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一个由人工智能支持的无偏见的呼吸系统疾病诊断模型,使用咳音频
Tabish Saeed1, Aneeqa Ijaz1, Ismail Sadiq2
1AI4Networks Research Center, Department of Electrical & Computer Engineering, University of Oklahoma, Tulsa, OK 74135, USA.
Bioengineering (Basel, Switzerland)
|January 22, 2024
概括
来自咳的呼吸道疾病诊断的人工智能 (AI) 模型可能会受到混变量的偏差. 拟议的无偏见网络 (RBF-Net) 有效地减轻了这些偏见,提高了COVID-19等疾病的诊断准确性.
科学领域:
- 医疗人工智能 医疗人工智能
- 呼吸道疾病的诊断 呼吸道疾病的诊断
- 医疗保健中的机器学习
背景情况:
- 人工智能驱动的咳分析显示了呼吸系统疾病 (RD) 诊断的前景.
- 现有的模型往往忽略了混变量 (例如性别,年龄,吸烟状况),导致偏见的预测.
- 这种偏差可以显著扭曲模型性能和可靠性.
研究的目的:
- 引入无偏见网络 (RBF-Net),这是一个AI模型,旨在减轻基于咳的RD诊断中的混变量.
- 通过解决数据分布挑战,提高AI诊断工具的准确性和可靠性.
- 通过COVID-19数据集验证RBF-Net的有效性.
主要方法:
- 开发了RBF-Net,这是一个端到端的解决方案,集成卷积神经网络 (CNN) 和长短期记忆 (LSTM) 来进行特征提取.
- 整合了一个有条件的生成对抗网络 (c-GAN) 带有偏差预测器,以从 RD 预测中解解相关的混因素.
- 在不平衡的COVID-19数据集上训练和评估模型,这些数据集来自大规模的咳数据集.
主要成果:
- 对于有偏见的培训数据,RBF-Net 显示出强度.
- 测试的准确度达到84.1% (性别),84.6% (年龄) 和80.5% (吸烟状态).
- 在相应的混变量中,其表现优于最先进的CNN-LSTM模型的5.5%,7.7%和8.2%.
结论:
- 在基于AI的咳诊断中,RBF-Net有效地减少了混变量的影响.
- 拟议的方法显著提高了呼吸道疾病检测模型的可靠性和准确性.
- RBF-Net为医疗保健中公正的AI诊断提供了一个有前途的方法.
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