BCN Medtech, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, Spain.
German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.
Comput Methods Programs Biomed. 2020 Jun;189:105348. doi: 10.1016/j.cmpb.2020.105348. Epub 2020 Jan 20.
Recently, longitudinal studies of Alzheimer's disease have gathered a substantial amount of neuroimaging data. New methods are needed to successfully leverage and distill meaningful information on the progression of the disease from the deluge of available data. Machine learning has been used successfully for many different tasks, including neuroimaging related problems. In this paper, we review recent statistical and machine learning applications in Alzheimer's disease using longitudinal neuroimaging.
We search for papers using longitudinal imaging data, focused on Alzheimer's Disease and published between 2007 and 2019 on four different search engines.
After the search, we obtain 104 relevant papers. We analyze their approach to typical challenges in longitudinal data analysis, such as missing data and variability in the number and extent of acquisitions.
Reviewed works show that machine learning methods using longitudinal data have potential for disease progression modelling and computer-aided diagnosis. We compare results and models, and propose future research directions in the field.
近年来，针对阿尔茨海默病的纵向研究积累了大量的神经影像学数据。需要新的方法来成功地利用和提取有关疾病进展的有意义的信息，以应对大量可用数据。机器学习已成功应用于许多不同的任务，包括与神经影像学相关的问题。在本文中，我们综述了近年来使用纵向神经影像学数据的阿尔茨海默病的统计和机器学习应用。
我们使用纵向成像数据搜索文献，重点关注阿尔茨海默病，并在四个不同的搜索引擎上搜索 2007 年至 2019 年间发表的文献。
搜索后，我们得到了 104 篇相关文献。我们分析了它们对纵向数据分析中典型挑战的处理方法，例如缺失数据以及采集数量和程度的变化。
综述文献表明，使用纵向数据的机器学习方法具有进行疾病进展建模和计算机辅助诊断的潜力。我们比较了结果和模型，并提出了该领域未来的研究方向。
