Skip to Main content Skip to Navigation
Journal articles

Accuracy of MRI Classification Algorithms in a Tertiary Memory Center Clinical Routine Cohort

Abstract : 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 has 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.
Complete list of metadatas

Cited literature [40 references]  Display  Hide  Download

https://hal.inria.fr/hal-02511532
Contributor : Olivier Colliot <>
Submitted on : Wednesday, March 18, 2020 - 8:06:36 PM
Last modification on : Wednesday, October 14, 2020 - 4:07:15 AM

File

Morin_ClinAD_Classifier_JAD_fi...
Files produced by the author(s)

Identifiers

Citation

Alexandre Morin, Jorge Samper-González, Anne Bertrand, Sebastian Stroer, Didier Dormont, et al.. Accuracy of MRI Classification Algorithms in a Tertiary Memory Center Clinical Routine Cohort. Journal of Alzheimer's Disease, IOS Press, 2020, pp.1-10. ⟨10.3233/JAD-190594⟩. ⟨hal-02511532⟩

Share

Metrics

Record views

342

Files downloads

1204