Journal Article

Accuracy of dementia diagnosis—a direct comparison between radiologists and a computerized method

Stefan Klöppel, Cynthia M. Stonnington, Josephine Barnes, Frederick Chen, Carlton Chu, Catriona D. Good, Irina Mader, L. Anne Mitchell, Ameet C. Patel, Catherine C. Roberts, Nick C. Fox, Clifford R. Jack, John Ashburner and Richard S. J. Frackowiak

in Brain

Published on behalf of The Guarantors of Brain

Volume 131, issue 11, pages 2969-2974
Published in print November 2008 | ISSN: 0006-8950
Published online October 2008 | e-ISSN: 1460-2156 | DOI:

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There has been recent interest in the application of machine learning techniques to neuroimaging-based diagnosis. These methods promise fully automated, standard PC-based clinical decisions, unbiased by variable radiological expertise. We recently used support vector machines (SVMs) to separate sporadic Alzheimer's disease from normal ageing and from fronto-temporal lobar degeneration (FTLD). In this study, we compare the results to those obtained by radiologists. A binary diagnostic classification was made by six radiologists with different levels of experience on the same scans and information that had been previously analysed with SVM. SVMs correctly classified 95% (sensitivity/specificity: 95/95) of sporadic Alzheimer's disease and controls into their respective groups. Radiologists correctly classified 65–95% (median 89%; sensitivity/specificity: 88/90) of scans. SVM correctly classified another set of sporadic Alzheimer's disease in 93% (sensitivity/specificity: 100/86) of cases, whereas radiologists ranged between 80% and 90% (median 83%; sensitivity/specificity: 80/85). SVMs were better at separating patients with sporadic Alzheimer's disease from those with FTLD (SVM 89%; sensitivity/specificity: 83/95; compared to radiological range from 63% to 83%; median 71%; sensitivity/specificity: 64/76). Radiologists were always accurate when they reported a high degree of diagnostic confidence. The results show that well-trained neuroradiologists classify typical Alzheimer's disease-associated scans comparable to SVMs. However, SVMs require no expert knowledge and trained SVMs can readily be exchanged between centres for use in diagnostic classification. These results are encouraging and indicate a role for computerized diagnostic methods in clinical practice.

Keywords: MRI; diagnosis; dementia; support vector machine

Journal Article.  3538 words.  Illustrated.

Subjects: Neurology ; Neuroscience

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