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相关概念视频

Gross Anatomy of the Liver01:17

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The liver, the largest gland within the human body, is a firm and reddish-brown organ. This wedge-shaped structure weighs approximately 1.5 kg and occupies a significant portion of the right hypochondriac and epigastric regions. It extends more to the right of the body's midline than to the left.
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The liver is an important organ in vertebrates that plays an essential role in metabolism. It is also responsible for storing and redistributing nutrients such as carbohydrates, fats, and vitamins in the body. Additionally, the liver releases bile salts which are critical for digesting food and eliminating toxic metabolites from the body.
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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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在肝移植后使用生成人工智能定义体重轨迹.

Alexis Kim1, Linxi Li2, Reem Hamraz3

  • 1Department of Internal Medicine, Virginia Commonwealth University (VCU).

Liver transplantation : official publication of the American Association for the Study of Liver Diseases and the International Liver Transplantation Society
|December 18, 2025
PubMed
概括

生成型人工智能在肝移植患者中发现了两个主要的体重增加模式. 了解这些轨迹,受MASH肝硬化和男性性别等因素的影响,可以改善患者的护理.

关键词:
人工智能的人工智能是人工智能.人体体重轨迹的轨迹肝硬化是肝硬化的一种疾病.肝移植 肝移植 肝移植风险评估 风险评估 风险评估体重增加 体重增加

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科学领域:

  • 移植医学 移植医学
  • 医疗保健中的人工智能
  • 生物统计学 生物统计学

背景情况:

  • 了解肝移植 (LT) 后体重变化对于患者的治疗结果至关重要.
  • 当前的统计方法可能无法完全捕捉复杂的重量轨迹.
  • 生成型人工智能 (GenAI) 提供了一种新的方法来模拟这些模式.

研究的目的:

  • 应用GenAI来识别和可视化影响LT受体体重轨迹的不可观察 (潜伏) 因素.
  • 为了建模个人体重变化,在LT后的36个月内.
  • 为了将已识别的体重模式与临床参数相关联.

主要方法:

  • 在使用GenAI的562名成年LT接受者中建模了体重轨迹.
  • 将纵向重量数据转换为横截面向量.
  • 进行多变量分析,将潜在因素与临床数据联系起来.

主要成果:

  • 确定了两个主要潜伏因素 (LF1和LF2),解释了LT后99%的体重变化.
  • LF1的特点是早期的快速重量流动和随后的逐渐增加.
  • LF2显示了不同的模式,最初的收益然后是损失,或者最初的损失然后是快速的收益.
  • 马什肝硬化和男性性别是体重增加的重要预测因素.

结论:

  • GenAI成功地确定了LT接受者的关键体重增加轨迹.
  • 这些已识别的模式可以加强临床风险分层和管理策略.
  • 新的统计方法可以揭示复杂的患者数据模式.