Machine Learning for Brain Disorders
Machine Learning for Brain Disorders
复制标题
机器学习治疗脑部疾病
DOI：
10.1007/978-1-0716-3195-9_25
复制
发表时间：
2023
期刊：
影响因子：
--
通讯作者：
Modat M
中科院分区：
文献类型：
--
作者：
Modat M
痴呆症是指由于脑损伤而导致认知和行为障碍的人。痴呆的常见原因包括阿尔茨海默病、血管性痴呆或额颞叶痴呆等。这些病理的发作通常发生在任何临床症状被感知之前至少十年。已经开发了几种生物标志物，以更好地了解前驱期和症状期的疾病进展。这些标记通常来自遗传信息、生物流体、医学图像或临床和认知评估。现在还使用智能设备捕获信息，以进一步了解患者如何受到影响。在过去的二三十年里，研究界做出了巨大的努力，从许多来源获取和共享大量数据。因此，科学文献中提出了许多使用机器学习的方法。这些工具包括用于数据协调的专用工具，作为疾病进展代理的生物标志物的提取，分类工具或创建模拟和帮助预测疾病进展的集中建模工具。然而，到目前为止，很少有方法被转化为临床护理，许多挑战仍然需要解决。
Dementia denotes the condition that affects people suffering from cognitive and behavioral impairments due to brain damage. Common causes of dementia include Alzheimer’s disease, vascular dementia, or frontotemporal dementia, among others. The onset of these pathologies often occurs at least a decade before any clinical symptoms are perceived. Several biomarkers have been developed to gain a better insight into disease progression, both in the prodromal and the symptomatic phases. Those markers are commonly derived from genetic information, biofluid, medical images, or clinical and cognitive assessments. Information is nowadays also captured using smart devices to further understand how patients are affected. In the last two to three decades, the research community has made a great effort to capture and share for research a large amount of data from many sources. As a result, many approaches using machine learning have been proposed in the scientific literature. Those include dedicated tools for data harmonization, extraction of biomarkers that act as disease progression proxy, classification tools, or creation of focused modeling tools that mimic and help predict disease progression. To date, however, very few methods have been translated to clinical care, and many challenges still need addressing.