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Updated: May 27, 2025

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一个可解释和准确的基于变压器的深度学习模型,用于使用现实世界的儿科数据进行喘息分类.

Beom Joon Kim1, Jeong Hyeon Mun2, Dae Hwan Hwang2

  • 1Department of Pediatrics, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea.

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概括

这项研究介绍了一种基于变压器的AI模型,用于使用呼吸声诊断儿科呼吸系统疾病. 该模型准确地检测出喘息,提供了一个可靠的工具来帮助临床医生在现实世界中实践.

关键词:
人工智能的人工智能分类 分类 分类 分类.深度学习是一种深度学习.呼吸道声音 呼吸道声音喘息,喘息,喘息,喘息,喘息,喘息,喘息,喘息,喘息,喘息,喘息,喘息,喘息,喘息,喘息,喘息

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科学领域:

  • 人工智能在医学中的应用
  • 儿科肺病学 儿科肺病学
  • 生物医学信号处理

背景情况:

  • 使用耳镜进行听觉对于诊断呼吸道疾病至关重要,但依赖于主观解释.
  • 现有的深度学习模型,主要是基于CNN的,与复杂的呼吸声模式作斗争.
  • 需要先进的人工智能客观地解释肺部声音,以改善儿科呼吸道诊断.

研究的目的:

  • 开发和验证人工智能深度学习模型,以准确解释儿科呼吸声.
  • 将音频谱变压器 (AST) 模型应用于临床数据以检测喘息.
  • 将AST模型的性能与基于CNN的模型进行比较,并评估其临床可靠性.

主要方法:

  • 一项涉及韩国两所大学医院 (2019-2020) 儿童的前性研究.
  • 由肺病学家记录儿科呼吸声,并构建一个双盲验证的数据集.
  • 开发了一个深度学习模型,使用预训练的AST模型重量在194个喘息和531个其他呼吸声的数据集上.

主要成果:

  • 基于AST的模型实现了91.1%的准确性,86.6%的AUC,88.2%的精度,76.9%的回忆率和82.2%的F1得分.
  • 该模型在检测儿科呼吸声中的喘息时表现出高准确度.
  • 评分类激活映射 (Score-CAM) 可视化证实了该模型可靠的决策过程.

结论:

  • 基于变压器的AST模型显示,对儿童精确的喘息检测有很大的希望.
  • 这种人工智能模型提供了一个可靠和客观的工具,以支持儿科呼吸道疾病的临床诊断.
  • 开发的AI系统预计将提高诊断儿科呼吸系统疾病在临床环境中的准确性.