相关实验视频

Updated: Jul 25, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

预测DWI-FLAIR对NCCT的不匹配:人工智能在超急性决策中的作用

Beom Joon Kim^1,2, Kairan Zhu³, Wu Qiu⁴

¹Department of Neurology, Seoul National University Bundang Hospital, Seongnam-si, Republic of Korea.

Frontiers in neurology

|June 28, 2023

概括

人工智能可以使用非对比计算断层扫描 (NCCT) 扫描来预测扩散加权成像 (DWI) 和流体减弱反转恢复 (FLAIR) 的不匹配. 这种深度学习方法有助于评估急性缺血性中风治疗的资格.

科学领域:

放射学放射学是指放射学
人工智能的人工智能
神经学神经学

背景情况:

扩散加权成像 (DWI) 和流体减弱倒置恢复 (FLAIR) 的不匹配对于确定急性缺血性中风中静脉血栓溶解的资格至关重要.
目前的临床实践受到MRI可用性和主观图像解释的限制.

研究的目的:

开发和评估深度学习 (DL) 模型,用于使用非对比计算断层扫描 (NCCT) 图像预测DWI-FLAIR不匹配.
评估DL辅助的DWI-FLAIR不匹配评估对未经验的神经病学家诊断准确性的影响.

主要方法:

222名急性缺血性中风患者接受了NCCT,DWI和FLAIR成像.
深度学习模型 (nnU-net架构) 经过训练,可以从NCCT图像中预测DWI和FLAIR病变.
没有经验的神经科医生评估了DWI-FLAIR在NCCT上的不匹配,有或没有DL模型的帮助.

主要成果:

DL模型实现了Dice系数为39.1%,DWI病变的体积相关系数为0.76,FLAIR病变的体积相关系数为18.9%和0.61.
来自DL模型的协助提高了经验不丰富的神经病学家对DWI-FLAIR不匹配评估准确度 (AUC-ROC从0.493到0.613),特别是在较大的病变 (≥15mL) 中.

结论:

先进的人工智能技术可以使用NCCT图像有效估计DWI-FLAIR不匹配.
基于DL的预测有望提高中风治疗资格评估的可访问性和准确性.

关键词:

酒后驾驶酒后驾驶酒后驾驶 DWI-FLAIR不匹配的情况一个闪耀的火焰人工智能的人工智能是人工智能.非对比的计算机断层扫描.

更多相关视频

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Automation of the Micronucleus Assay Using Imaging Flow Cytometry and Artificial Intelligence

Published on: January 27, 2023

Disruption of Frontal Lobe Neural Synchrony During Cognitive Control by Alcohol Intoxication

Disruption of Frontal Lobe Neural Synchrony During Cognitive Control by Alcohol Intoxication

Published on: February 6, 2019

相关概念视频

Decision Making

Decision Making

Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...

Reason and Intuition

Reason and Intuition

The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...