• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文会议>33rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society >Machine Learning classification of MRI features of Alzheimer's disease and mild cognitive impairment subjects to reduce the sample size in clinical trials
【24h】

Machine Learning classification of MRI features of Alzheimer's disease and mild cognitive impairment subjects to reduce the sample size in clinical trials

机译：针对阿尔茨海默氏病和轻度认知障碍受试者的MRI特征的机器学习分类，以减少临床试验中的样本量

获取原文
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

There is a need for objective tools to help clinicians to diagnose Alzheimer's Disease (AD) early and accurately and to conduct Clinical Trials (CTs) with fewer patients. Magnetic Resonance Imaging (MRI) is a promising AD biomarker but no single MRI feature is optimal for all disease stages. Machine Learning classification can address these challenges. In this study, we have investigated the classification of MRI features from AD, Mild Cognitive Impairment (MCI), and control subjects from ADNI with four techniques. The highest accuracy rates for the classification of controls against ADs and MCIs were 89.2% and 72.7%, respectively. Moreover, we used the classifiers to select AD and MCI subjects who are most likely to decline for inclusion in hypothetical CTs. Using the hippocampal volume as an outcome measure, we found that the required group sizes for the CTs were reduced from 197 to 117 AD patients and from 366 to 215 MCI subjects.
机译：需要客观的工具来帮助临床医生尽早准确地诊断出阿尔茨海默氏病（AD），并以更少的患者进行临床试验（CT）。磁共振成像（MRI）是有前途的AD生物标志物，但没有一种MRI功能适用于所有疾病阶段。机器学习分类可以解决这些挑战。在这项研究中，我们用四种技术研究了AD，轻度认知障碍（MCI）和ADNI对照对象的MRI特征分类。针对AD和MCI的对照分类的最高准确率分别为89.2％和72.7％。此外，我们使用分类器选择最有可能拒绝纳入假设CT的AD和MCI受试者。使用海马体积作为结果指标，我们发现CT所需的组大小从197例减少到117例AD患者，从366例减少到215例MCI患者。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号