Semi-quantitative scoring schemes like the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) are the most commonly used method in Alzheimer's disease (AD) neuropathology practice. Computational approaches based on machine learning have recently generated quantitative scores for whole slide images (WSIs) that are highly correlated with human derived semi-quantitative scores, such as those of CERAD, for Alzheimer's disease pathology. However, the robustness of such models have yet to be tested in different cohorts. To validate previously published machine learning algorithms using convolutional neural networks (CNNs) and determine if pathological heterogeneity may alter algorithm derived measures, 40 cases from the Goizueta Emory Alzheimer's Disease Center brain bank displaying an array of pathological diagnoses (including AD with and without Lewy body disease (LBD), and / or TDP-43-positive inclusions) and levels of Aβ pathologies were evaluated. Furthermore, to provide deeper phenotyping, amyloid burden in gray matter vs whole tissue were compared, and quantitative CNN scores for both correlated significantly to CERAD-like scores. Quantitative scores also show clear stratification based on AD pathologies with or without additional diagnoses (including LBD and TDP-43 inclusions) vs cases with no significant neurodegeneration (control cases) as well as NIA Reagan scoring criteria. Specifically, the concomitant diagnosis group of AD + TDP-43 showed significantly greater CNN-score for cored plaques than the AD group. Finally, we report that whole tissue computational scores correlate better with CERAD-like categories than focusing on computational scores from a field of view with densest pathology, which is the standard of practice in neuropathological assessment per CERAD guidelines. Together these findings validate and expand CNN models to be robust to cohort variations and provide additional proof-of-concept for future studies to incorporate machine learning algorithms into neuropathological practice.

中文翻译：

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验证机器学习模型以检测跨机构的淀粉样蛋白病变。

半定量计分方案（如建立阿尔茨海默氏病注册机构联盟（CERAD））是阿尔茨海默氏病（AD）神经病理学实践中最常用的方法。基于机器学习的计算方法最近已为整个幻灯片图像（WSI）生成了定量分数，该分数与人类衍生的半定量分数（例如CERAD）在阿尔茨海默氏病病理上的高度相关。但是，此类模型的鲁棒性尚未在不同的队列中进行测试。为了验证使用卷积神经网络（CNN）先前发布的机器学习算法并确定病理异质性是否会改变算法得出的指标，来自Goizueta Emory Alzheimer的40个案例 疾病中心脑库显示出一系列病理诊断（包括伴或不伴路易体病（LBD）和/或TDP-43阳性夹杂物的AD）和Aβ病理水平。此外，为了提供更深的表型，比较了灰质与整个组织的淀粉样蛋白负荷，并且两者的定量CNN得分均与CERAD样得分显着相关。定量评分还显示了基于AD病理的明确分层，无论是否有其他诊断（包括LBD和TDP-43夹杂物）与无明显神经退行性变的病例（对照病例）以及NIA Reagan评分标准。具体而言，AD + TDP-43的伴随诊断组显示有核斑块的CNN评分明显高于AD组。最后，我们报告说，整个组织的计算得分与CERAD样类别的相关性要好于从最密集的病理学领域关注计算得分，这是根据CERAD指南进行神经病理学评估的实践标准。这些发现共同验证并扩展了CNN模型，使其对同类人群的变化具有鲁棒性，并为将机器学习算法纳入神经病理学实践的未来研究提供了额外的概念验证。