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Larynx01:21

Larynx

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The human larynx, often referred to as the voice box, is an intricate organ located in the neck. It serves as a pathway for air to enter the lungs during respiration and is an essential component of voice production.
Anatomy of the Larynx
The larynx consists of various components, including cartilage, muscles, and vocal cords. Its structure includes three large unpaired cartilages—the thyroid, cricoid, and epiglottis—and three smaller paired cartilages—the arytenoids,...
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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
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利用机器学习来诊断复杂的声病例.

Ariel Roitman1, Yiftach Edelstain2, Chen Katzir2

  • 1Carmel Medical Center, Department of Otolaryngology - Head and Neck Surgery, Haifa, Israel; The Ruth and Bruce Rappaport Faculty of Medicine, Technion - Israel Institute of Technology, Haifa, Israel.

American journal of otolaryngology
|December 14, 2024
PubMed
概括

机器学习模型现在可以分析语音录音以检测声病理,改进诊断诸如喉 dystonia 和增强患者护理等条件.

关键词:
这是一个HUBERT模型.喉阻塞症 喉阻塞症 喉阻塞症机器学习 机器学习萨尔布鲁肯语音数据库spasmodic dysphonia 性失声症 性失声症 性失声症 性失声症声音障碍 诊断 语音障碍 诊断

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科学领域:

  • 计算语言学计算语言学
  • 医学诊断 医学诊断 医学诊断
  • 医疗保健中的人工智能

背景情况:

  • 传统的声病理诊断依赖于喉科专家的专业知识和直接可视化.
  • 需要补充,可访问的诊断方法.

研究的目的:

  • 开发和评估用于语音分析的机器学习算法.
  • 为了区分健康和的声音.
  • 从语音录音中识别特定的喉疾病.

主要方法:

  • 在HuBERT模型上利用转移学习与萨尔布鲁肯语音数据库.
  • 采用了两阶段的机器学习方法:二元分类 (健康与) 和多类分类 (特定疾病).
  • 分析了2103次会议的数据,包括各种病理和健康个体.

主要成果:

  • 二元分类器在区分健康声音和病态声音方面取得了82%的准确性.
  • 多类分类器在识别特定的喉疾病,特别是喉 dystonia,显示了超过93%的准确性.
  • 喉 dystonia 仍然是一个诊断挑战,突出了人工智能辅助方法的潜力.

结论:

  • 机器学习有效地将语音样本分类为不同的病理.
  • 这种人工智能驱动的方法可以增强患者分拣和简化诊断过程.
  • 这种方法有望改善护理,特别是对于复杂的疾病,如喉 dystonia.