COVision:用于区分COVID-19与常见肺部疾病的卷积神经网络,使用CT扫描来区分COVID-19和常见肺部疾病
Kush V Parikh1, Timothy J Mathew2
1Troy High School, Troy, MI, USA. kushparikh5@gmail.com.
BMC pulmonary medicine
|November 29, 2023
概括
深度学习模型COVision使用CT扫描和临床数据准确区分COVID-19和肺炎. 这种人工智能工具的性能优于经验丰富的放射科医生,提高了COVID-19检测的诊断准确性.
科学领域:
- 人工智能在医学中的应用
- 医学成像分析 医学成像分析
- 深度学习用于诊断.
背景情况:
- 准确的COVID-19诊断至关重要,特别是在资源有限的环境中.
- 错误诊断率达到26.6%,这是由于CT扫描中的重叠的肺病变出现.
- 现有的深度学习模型难以将COVID-19与其他肺部疾病 (如细菌性肺炎) 区分开来.
研究的目的:
- 开发COVision,这是一种用于准确区分COVID-19和其他肺部疾病的深度学习模型.
- 为了提高诊断特异性,并最大限度地减少模型复杂性和过拟合.
- 通过将CT扫描分析与临床因素相结合,创建一个全面的诊断工具.
主要方法:
- 开发了COVision,包括用于CT扫描的卷积神经网络 (CNN) 和临床因素神经网络 (CFNN).
- 利用联邦平均计算组合CNN和CFNN进行全面的诊断方法.
- 通过减少隐藏层和可训练参数来最大限度地降低模型的复杂性.
主要成果:
- 美国有线电视新闻网 (CNN) 在一个验证集上实现了95.8%的准确性,而CFNN在一个验证集上实现了88.75%的准确性.
- 与三个经验丰富的放射科医生相比,COVision在统计学上表现出显著的优异性能.
- 格拉德-CAM分析显示,COVID-19的外周病变模式与肺炎的中央模式不同.
结论:
- COVision提供了一种高度特定和准确的方法来区分COVID-19和其他肺部疾病,特别是肺炎.
- 该模型的性能超过了经验丰富的放射科医生,表明其潜在的临床实用性.
- COVision分析成像和临床数据的能力为COVID-19提供了强大的诊断解决方案.
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