Accuracy of MRI Classification Algorithms in a Tertiary Memory Center Clinical Routine Cohort.
Background: Automated volumetry software (AVS) has recently become widely available to neuroradiologists. MRI volumetry with AVS may support the diagnosis of dementias by identifying regional atrophy. Moreover, automatic classifiers using machine learning techniques have recently emerged as promisin...
| Publicado en: | Journal of Alzheimer's Disease Vol. 74; no. 4; pp. 1157 - 1167 |
|---|---|
| Autores principales: | , , , , , , , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2020
|
| 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=142832381&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142832381 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13872877 FLR jtl: Journal of Alzheimer's Disease issn: 13872877 maglogo: N pubinfo: dt: 2020 vid: 74 iid: 4 pid: 20732 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 142832381 142832381 NLM32144978 142832381 10.3233/JAD-190594 NLM32144978 142832381 ppf: 1157 ppct: 10 formats: tig: atl: Accuracy of MRI Classification Algorithms in a Tertiary Memory Center Clinical Routine Cohort. aug: au: Morin, Alexandre Samper-Gonzalez, Jorge Bertrand, Anne Ströer, Sébastian Dormont, Didier Mendes, Aline Coupé, Pierrick Ahdidan, Jamila Lévy, Marcel Samri, Dalila Hampel, Harald Dubois, Bruno Teichmann, Marc Epelbaum, Stéphane Colliot, Olivier affil: Department of Neurology, AP-HP, Hôpital de la Pitié-Salpêtrière, Unité de Neuro-Psychiatrie Comportementale (UNPC), Paris, France sug: subj: Cognition Disorders Magnetic Resonance Imaging Classification Image Interpretation, Computer Assisted Methods Brain Neuroradiography Classification Classification Algorithms Dementia Diagnosis Alzheimer's Disease Dementia Middle Age Male Alzheimer's Disease Diagnosis Cognition Disorders Diagnosis Retrospective Design Aged Reproducibility of Results Female Algorithms Software Human Comparative Studies Multicenter Studies Evaluation Research Validation Studies Middle Aged: 45-64 years Aged: 65+ years Male Female ab: Background: Automated volumetry software (AVS) has recently become widely available to neuroradiologists. MRI volumetry with AVS may support the diagnosis of dementias by identifying regional atrophy. Moreover, automatic classifiers using machine learning techniques have recently emerged as promising approaches to assist diagnosis. However, the performance of both AVS and automatic classifiers have been evaluated mostly in the artificial setting of research datasets.Objective: Our aim was to evaluate the performance of two AVS and an automatic classifier in the clinical routine condition of a memory clinic.Methods: We studied 239 patients with cognitive troubles from a single memory center cohort. Using clinical routine T1-weighted MRI, we evaluated the classification performance of: 1) univariate volumetry using two AVS (volBrain and Neuroreader™); 2) Support Vector Machine (SVM) automatic classifier, using either the AVS volumes (SVM-AVS), or whole gray matter (SVM-WGM); 3) reading by two neuroradiologists. The performance measure was the balanced diagnostic accuracy. The reference standard was consensus diagnosis by three neurologists using clinical, biological (cerebrospinal fluid) and imaging data and following international criteria.Results: Univariate AVS volumetry provided only moderate accuracies (46% to 71% with hippocampal volume). The accuracy improved when using SVM-AVS classifier (52% to 85%), becoming close to that of SVM-WGM (52 to 90%). Visual classification by neuroradiologists ranged between SVM-AVS and SVM-WGM.Conclusion: In the routine practice of a memory clinic, the use of volumetric measures provided by AVS yields only moderate accuracy. Automatic classifiers can improve accuracy and could be a useful tool to assist diagnosis. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|