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Applying automated MR-based diagnostic methods to the memory clinic: A prospective study

机译:将基于mR的自动诊断方法应用于记忆诊所:一项前瞻性研究

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摘要

Several studies have demonstrated that fully automated pattern recognition methods applied to structural magnetic resonance imaging (MRI) aid in the diagnosis of dementia, but these conclusions are based on highly preselected samples that significantly differ from that seen in a dementia clinic. At a single dementia clinic, we evaluated the ability of a linear support vector machine trained with completely unrelated data to differentiate between Alzheimer’s disease (AD), frontotemporal dementia (FTD), Lewy body dementia, and healthy aging based on 3D-T1 weighted MRI data sets. Furthermore, we predicted progression to AD in subjects with mild cognitive impairment (MCI) at baseline and automatically quantified white matter hyperintensities from FLAIR-images. Separating additionally recruited healthy elderly from those with dementia was accurate with an area under the curve (AUC) of 0.97 (according to Fig. 4). Multi-class separation of patients with either AD or FTD from other included groups was good on the training set (AUC >  0.9) but substantially less accurate (AUC = 0.76 for AD, AUC = 0.78 for FTD) on 134 cases from the local clinic. Longitudinal data from 28 cases with MCI at baseline and appropriate follow-up data were available. The computer tool discriminated progressive from stable MCI with AUC = 0.73, compared to AUC = 0.80 for the training set. A relatively low accuracy by clinicians (AUC = 0.81) illustrates the difficulties of predicting conversion in this heterogeneous cohort. This first application of a MRI-based pattern recognition method to a routine sample demonstrates feasibility, but also illustrates that automated multi-class differential diagnoses have to be the focus of future methodological developments and application studies
机译:多项研究表明,应用于结构磁共振成像(MRI)的全自动模式识别方法有助于痴呆症的诊断,但是这些结论是基于高度预选的样本,这些样本与痴呆症诊所的样本明显不同。在单个痴呆诊所中,我们评估了基于完全无关数据训练的线性支持向量机基于3D-T1加权MRI区分阿尔茨海默氏病(AD),额颞叶痴呆(FTD),路易体痴呆和健康衰老的能力数据集。此外,我们在基线时预测患有轻度认知障碍(MCI)的受试者的AD进展,并根据FLAIR图像自动定量白质高信号。将新招募的健康老人与痴呆症患者分开是准确的,曲线下面积(AUC)为0.97(根据图4)。在训练组中,将AD或FTD患者与其他纳入组进行多类分离是好的(AUC> 0.9),但准确性较差(ADC的AUC = 0.76,FTD的AUC = 0.78),来自当地诊所的134例患者。现有28例MCI基线的纵向数据和适当的随访数据。与训练集的AUC = 0.80相比,计算机工具从AUC = 0.73的稳定MCI区分渐进。临床医生相对较低的准确性(AUC = 0.81)说明了预测这一异质队列中转化的困难。基于MRI的模式识别方法在常规样本中的首次应用证明了可行性,但也说明了自动化的多类差异诊断必须成为未来方法学发展和应用研究的重点

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