Brain Advance Access originally published online on October 3, 2008
Brain 2008 131(11):2969-2974; doi:10.1093/brain/awn239
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Accuracy of dementia diagnosis—a direct comparison between radiologists and a computerized method
1Department of Psychiatry and Psychotherapy and Freiburg Brain Imaging, University Clinic Freiburg, Freiburg, Germany, 2Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London, London, UK, 3Department of Psychiatry and Psychology, Mayo Clinic, Scottsdale, AZ, USA, 4Dementia Research Centre, University College London, Institute of Neurology, London, UK, 5Department of Radiology, Mayo Clinic, Scottsdale, AZ, USA, 6Department of Neuroradiology, Hurstwood Park Neurosciences Centre, Brighton & Sussex Universities Hospital NHS Trust, Haywards Heath, West Sussex, UK, 7Department of Neuroradiology and Department of Neurology, Freiburg Brain Imaging, Neurocenter, University Clinic Freiburg, Freiburg, Germany, 8Department of Radiology, Austin Health, Heidelberg, 9Department of Radiology, University of Melbourne, Melbourne, Australia, 10Department of Radiology, Mayo Clinic, Rochester, USA, 11Département d’études cognitives, Ecole Normale Supérieure, Paris, France and 12Laboratory of Neuroimaging, IRCCS Santa Lucia, Roma, Italy
Correspondence to: Stefan Klöppel, MD, Department of Psychiatry and Psychotherapy, University Clinic Freiburg, Freiburg, Germany E-mail: stefan.kloeppel{at}uniklinik-freiburg.de
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.
Key Words: MRI; diagnosis; dementia; support vector machine
Abbreviations: FTLD, fronto-temporal lobar degeneration; GM, grey matter; MCI, mild cognitive impairment; MMSE, mini mental state examination; NIA-RIA, National Institute on Aging and the Reagan Institute of the Alzheimer's Association; sAD, sporadic Alzheimer's; Disease; SVM, support vector machine
Received April 15, 2008. Revised August 3, 2008. Accepted September 3, 2008.
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