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应用数字图像处理和卷积神经网络来检测心声图中的心肌狭窄:临床决策支持
Genilton de França Barros Filho1, José Fernando de Morais Firmino1, Israel Solha1
1Postgraduate Program in Mechanical Engineering, Federal University of Paraíba, João Pessoa 58051-900, PB, Brazil.
数字图像处理和卷积神经网络有助于通过心声图来诊断心筋狭窄. 这些工具有效地区分正常门和狭窄门,支持临床决策.
科学领域:
- 心脏病学
- 医学成像
- 人工智能
背景情况:
- 伴心膜变化,如伴心膜狭窄,是常见的病理变化.
- 关节狭窄会损害门的功能,特别是门完全开放的能力.
研究的目的:
- 应用数字图像处理 (DIP) 并开发卷积神经网络 (CNN) 用于诊断心肌狭窄.
- 通过食管回声图 (TEE) 的数据为专家提供决策支持.
主要方法:
- 获得心声图检查.
- 应用DIP和数据增强技术.
- 开发和培训一个CNN视频分类模型.
主要成果:
- DIP将病例分为没有狭窄 (26.7%),轻度 (26.7%),中度 (13.3%) 和严重 (33.3%).
- 一个CNN模型在视频中分辨出有或没有额头肌狭窄的情况, 准确率达到了92%.
- 由于训练数据不足,CNN模型最初试图分类四个严重程度.
结论:
- DIP和CNN都是有效的工具来区分中狭窄和正常病例.
- DIP证明了对不同程度的狭窄症的分类能力.
- 开发的方法显示出潜在的价值临床决策支持工具,以诊断心肌狭窄.
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