使用人工智能的COVID-19肺炎CT定量评估分析-可行性和与其他常见的肺炎形式的差异化
Una Ebong1, Susanne Martina Büttner1, Stefan A Schmidt1
1Department of Diagnostic and Interventional Radiology, Ulm University Medical Center, Albert-Einstein-Allee 23, 89081 Ulm, Germany.
Diagnostics (Basel, Switzerland)
|June 28, 2023
概括
一个AI原型可以在肺部CT扫描中检测和量化肺炎,包括COVID-19. 虽然人类放射科医生表现出更高的特异性,但人工智能提供了快速,自动化分析肺炎的焦点.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 放射学 放射学是一门学科.
背景情况:
- 准确检测和区分肺炎病因,包括COVID-19,对于患者管理至关重要.
- 计算机断层扫描 (CT) 是评估肺病理的一个关键成像模式.
- 人工智能驱动的工具在提高医疗成像诊断准确性和效率方面表现有前途.
研究的目的:
- 评估基于AI的软件原型的技术可行性,用于在肺部CT数据集中检测COVID-19肺炎.
- 评估AI在区分COVID-19肺炎与其他类型肺炎方面的能力.
- 将AI的表现与人类读者评估进行比较.
主要方法:
- 一项追溯病例控制研究,涉及144名肺CT数据集患者.
- 小组分析包括细菌性,病毒性,真菌性肺炎和无肺炎的对照组.
- 基于AI的"肺炎分析原型"被用于评分肺炎的程度和病因,与读者评估相比.
主要成果:
- 人工智能在COVID-19检测中实现了80.8%的灵敏度和50%的特异性,而在人类放射科医生中达到80.8%的灵敏度和97.2%的特异性.
- 人工智能分析显示,与真菌和细菌性肺炎相比,COVID-19患者的COVID-19概率显著更高.
- 肺炎焦点的自动细分和量化得以迅速实现,平均后处理时间为7.61分钟.
结论:
- 人工智能原型证明了检测和量化COVID-19之外的肺炎的技术可行性,向经验丰富的读者展示了COVID-19肺炎的可比结果.
- 人工智能在快速,自动化的细分和肺炎量化方面提供了优势.
- 进一步开发可能会提高AI在区分各种肺炎病因方面的特异性.
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