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Protocol and Guidelines for Point-of-Care Lung Ultrasound in Diagnosing Neonatal Pulmonary Diseases Based on International Expert Consensus
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使用双树复杂波纹转换对新生儿肺部病理的分类.

Sagarjit Aujla1, Adel Mohamed2, Ryan Tan3

  • 1Department of Electrical, Computer, and Biomedical Engineering, Toronto Metropolitan University, 350 Victoria Street, Toronto, ON, M5B 2K3, Canada. s1aujla@torontomu.ca.

Biomedical engineering online
|December 4, 2023
PubMed
概括

这项研究引入了一种自动化系统,用于使用肺超声波 (LUS) 图像来诊断新生儿肺病理. 开发的框架显著提高了诊断准确性,帮助发展中国家的临床医生.

关键词:
图像分析 图像分析新生儿肺部超声波新生儿肺部超声波模式分类模式的分类.波纹分解 波纹分解

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 新生儿护理 新生儿护理

背景情况:

  • 未经诊断的新生儿肺部病理导致发展中国家的显著死亡率.
  • 肺部超声波 (LUS) 是一种安全和便携式的诊断工具,但在资源有限的环境中缺乏训练有素的口译人员.
  • 自动化LUS解释可以实现快速诊断和改善结果.

研究的目的:

  • 从LUS图像开发一个自动化的框架来分类常见的新生儿肺病理.
  • 解决发展中国家由于缺乏训练有素的临床医生而面临的诊断挑战.

主要方法:

  • 利用二维双树复杂波波变换 (DTCWT) 来从LUS图像中提取空间和纹理模式.
  • 使用线性差异分析 (LDA) 分类了六种常见的新生儿肺部病理.
  • 平衡了42名新生儿1550张LUS图像的数据集,以防止阶级偏见.

主要成果:

  • 在不平衡的数据上实现了每图像74.39%的精度,在平衡的数据集上改进到92.78%.
  • 在不平衡的数据中,每人准确率达到了64.97%,在平衡的数据集中增加到81.53%.
  • 证明了DTCWT和LDA在分类新生儿肺病理方面的有效性.

结论:

  • 拟议的框架可以自动化新生儿肺病理学诊断使用LUS.
  • 自动化LUS解释可以减少新生儿死亡率在受过训练的医疗专业人员有限的地区.
  • 这项技术有可能改善发展中国家的新生儿卫生保健.