识别儿科心脏声和区分无辜的病态使用深度学习
George Zhou1, Candace Chien2, Justin Chen3
1Weill Cornell Medicine, New York, NY 10021, USA.
Artificial intelligence in medicine
|May 9, 2024
概括
这项研究引入了新的深度学习算法来对儿科心脏声音进行分类,在区分正常声音和无辜和病态声方面达到高准确度. 这些先进的方法优于当前的标准,提供了更好的诊断潜力.
科学领域:
- 人工智能的人工智能
- 心脏病学 心脏病学
- 医疗成像医学成像
背景情况:
- 准确地对儿科心脏声音进行分类对于及时诊断和治疗至关重要.
- 目前用于心脏声音分析的方法往往缺乏复杂儿科病例所需的精度.
- 深度学习为分析心脏声音的微妙变化提供了提高准确性的潜力.
研究的目的:
- 开发和评估用于多类分类儿科心脏声音的深度学习算法.
- 使用先进的AI技术,区分正常的心声,无辜的声和病态的声.
- 为了比较新的视觉变压器模型与传统的卷积神经网络 (CNN) 方法的性能.
主要方法:
- 利用了儿科心脏声音的数据集,包括正常,无辜和病态的声,加上公开数据.
- 使用在格拉米安角场 (GAF) 和马尔科夫过渡场 (MTF) 图像表示上训练的视觉转换器开发了两种新的方法.
- 视觉变压器模型与光谱图像上训练的ResNet-50 CNN进行了比较.
主要成果:
- 视觉变压器模型在所有测试的图像表示中始终优于ResNet-50 CNN.
- 格拉米安角场 (GAF) 表示表现为儿科心脏声音分类表现出了卓越的性能.
- 最好的模型实现了高的曲线下面面积 (AUC) 值:正常声音为0.92,无辜语为0.83,病理语为0.88.
结论:
- 新的深度学习方法,特别是带GAF的视觉转换器,显著提高了儿科心脏声音分类的准确性.
- 这项研究首次展示了使用深度学习对儿科语进行多类分类的演示.
- 开发的模型提供了一个更易于解释和解释的方法,可能会增加临床医生的信任和在临床实践中的采用.
相关概念视频
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Auscultation is the process of listening to these internal body sounds using a stethoscope. The heart produces four types of sounds, but only two—S1 and S2—can usually be heard with a stethoscope.
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