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一个8分尺度的肺超声波评分网络融合了当地细节和全球特征

Yonghua Chu¹, Xiang Luo², Jucheng Zhang^3,4

¹Department of Clinical Engineering, School of Medicine, The Second Affiliated Hospital, Zhejiang University, Hangzhou, 310009, China.

Scientific reports

|February 16, 2025

概括

一个自动化肺超声波 (LUS) 评分网络,DGF-ResNet,可以减少主观的解释错误. 这种人工智能模型提高了肺透气监测和早期疾病检测的诊断准确度.

科学领域:

医疗成像医学成像
人工智能的人工智能
肺部病理学肺部病理学

背景情况:

手动肺超声波 (LUS) 评分是主观的,容易出现不一致.
不同的临床医生经验可能导致肺透气评估中的错误诊断.
需要客观和自动化的方法来改善LUS解释.

研究的目的:

开发一个自动化的肺超声波评分网络,详细的全球融合残留网络 (DGF-ResNet).
使用人工智能提高肺透气监测的准确性和一致性.
通过客观的LUS分析来支持肺部疾病的早期诊断.

主要方法:

提出了DGF-ResNet,一个8点级的自动化LUS评分网络.
实现了混合特征融合块,结合了细节特征提取 (DFE) 和全局特征提取 (GFE) 模块.
DFE使用局部注意力;GFE使用递归封闭卷积和全球注意力来提取特征.

主要成果:

与VGG16,ResNet50和视觉转换器相比,DGF-ResNet表现出了更好的表现.
在准确性,精度,回忆和F1分数方面取得了显著的改进.
比视觉变压器的精度高出7.05%,比VGG16的精度高出3.06%,比ResNet50的精度高出2.05%.

结论:

该DGF-ResNet提供了一个强大的和自动化的解决方案,用于LUS评分.
自动化LUS评分可以减轻临床实践中的主观解释问题.
这种人工智能方法有可能提高呼吸系统医学中的诊断准确性.

关键词:

自动肺部得分自动肺部得分肺部超声波肺部超声波肺部超声波递归门式卷曲是指回归式的门式卷曲.一个重复网的重复网.

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