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LIBRIS Formathandbok  (Information om MARC21)
FältnamnIndikatorerMetadata
00009171naa a2200793 4500
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008210824s2022 | |||||||||||000 ||eng|
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0247 a https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1784132 URI
0247 a https://doi.org/10.1007/s00259-021-05483-02 DOI
0247 a http://kipublications.ki.se/Default.aspx?queryparsed=id:1472305402 URI
0247 a https://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-453922 URI
040 a (SwePub)liud (SwePub)kid (SwePub)hh
041 a engb eng
042 9 SwePub
072 7a ref2 swepub-contenttype
072 7a art2 swepub-publicationtype
1001 a Etminani, Kobra,d 1984-u Högskolan i Halmstad,Akademin för informationsteknologi,Halmstad Univ, Sweden4 aut0 (Swepub:hh)etikob
2451 0a 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
264 c 2021-07-30
264 1a New York :b Springer,c 2022
338 a electronic2 rdacarrier
500 a 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]
500 a 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).
520 a 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.
650 7a MEDICIN OCH HÄLSOVETENSKAPx Klinisk medicinx Neurologi0 (SwePub)302072 hsv//swe
650 7a MEDICAL AND HEALTH SCIENCESx Clinical Medicinex Neurology0 (SwePub)302072 hsv//eng
650 7a TEKNIK OCH TEKNOLOGIERx Elektroteknik och elektronikx Datorsystem0 (SwePub)202062 hsv//swe
650 7a ENGINEERING AND TECHNOLOGYx Electrical Engineering, Electronic Engineering, Information Engineeringx Computer Systems0 (SwePub)202062 hsv//eng
653 a Artificial intelligence; Deep learning; FDG PET; Alzheimers disease; Mild cognitive impairment; Dementia with Lewy bodies
7001 a Soliman, Amira,d 1980-u Högskolan i Halmstad,Akademin för informationsteknologi,Halmstad Univ, Sweden4 aut0 (Swepub:hh)amisol
7001 a Davidsson, Anetteu Linköpings universitet,Avdelningen för diagnostik och specialistmedicin,Medicinska fakulteten,Region Östergötland, Fysiologiska kliniken US4 aut0 (Swepub:liu)aneda83
7001 a Chang, Jose R.u Halmstad Univ, Sweden; Natl Cheng Kung Univ Tainan, Taiwan4 aut
7001 a Martinez-Sanchis, Begonau Hosp Univ Politecn Fe, Spain4 aut
7001 a Byttner, Stefan,d 1975-u Högskolan i Halmstad,Akademin för informationsteknologi,Halmstad Univ, Sweden4 aut0 (Swepub:hh)stefan
7001 a Camacho, Valleu Servicio de Medicina Nuclear, Hospital de La Santa Creu I Sant Pau, Universitat Autònoma de Barcelona, Barcelona, Spain4 aut
7001 a Bauckneht, Matteou IRCCS Osped Policlin San Martino, Italy4 aut
7001 a Stegeran, Roxanau Region Östergötland, Röntgenkliniken i Linköping4 aut0 (Swepub:liu)n/a
7001 a Ressner, Marcus,d 1967-u Linköpings universitet,Avdelningen för diagnostik och specialistmedicin,Medicinska fakulteten,Region Östergötland, Medicinsk strålningsfysik4 aut0 (Swepub:liu)marre80
7001 a Agudelo-Cifuentes, Marcu Hosp Univ Politecn Fe, Spain4 aut
7001 a Chincarini, Andreau Natl Inst Nucl Phys INFN, Italy4 aut
7001 a Brendel, Matthiasu Univ Hosp, Germany4 aut
7001 a Rominger, Axelu Univ Hosp, Germany; Univ Hosp Bern, Switzerland4 aut
7001 a Bruffaerts, Roseu Dept Neurosci, Belgium; Hasselt Univ, Belgium4 aut
7001 a Vandenberghe, Riku Dept Neurosci, Belgium; Univ Hosp Leuven, Belgium4 aut
7001 a Kramberger, Milica G.u Karolinska Institutet,Univ Med Ctr, Slovenia4 aut
7001 a Trost, Majau Univ Med Ctr, Slovenia; Univ Ljubljana, Slovenia4 aut
7001 a Nicastro, Nicolasu Univ Hosp Geneva, Switzerland,Amsterdam UMC, Netherlands4 aut
7001 a Frisoni, Giovanni B.u Univ Hosp, Switzerland4 aut
7001 a Lemstra, Afina W.u Alzheimer Ctr, Netherlands4 aut
7001 a van Berckel, Bart N. M.u Amsterdam UMC, Netherlands4 aut
7001 a Pilotto, Andreau Univ Brescia, Italy; FERB ONLUS S Isidoro Hosp, Italy4 aut
7001 a Padovani, Alessandrou Univ Brescia, Italy4 aut
7001 a Morbelli, Silviau Univ Genoa, Italy4 aut
7001 a Aarsland, Dagu Karolinska Institutet,Stavanger Univ Hosp, Norway; Kings Coll London, England4 aut
7001 a Nobili, Flaviou Univ Genoa, Italy; IRCCS Osped Policlin San Martino, Italy4 aut
7001 a Garibotto, Valentinau Univ Geneva, Switzerland; Univ Geneva, Switzerland4 aut
7001 a Ochoa-Figueroa, Miguelu Linköpings 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, Sweden4 aut0 (Swepub:liu)n/a
7102 a Högskolan i Halmstadb Akademin för informationsteknologi4 org
7730 t European Journal of Nuclear Medicine and Molecular Imagingd New York : Springerg 49, s. 563-584q 49<563-584x 1619-7070x 1619-7089
8564 u https://liu.diva-portal.org/smash/get/diva2:1587233/FULLTEXT01.pdfx primaryx Raw objecty fulltext:print
8564 u https://link.springer.com/content/pdf/10.1007/s00259-021-05483-0.pdf
8564 u https://doi.org/10.1007/s00259-021-05483-0y Fulltext
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-178413
8564 8u https://doi.org/10.1007/s00259-021-05483-0
8564 8u http://kipublications.ki.se/Default.aspx?queryparsed=id:147230540
8564 8u https://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-45392

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