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Radiological Classification Of Dementia From Anatomical MRI Assisted By Machine Learning-derived Maps.

P. Chagué, B. Marro, S. Fadili, M. Houot, A. Morin, Jorge Samper-González, Paul Beunon, L. Arrivé, D. Dormont, B. Dubois, M. Teichmann, S. Epelbaum, O. Colliot
Published 2020 · Medicine

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BACKGROUND AND PURPOSE Many artificial intelligence tools are currently being developed to assist diagnosis of dementia from magnetic resonance imaging (MRI). However, these tools have so far been difficult to integrate in the clinical routine workflow. In this work, we propose a new simple way to use them and assess their utility for improving diagnostic accuracy. MATERIALS AND METHODS We studied 34 patients with early-onset Alzheimer's disease (EOAD), 49 with late-onset AD (LOAD), 39 with frontotemporal dementia (FTD) and 24 with depression from the pre-existing cohort CLIN-AD. Support vector machine (SVM) automatic classifiers using 3D T1 MRI were trained to distinguish: LOAD vs Depression, FTD vs LOAD, EOAD vs Depression, EOAD vs FTD. We extracted SVM weight maps, which are tridimensional representations of discriminant atrophy patterns used by the classifier to take its decisions and we printed posters of these maps. Four radiologists (2 senior neuroradiologists and 2 unspecialized junior radiologists) performed a visual classification of the 4 diagnostic pairs using 3D T1 MRI. Classifications were performed twice: first with standard radiological reading and then using SVM weight maps as a guide. RESULTS Diagnostic performance was significantly improved by the use of the weight maps for the two junior radiologists in the case of FTD vs EOAD. Improvement was over 10 points of diagnostic accuracy. CONCLUSION This tool can improve the diagnostic accuracy of junior radiologists and could be integrated in the clinical routine workflow.
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