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识别患者性别:使用儿童和青少年心脏声音进行机器学习初步分析
1Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA, USA. rmcarri@emory.edu.
Pediatric cardiology
|June 27, 2024
概括
机器学习模型可以使用心脏声音记录来分类性别,肺部听力显示最高准确度. 这项研究探讨了医疗AI使用生理声音的潜在偏见.
科学领域:
- 人工智能的人工智能
- 生物医学工程 生物医学工程
- 心脏病学 心脏病学
背景情况:
- 以前的研究表明,人工智能能够从医疗图像中对人口统计学进行分类,这引发了对偏见的担忧.
- 生理学声音的潜力,特别是心脏声音,推断社会人口统计学特征仍然在很大程度上未被探索.
- 调查这一领域对于确保公平性和了解医疗AI应用中的潜在偏见至关重要.
研究的目的:
- 开发和评估机器学习 (ML) 分类器,以从心电图 (心脏声音) 记录中确定性别 (男性/女性).
- 评估不同的听觉点和特征提取方法的有效性,以进行性别分类.
主要方法:
- 使用了在巴西进行心脏查的开放访问CirCor DigiScope声心图数据集.
- 从四个听觉点 (大动脉, mitra,肺,三) 的心脏声音记录中提取了Mel频率的头角系数 (MFCC).
- 开发了ML分类器,并使用接收器运行特征曲线下的面积 (AUC ROC) 评估了它们的性能.
主要成果:
- 使用肺听 (PV) 记录开发的性别分类器获得了最高的性能,AUC ROC为70.3%.
- 关节 (MV) 记录产生了第二个最佳分类器 (AUC ROC 58.8%),而大动脉 (AV) 和三管 (TV) 记录显示出较低的性能.
- 提取的MFCC数量的增加并没有显著提高分类准确性.
结论:
- 使用心电图数据和机器学习来分类性别是可行的.
- 随着ML应用的扩大,需要进一步的研究来调查与健康相关的音频录音中社会人口统计信号的存在.
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