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相关概念视频

Synthesis and Regulation of Thyroid Hormones01:20

Synthesis and Regulation of Thyroid Hormones

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Low blood levels of the thyroid hormones — triiodothyronine (T3) and thyroxine (T4) — signal the hypothalamus to release the thyrotropin-releasing hormone (TRH). TRH then reaches the pituitary gland and stimulates the release of thyroid-stimulating hormone(TSH) into the bloodstream.
Upon reaching the thyroid gland, TSH stimulates the follicular cells' active uptake of iodide ions from the blood. The ions diffuse to the apical surface of the cells and are oxidized to iodine. The...
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The Thyroid Gland01:23

The Thyroid Gland

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The thyroid gland is a small, butterfly-shaped gland located in the neck and covers the anterior surface of the trachea. The gland has two lateral lobes connected by a thin tissue mass called the isthmus. Internally, each lobe comprises many small spherical structures known as thyroid follicles, surrounded by a network of blood vessels.
The follicles have a central cavity lined by simple cuboidal to squamous epithelial cells called follicular cells. These cells produce the glycoprotein...
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Functions of Thyroid Hormones01:18

Functions of Thyroid Hormones

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The thyroid hormone (TH) plays a pivotal role in the intricate orchestration of physiological processes, exerting profound effects on development, metabolism, and homeostasis throughout different life stages.
TH is indispensable for the normal development and maturation of the skeletal, muscular, and nervous systems during fetal and childhood growth. It facilitates bone mineral turnover and regulates protein synthesis in developing tissues, contributing significantly to overall growth and...
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相关实验视频

Updated: Jul 6, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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SSC:用于预测甲状腺疾病的新型自堆叠合体模型.

Shengjun Ji1

  • 1School of information, Xi'an University of Finance and Economics, Xi'an, China.

PloS one
|January 3, 2024
PubMed
概括

这项研究引入了一种新的机器学习分类器,用于准确预测甲状腺疾病. 这种先进的技术有效地诊断出各种甲状腺疾病,改进了以前的方法.

科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的人工智能
  • 计算生物学 计算生物学

背景情况:

  • 甲状腺疾病对健康构成重大风险,影响生活质量并增加医疗保健成本.
  • 准确诊断甲状腺疾病是具有挑战性的,特别是对于经验较少的临床医生来说.
  • 机器学习为疾病诊断提供了一个有前途的方法,基于先前的研究.

研究的目的:

  • 开发和验证一种新的,高性能机器学习技术,用于预测甲状腺疾病.
  • 用重新采样方法解决甲状腺疾病预测中不平衡数据集的挑战.
  • 通过先进的计算方法提高甲状腺疾病诊断的准确性和可靠性.

主要方法:

  • 使用UCI甲状腺疾病数据集 (9172个样本,30个特征) 具有高度不平衡的类分布.
  • 采用下行采样重新采样技术来平衡目标类分布.
  • 开发和评估了一种基于随机森林 (RF) 的新型自堆叠分类器,用于检测甲状腺疾病.

主要成果:

  • 拟议的基于射频的自堆叠分类器在诊断原发性甲状腺功能低下症,蛋白结合增加,补偿性甲状腺功能低下症和并发性非甲状腺疾病方面实现了99.5%的准确性.
  • 该模型展示了100%宏精度,100%宏回忆和100%宏F1得分的最先进的性能.

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  • 对各种机器学习分类器,深度神经网络和集体投票分类器进行比较分析证实了拟议方法的可行性,得到K-fold交叉验证的支持.

    • 结论:

    • 基于射频的新型自堆叠分类器为甲状腺疾病预测提供了一种高度有效和准确的方法.
    • 重新采样策略有效地解决了数据不平衡问题,提高了诊断性能.
    • 这种机器学习方法代表了甲状腺疾病计算诊断的重大进步.