Tolonen Antti, Rhodius-Meester Hanneke F M, Bruun Marie, Koikkalainen Juha, Barkhof Frederik, Lemstra Afina W, Koene Teddy, Scheltens Philip, Teunissen Charlotte E, Tong Tong, Guerrero Ricardo, Schuh Andreas, Ledig Christian, Baroni Marta, Rueckert Daniel, Soininen Hilkka, Remes Anne M, Waldemar Gunhild, Hasselbalch Steen G, Mecocci Patrizia, van der Flier Wiesje M, Lötjönen Jyrki
VTT Technical Research Centre of Finland, Tampere, Finland.
Alzheimer Center, Department of Neurology, VU University Medical Center, Amsterdam Neuroscience, Amsterdam, Netherlands.
Front Aging Neurosci. 2018 Apr 25;10:111. doi: 10.3389/fnagi.2018.00111. eCollection 2018.
Clinical decision support systems (CDSSs) hold potential for the differential diagnosis of neurodegenerative diseases. We developed a novel CDSS, the PredictND tool, designed for differential diagnosis of different types of dementia. It combines information obtained from multiple diagnostic tests such as neuropsychological tests, MRI and cerebrospinal fluid samples. Here we evaluated how the classifier used in it performs in differentiating between controls with subjective cognitive decline, dementia due to Alzheimer's disease, vascular dementia, frontotemporal lobar degeneration and dementia with Lewy bodies. We used the multiclass Disease State Index classifier, which is the classifier used by the PredictND tool, to differentiate between controls and patients with the four different types of dementia. The multiclass Disease State Index classifier is an extension of a previously developed two-class Disease State Index classifier. As the two-class Disease State Index classifier, the multiclass Disease State Index classifier also offers a visualization of its decision making process, which makes it especially suitable for medical decision support where interpretability of the results is highly important. A subset of the Amsterdam Dementia cohort, consisting of 504 patients (age 65 ± 8 years, 44% females) with data from neuropsychological tests, cerebrospinal fluid samples and both automatic and visual MRI quantifications, was used for the evaluation. The Disease State Index classifier was highly accurate in separating the five classes from each other (balanced accuracy 82.3%). Accuracy was highest for vascular dementia and lowest for dementia with Lewy bodies. For the 50% of patients for which the classifier was most confident on the classification the balanced accuracy was 93.6%. Data-driven CDSSs can be of aid in differential diagnosis in clinical practice. The decision support system tested in this study was highly accurate in separating the different dementias and controls from each other. In addition to the predicted class, it also provides a confidence measure for the classification.
临床决策支持系统（CDSSs）在神经退行性疾病的鉴别诊断方面具有潜力。我们开发了一种新型CDSS，即PredictND工具，用于不同类型痴呆的鉴别诊断。它整合了从多种诊断测试中获取的信息，如神经心理学测试、磁共振成像（MRI）和脑脊液样本。在此，我们评估了其中使用的分类器在区分主观认知衰退的对照者、阿尔茨海默病所致痴呆、血管性痴呆、额颞叶变性和路易体痴呆方面的表现。我们使用多类疾病状态指数分类器（即PredictND工具所使用的分类器）来区分对照者和患有四种不同类型痴呆的患者。多类疾病状态指数分类器是先前开发的二类疾病状态指数分类器的扩展。与二类疾病状态指数分类器一样，多类疾病状态指数分类器也能对其决策过程进行可视化展示，这使其特别适用于结果可解释性至关重要的医学决策支持。阿姆斯特丹痴呆队列的一个子集，由504名患者（年龄65±8岁，44%为女性）组成，这些患者具有神经心理学测试、脑脊液样本以及自动和视觉MRI定量数据，用于评估。疾病状态指数分类器在将这五类相互区分开方面具有很高的准确性（平衡准确率82.3%）。血管性痴呆的准确率最高，路易体痴呆的准确率最低。对于分类器对分类最有信心的50%的患者，平衡准确率为93.6%。数据驱动的CDSSs在临床实践的鉴别诊断中可能会有所帮助。本研究中测试的决策支持系统在区分不同痴呆和对照者方面具有很高的准确性。除了预测类别外，它还为分类提供了一个置信度度量。
