使用语音信号,人口统计数据和结构化医疗记录,人工智能检测喉瘤
Chi-Te Wang1,2,3, Tsai-Min Chen4,5, Nien-Ting Lee2
1Department of Otolaryngology Head and Neck Surgery, Far Eastern Memorial Hospital, Taipei, Taiwan.
The Laryngoscope
|June 12, 2024
概括
人工智能 (AI) 模型可以使用语音信号,人口统计数据和医疗记录来检测喉瘤. 将这些因素结合起来,显著提高了AI的准确性,接近人类在诊断语音障碍方面的表现.
科学领域:
- 耳鼻喉科 耳鼻喉科 耳鼻喉科
- 人工智能在医学中的应用
- 医学诊断 医学诊断 医学诊断
背景情况:
- 对于及时治疗而言,检测喉瘤至关重要.
- 从良性语音障碍区分喉瘤可能是具有挑战性的.
- 人工智能为改善诊断准确性提供了潜力.
研究的目的:
- 调查人工智能模型在检测喉瘤方面的有效性.
- 评估语音信号,人口统计和医疗记录对AI性能的贡献.
- 将人工智能驱动的检测与人类评估进行比较.
主要方法:
- 利用语音记录,人口统计数据和来自囊瘤和良性语音障碍患者的结构化医疗记录的数据集.
- 开发了一种包含卷积神经网络,封闭循环单元和注意层的AI模型.
- 采用十倍交叉验证和外部验证,以进行可靠的绩效评估.
主要成果:
- 结合语音信号,人口统计数据和医疗记录的AI模型实现了0.878.8的AUC.
- 综合人工智能模型表现出高灵敏度 (0.783) 和特异性 (0.816).
- 随着额外的医疗记录,AI检测准确度与人类评估 (82%对83%) 相当.
结论:
- 仅仅是语音信号是AI对囊瘤的分化不够的.
- 人口统计和医疗记录显著提高AI模型的性能.
- 人工智能在接近人类的精度上显示出有前途的潜力,用于大瘤检测.
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