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相关实验视频

Updated: Jul 26, 2025

A Swin Transformer-Based Model for Thyroid Nodule Detection in Ultrasound Images
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对用于检测甲状腺疾病的人工智能技术进行系统审查.

Lerina Aversano1, Mario Luca Bernardi1, Marta Cimitile2

  • 1Department of Engineering, University of Sannio, Benevento, Italy.

PeerJ. Computer science
|June 22, 2023
PubMed
概括

人工智能 (AI) 有助于早期发现甲状腺疾病,实现高精度. 然而,挑战包括使用私有,过时的数据集以及需要在甲状腺健康的AI应用中使用多种数据类型.

关键词:
人工智能的人工智能是人工智能.深度学习是一种深度学习.检测 检测 检测 检测 检测机器学习是机器学习.甲状腺疾病是一种甲状腺疾病.

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科学领域:

  • 医疗信息学 医疗信息学
  • 医疗保健中的人工智能

背景情况:

  • 人工智能 (AI) 在医疗保健中的应用正在扩大,早期疾病检测是主要重点.
  • 甲状腺疾病,如甲状腺功能低下症和甲状腺功能过高症,从早期诊断中显著受益,以获得有效的治疗和降低医疗保健成本.

研究的目的:

  • 系统地审查和分析用于检测和识别甲状腺疾病的人工智能技术.
  • 将审查的贡献分类,突出该领域最近研究的优缺点.

主要方法:

  • 进行了系统的文献审查,选择和分析了72篇论文.
  • 根据三个研究问题分析了论文:检测到的甲状腺疾病,使用的数据集和使用的数据类型.
  • 用各种观点和分类学来对贡献进行分类.

主要成果:

  • 大多数审查的论文使用监督学习方法来检测甲状腺功能低下症和甲状腺功能过强症.
  • 报告的平均检测准确度很高,为96.84%.
  • 常见的问题包括使用私有,过时的数据集,主要包括临床数据.

结论:

  • 人工智能对准确检测甲状腺疾病,特别是对低甲状腺和甲状腺功能障碍的高潜力.
  • 未来的研究应该解决当前数据集的局限性,并探索人工智能驱动的甲状腺诊断的各种数据模式.
  • 需要进一步开发以优化人工智能应用程序,以便更广泛地检测和管理甲状腺疾病.