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相关概念视频

The Effect of Aging on Tissues01:19

The Effect of Aging on Tissues

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Several body functions deteriorate with age. The external signs of aging are easily identifiable. For example, the skin becomes dry, less elastic, and thins out, forming wrinkles. The skin of the face begins to appear looser due to a decrease in the levels of elastic and collagen fibers in the connective tissue. Additionally, melanin production in the hair follicle decreases with age, resulting in gray hair. Moreover, the senses of sight and hearing decline, so glasses and hearing aids may...
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Cognitive Development During Adulthood01:30

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Cognitive development continues throughout adulthood, undergoing significant shifts across early, middle, and late stages. Individual transition occurs from adolescent idealism to pragmatic and adaptable thinking in early adulthood. During this period, individuals learn to integrate personal beliefs with the recognition that other perspectives are equally valid. Exposure to the complexities of modern society, diverse experiences, and higher education contribute to this adaptive thought process,...
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相关实验视频

Updated: May 2, 2026

Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
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Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

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T2AgeNet:一个以文本为导向的框架,具有用于大脑年龄估计的组织特征.

Shengxian Chen, Wenxuan Wu, Xin Zhang

    IEEE transactions on bio-medical engineering
    |December 12, 2025
    PubMed
    概括

    这项研究引入了T2AgeNet,这是一种使用结构性MRI估计大脑年龄的新框架. 该模型实现了准确的年龄预测,并识别了组织特异性异常,增强了神经发育和神经退行检测.

    科学领域:

    • 神经成像是一种神经成像.
    • 人工智能的人工智能
    • 生物医学工程 生物医学工程

    背景情况:

    • 从结构性MRI来估计大脑年龄,有助于检测神经发育和神经退行性疾病.
    • 现有的方法往往缺乏组织水平的特异性,并且无法整合医疗事先的知识.
    • 来自MRI的全球生物标志物无法捕捉局部病理变化.

    研究的目的:

    • 开发一个组织水平的大脑年龄估计框架,整合解剖学和临床信息.
    • 通过结合组织特异性来解决全球生物标志物的局限性.
    • 通过双路图像-文本方法,利用医疗事先的知识.

    主要方法:

    • 提出T2AgeNet,一个双路图像-文本框架,用于局部化大脑年龄预测.
    • 利用大脑MRI细分来生成特定组织的面具,用于局部分析.
    • 综合临床语义,通过将视觉特征与个性化描述对齐,并使用大型语言模型将老化特征转化为文本.

    主要成果:

    • 在各种人群中实现了精确的大脑年龄估计,包括胎儿发育,早产婴儿,阿尔茨海默病和自闭症谱系障碍.
    • 证明了T2AgeNet在OASIS-3和ABIDE-I数据集上识别组织特异性结构异常的能力.
    • 标识异常与已知的神经模式的一致性.

    更多相关视频

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    相关实验视频

    Last Updated: May 2, 2026

    Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly
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    Lesion Explorer: A Video-guided, Standardized Protocol for Accurate and Reliable MRI-derived Volumetrics in Alzheimer's Disease and Normal Elderly

    Published on: April 14, 2014

    40.8K
    Three-Dimensional Shape Modeling and Analysis of Brain Structures
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    Three-Dimensional Shape Modeling and Analysis of Brain Structures

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    结论:

    • T2AgeNet提供了一种用于组织水平大脑年龄估计的新方法,改进了全球生物标志物方法.
    • 该框架有效地整合了解剖学和语义信息,以进行增强的神经成像分析.
    • 通过精确的局部大脑年龄评估,T2AgeNet对神经系统疾病的早期检测和特征表示希望.