基于转移学习的肺超声波B线评估急性心力衰竭的肺超声波
Joseph R Pare1, Lars A Gjesteby2, Melinda Tonelli3
1Alpert Medical School of Brown University, Providence, RI, USA; Lifespan, Providence, RI, USA; Providence VA Medical Center, Providence, RI, USA; Boston University, Boston, MA, USA.
Ultrasound in medicine & biology
|February 29, 2024
概括
使用转移学习的AI工具可以准确地解释肺超声波 (LUS) B线,通过自动化这一关键评估来帮助新手用户诊断急性心力衰竭 (AHF).
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 心脏病学 心脏病学
背景情况:
- 肺部超声波 (LUS) 是有效的诊断急性心力衰竭 (AHF),超过传统方法.
- 目前LUS的使用仅限于受过培训的专业人士,面临解释一致性的挑战.
研究的目的:
- 利用转移学习开发一个支持人工智能的软件,以自动化LUS B-line解释.
- 帮助医疗保健专业人员,特别是新手,对AHF诊断进行客观B线评估.
主要方法:
- 一个卷积神经网络 (CNN) 在30名患者的49,952个LUS上受过训练.
- 人工智能模型在60个独特患者的476个LUS剪辑上进行了评估.
- 人工智能通过二进制和0-4多类评估评估了B线.
主要成果:
- 在二进制分类中,人工智能算法在检测病理状况时获得了0.967的AUC.
- 多分类人工智能尺度与专家评审者强烈一致,其线性加权kappa为0.839.
结论:
- 开发的AI算法在AHF的B线评估中表现出强的性能.
- 这种人工智能工具有可能通过提供定量和客观的LUS评估来增强临床工作流程.
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