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相关概念视频

Cushing Syndrome I: Introduction01:26

Cushing Syndrome I: Introduction

Cushing syndrome refers to the collection of clinical manifestations that arise when tissues are exposed to excessive amounts of cortisol or cortisol-like medications over an extended period. Cortisol, a glucocorticoid produced by the adrenal cortex, regulates metabolism, immune responses, and the body’s adaptation to stress. When its concentration remains chronically elevated, these physiological pathways become dysregulated, resulting in the characteristic features of the syndrome.Exogenous...
Cushing Syndrome II: Pathophysiology01:19

Cushing Syndrome II: Pathophysiology

Cortisol production is normally governed by the hypothalamic–pituitary–adrenal (HPA) axis, which maintains hormonal balance through tightly regulated feedback mechanisms. Disruption of this regulatory system is central to the development of Cushing syndrome, whether the excess cortisol originates from external medications or internal pathology. Persistent cortisol elevation alters metabolism, immune function, and endocrine signaling, producing the characteristic clinical features of the...

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机器学习辅助查犬类库辛综合征

Young-Jae Yoo1, Kyungchang Jeong2, Hanbit Seo2

  • 1Laboratory of Veterinary Internal Medicine, College of Veterinary Medicine, Chungbuk National University, Cheongju, Republic of Korea.

The veterinary quarterly
|December 23, 2025
PubMed
概括

机器学习有助于使用常规测试对犬类库辛综合征 (CS) 的诊断. 这种方法实现了88.5%的准确性,提高了这种常见的内分泌疾病的诊断效率.

关键词:
人工智能的人工智能是人工智能.提升了树的树.狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗狗梯度增强可以提高梯度.过高皮质醇主义是什么?其他非上腺疾病过多阴症 (polydipsia) 是一种多阴症.多尿症多尿症是什么意思

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科学领域:

  • 兽医医学 兽医医学 兽医医学
  • 内分泌学 在内分泌学.
  • 机器学习在诊断中的应用

背景情况:

  • 库辛综合征 (Cushing's syndrome,简称CS) 是狗中普遍存在的内分泌疾病,常常呈现出不同的临床症状.
  • 正确诊断CS是具有挑战性的,因为其复杂的呈现,阻碍及时干预.
  • 当前的诊断途径可能是资源密集型的,并且可能并不总是确定适合高级测试的候选人.

研究的目的:

  • 开发和验证一种机器学习模型,以协助诊断犬类库辛综合征.
  • 为了提高诊断准确性,利用常规可用的查诊断.
  • 提高兽医实践中库辛综合征诊断的效率和可访问性.

主要方法:

  • 一个增强树算法 (梯度增强) 被训练在一个包含全血细胞计,血清化学面板和尿液分析参数的数据集上.
  • 数据包括153只对照犬和152只被证实患有库辛综合征的狗.
  • 该模型对80%的数据进行了训练,并对剩余的20%进行了验证.

主要成果:

  • 机器学习模型的整体准确率为88.5% (95% CI:80.5-96.5%).
  • 敏感度为83.3% (95% CI: 70.7-96.7%) 和特异性为93.5% (95% CI: 84.9-100%).
  • 接收器操作特征曲线下的面积为0.912 (95% CI:0.835-0.988),表明了出色的诊断区分能力.

结论:

  • 机器学习算法可以有效地帮助使用标准查诊断来诊断犬类库辛综合征.
  • 开发的模型表现出高精度和差异化能力,为兽医提供了宝贵的工具.
  • 实施用户友好的界面可以提高诊断效率,并可能提高业主满意度.