科研之友
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摘要
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 Alzheimers 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 Alzheimers disease and controls into their respective groups. Radiologists correctly classified 6595 (median 89; sensitivity/specificity: 88/90) of scans. SVM correctly classified another set of sporadic Alzheimers 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 Alzheimers 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 Alzheimers 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.
- 出版日期2008-11
