • Martinez-Sanchis, BegonaHosp Univ Politecn Fe, Spain (författare)
  • Byttner, Stefan,1975-Högskolan i Halmstad,Akademin för informationsteknologi,Halmstad Univ, Sweden(Swepub:hh)stefan (författare)
  • Camacho, ValleServicio de Medicina Nuclear, Hospital de La Santa Creu I Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain (författare)
  • Bauckneht, MatteoIRCCS Osped Policlin San Martino, Italy (författare)
  • Stegeran, RoxanaRegion Östergötland, Röntgenkliniken i Linköping(Swepub:liu)n/a (författare)
  • Ressner, Marcus,1967-Linköpings universitet,Avdelningen för diagnostik och specialistmedicin,Medicinska fakulteten,Region Östergötland, Medicinsk strålningsfysik(Swepub:liu)marre80 (författare)
  • Agudelo-Cifuentes, MarcHosp Univ Politecn Fe, Spain (författare)
  • Chincarini, AndreaNatl Inst Nucl Phys INFN, Italy (författare)
  • Brendel, MatthiasUniv Hosp, Germany (författare)
  • Rominger, AxelUniv Hosp, Germany; Univ Hosp Bern, Switzerland (författare)
  • Bruffaerts, RoseDept Neurosci, Belgium; Hasselt Univ, Belgium (författare)
  • Vandenberghe, RikDept Neurosci, Belgium; Univ Hosp Leuven, Belgium (författare)
  • Kramberger, Milica G.Karolinska Institutet,Univ Med Ctr, Slovenia (författare)
  • Trost, MajaUniv Med Ctr, Slovenia; Univ Ljubljana, Slovenia (författare)
  • Nicastro, NicolasUniv Hosp Geneva, Switzerland,Amsterdam UMC, Netherlands (författare)
  • Frisoni, Giovanni B.Univ Hosp, Switzerland (författare)
  • Lemstra, Afina W.Alzheimer Ctr, Netherlands (författare)
  • van Berckel, Bart N. M.Amsterdam UMC, Netherlands (författare)
  • Pilotto, AndreaUniv Brescia, Italy; FERB ONLUS S Isidoro Hosp, Italy (författare)
  • Padovani, AlessandroUniv Brescia, Italy (författare)
  • Morbelli, SilviaUniv Genoa, Italy (författare)
  • Aarsland, DagKarolinska Institutet,Stavanger Univ Hosp, Norway; Kings Coll London, England (författare)
  • Nobili, FlavioUniv Genoa, Italy; IRCCS Osped Policlin San Martino, Italy (författare)
  • Garibotto, ValentinaUniv Geneva, Switzerland; Univ Geneva, Switzerland (författare)
  • Ochoa-Figueroa, MiguelLinköpings universitet,Institutionen för hälsa, medicin och vård,Medicinska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV,Region Östergötland, Röntgenkliniken i Linköping,Region Östergötland, Fysiologiska kliniken US,Linköping University, Linköping, Sweden; Linköping University Hospital, Linköping, Sweden(Swepub:liu)n/a (författare)
  • Högskolan i HalmstadAkademin för informationsteknologi (creator_code:org_t)

Sammanhörande titlar

  • Ingår i:European Journal of Nuclear Medicine and Molecular ImagingNew York : Springer49, s. 563-5841619-70701619-7089

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