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Machine Learning and Multiparametric Brain MRI to Differentiate Hereditary Diffuse Leukodystrophy with Spheroids from Multiple Sclerosis

Mangeat, G. (författare)
NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, Quebec, Canada
Ouellette, R. (författare)
Karolinska Institutet
Wabartha, M. (författare)
NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, Quebec, Canada
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De Leener, B. (författare)
Department of Computer Sciences and Software Engineering, Polytechnique Montreal, Montreal, Quebec, Canada
Plattén, Michael (författare)
Karolinska Institutet,KTH,Skolan för kemi, bioteknologi och hälsa (CBH),Karolinska Inst, Dept Clin Neurosci; Karolinska Univ Hosp,Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden; Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden; School of Engineering Sciences in Chemistry, Biochemistry and Health, Royal Institute of Technology, Stockholm, Sweden
Karrenbauer, V. (författare)
Karolinska Institutet
Warntjes, Marcel, 1973- (författare)
Linköpings universitet,Avdelningen för diagnostik och specialistmedicin,Medicinska fakulteten,Centrum för medicinsk bildvetenskap och visualisering, CMIV,SyntheticMR, Linköping, Sweden
Stikov, N. (författare)
NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, Quebec, Canada; Montreal Heart Institute, Montreal, Quebec, Canada
Mainero, C. (författare)
Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, MGH, Charlestown, MA; Harvard Medical School, Boston, MA
Cohen-Adad, J. (författare)
NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, Quebec, Canada; and Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montreal, Quebec, Canada
Granberg, T. (författare)
Karolinska Institutet
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 (creator_code:org_t)
2020-05-26
2020
Engelska.
Ingår i: Journal of Neuroimaging. - : Blackwell Publishing Inc.. - 1051-2284 .- 1552-6569. ; 30:5, s. 674-682
  • Tidskriftsartikel (refereegranskat)
Abstract Ämnesord
Stäng  
  • BACKGROUND AND PURPOSE: Hereditary diffuse leukoencephalopathy with spheroids (HDLS) and multiple sclerosis (MS) are demyelinating and neurodegenerative disorders that can be hard to distinguish clinically and radiologically. HDLS is a rare disorder compared to MS, which has led to occurrent misdiagnosis of HDLS as MS. That is problematic since their prognosis and treatment differ. Both disorders are investigated by MRI, which could help to identify patients with high probability of having HDLS, which could guide targeted genetic testing to confirm the HDLS diagnosis. METHODS: Here, we present a machine learning method based on quantitative MRI that can achieve a robust classification of HDLS versus MS. Four HDLS and 14 age-matched MS patients underwent a quantitative brain MRI protocol (synthetic MRI) at 3 Tesla (T) (scan time '7 minutes). We also performed a repeatability analysis of the predicting features to assess their generalizability by scanning a healthy control with five scan-rescans at 3T and 1.5T. RESULTS: Our predicting features were measured with an average confidence interval of 1.7% (P =.01), at 3T and 2.3% (P =.01) at 1.5T. The model gave a 100% correct classification of the cross-validation data when using 5-11 predicting features. When the maximum measurement noise was inserted in the model, the true positive rate of HDLS was 97.2%, while the true positive rate of MS was 99.6%. CONCLUSIONS: This study suggests that computer-assistance in combination with quantitative MRI may be helpful in aiding the challenging differential diagnosis of HDLS versus MS. 

Ämnesord

MEDICIN OCH HÄLSOVETENSKAP  -- Klinisk medicin -- Neurologi (hsv//swe)
MEDICAL AND HEALTH SCIENCES  -- Clinical Medicine -- Neurology (hsv//eng)

Nyckelord

Differential diagnosis
hereditary diffuse leukoencephalopathy with spheroids
multiple sclerosis
quantitative MRI
adult
Article
clinical article
controlled study
cross validation
feature selection
female
genetic screening
hereditary diffuse leukodystrophy
human
leave one out cross validation
leukodystrophy
machine learning
male
middle aged
noise
nuclear magnetic resonance imaging
prospective study
quantitative analysis
volumetry

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