一个基于人工智能的非侵入性系统,用于精确地分类COVID-19中的无氧化物,使用神经成像
Hossam Magdy Balaha1, Mayada Elgendy2, Ahmed Alksas1
1Department of Bioengineering, J.B. Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA.
Heliyon
|July 8, 2024
概括
这项研究开发了一种使用MRI和AI的计算机辅助诊断系统,以检测COVID-19相关的厌氧症严重程度. 该系统准确地分类嗅觉功能障碍,帮助临床护理决策.
科学领域:
- 医学成像和人工智能 医学成像和人工智能
- 神经学和神经科学 神经学和神经科学
- 传染病与公共卫生问题
背景情况:
- COVID-19 (冠状病毒疾病) 是由SARS-CoV-2引起的急性呼吸道疾病.
- 嗅觉和味觉功能障碍是COVID-19感染的常见症状.
- 准确评估厌氧症的严重程度对于患者的管理至关重要.
研究的目的:
- 开发一种计算机辅助诊断 (CAD) 系统,用于分类COVID-19相关的厌氧症.
- 在COVID-19患者中区分正常,轻度和严重的嗅觉功能障碍.
- 利用先进的成像和机器学习技术进行客观的厌氧评估.
主要方法:
- 使用FLAIR-MRI和DTI从嗅觉神经中提取外观,形态 (球体波) 和扩散性 (微分异型和平均扩散性) 标记.
- 实施CAD系统,包括嗅觉神经识别,标记器提取和融合以及基于分类器的决策.
- 集成机器学习分类器,以多数投票和堆叠来诊断嗅灯泡 (OB) 厌氧症.
主要成果:
- 在多种验证方法中,CAD系统实现了高性能:五倍 (准确率94.1%,BAC92.18%),十倍 (准确率94.43%,BAC93.0%) 和Leave-One-Subject-Out (LOSO) (准确率91.6%,BAC90.27%).
- 组合方法,包括堆叠和多数投票,显著改善了诊断性能.
- 该系统在分类嗅觉功能障碍严重程度方面表现出强大的准确性.
结论:
- 拟议的CAD系统有效地使用MRI衍生的嗅觉神经标记物和整体机器学习来诊断COVID-19相关的厌氧症.
- 该系统的高精度和验证的性能指标表明其在临床环境中的潜在实用性.
- 这项技术可以帮助临床医生根据嗅觉功能障碍的严重程度识别需要更密集护理的患者.
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