评估COVID-19疾病的严重程度:放射视觉评分与使用肺炎分析算法自动定量CT参数的放射视觉评分
Dario Baldi1, Bruna Punzo1, Andrea Colacino1
1IRCCS SYNLAB SDN, Naples, Italy.
人工智能辅助CT分析显示,与放射科医生对COVID-19肺部参与的视觉评估有很高的一致性. 这种工具可以提高诊断效率和可靠性,帮助治疗冠状病毒肺炎患者.
科学领域:
- 放射学和医学成像学 医学成像学
- 医疗保健中的人工智能
- 传染性疾病 传染性疾病
背景情况:
- 冠状病毒疾病 (COVID-19) 可以导致严重的急性肺损伤.
- 精确评估肺部参与对于患者管理至关重要.
- 放射科医生传统上使用视觉评分来确定肺部参与,这可能是主观的.
研究的目的:
- 评估人工智能辅助的定量工具对评估COVID-19肺部影响的影响.
- 将基于人工智能的CT分析与放射科医生的半定量视觉评分进行比较.
- 确定两种方法的诊断性能和一致性.
主要方法:
- 追溯招募611名疑似肺炎和呼吸困难的患者.
- 使用5级严重程度尺度对肺部感染的视觉评估.
- 基于AI的自动CT分析量化肺部变化的体积和密度.
- 与SpO2和RT-PCR结果的相关性分析.
主要成果:
- 视觉和AI定量评估之间的高度一致性 (k=0.73),严重病例的良好一致性 (k=0.91).
- 在预测RT-PCR结果时,这两种方法的诊断性能相似 (AUC~0.55-0.56).
- 人工智能工具表现出随着时间的推移而改善的一致性和类似的准确性,特别是在>25%的肺部参与患者中.
结论:
- 人工智能辅助CT分析是COVID-19肺炎评估的宝贵工具.
- 人工智能工具显示高一致性和可靠性,与专家放射科医生相美.
- 人工智能算法可以提高诊断效率,并有助于COVID-19常规患者管理.
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