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【Brain】计算机应用技术有望在痴呆诊断中进一步
Accuracy of dementia diagnosis—a direct comparison between radiologists and a computerized method
Correspondence to: Stefan Klöppel, MD, Department of Psychiatry and Psychotherapy, University Clinic Freiburg, Freiburg, Germany E-mail: stefan.kloeppel@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. 本人认领 48小时内贴出译文
Accuracy of dementia diagnosis—a direct comparison between radiologists and a computerized method
精确诊断痴呆：影像学与计算机应用技术的比较
Correspondence to: Stefan Klöppel, MD, Department of Psychiatry and Psychotherapy, University Clinic Freiburg, Freiburg, Germany E-mail: stefan.kloeppel@uniklinik-freiburg.de
There has been recent interest in the application of machine learning techniques to neuroimaging-based diagnosis.
最近比较热门的研究室在神经影像诊断中引入计算机智能技术。
These methods promise fully automated, standard PC-based clinical decisions, unbiased by variable radiological expertise.
此技术可以在各种影像专家意见基础上客观地提供全自动标准化临床决策。
We recently used support vector machines (SVMs) to separate sporadic Alzheimer's disease from normal ageing and from fronto-temporal lobar degeneration (FTLD).
我们应用向量支持软件系统（SVMs）从正常老化和额颞叶变性中筛查散发的老年性痴呆病例。
In this study, we compare the results to those obtained by radiologists.
在此项研究中把从神经影像学中获得结果与之相比较。
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.
并由工作经验和水平各不相同的六位神经影像学专家就SVM分析病例的相关影像给出二元的诊断意见。
SVMs correctly classified 95% (sensitivity/specificity: 95/95) of sporadic Alzheimer's disease and controls into their respective groups.
SVMs正确诊断了95%的散发性老年性痴呆（敏感度/特异度：95/95）
Radiologists correctly classified 65–95% (median 89%; sensitivity/specificity: 88/90) of scans.
神经影像医生的诊断正确率65-95%（平均89%；敏感度/特异度：88/90）
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).
SVM在另一组中散发性老年性痴呆的诊断正确率为93%（敏感度/特异度：100/86）而神经影像医生的正确率在80-90%之间（平均83%；敏感度/特异度：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).
SVMs在从额颞叶变性中筛选散发性老年性痴呆的准确率更高（SVM 89%;敏感度/特异度：83/95；影像学63% - 83%;平均71%；敏感度/特异度：64/76）
Radiologists were always accurate when they reported a high degree of diagnostic confidence.
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作者:admin@医学,生命科学 2011-02-17 18:01
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