COVID-BLUeS – A Prospective Study on the Value of AI in Lung Ultrasound Analysis
Nina Wiedemann, Dianne de Korte-de Boer, Matthias Richter, Sjors van de Weijer, Charlotte Buhre, Franz A. M. Eggert, Sophie Aarnoudse, Lotte Grevendonk, Steffen Röber, Carlijn M.E. Remie, Wolfgang Buhre, Ronald Henry, Jannis Born
作为一种轻量级和非侵入性成像技术,肺超声(LUS)在评估肺部病理学方面已经变得重要。 由于时间和专业知识密集的解释,人工智能(AI)在医疗决策支持系统中的使用是有希望的,但由于用于训练AI模型的现有数据质量差,其用于实际应用的可用性仍不清楚。 在一项前瞻性研究中,我们分析了在马斯特里赫特大学医学中心收集的63名COVID-19嫌疑人(33名阳性)的数据。 在BLUE协议之后获得了六个身体位置的超声记录,并手动标记为肺部参与的严重程度。 应用和训练了几种AI模型,用于检测和肺部感染的严重程度。 根据LUS视频的人类注释者分配的肺部感染的严重程度在COVID-19阳性和阴性患者之间没有显着差异(p = 0.89)。 然而,基于图像的AI模型的预测确定了65的COVID-19感染
As a lightweight and non-invasive imaging technique, lung ultrasound (LUS) has gained importance for assessing lung pathologies. The use of Artificial intelligence (AI) in medical decision support systems is promising due to the time- and expertise-intensive interpretation, however, due to the poor quality of existing data used for training AI models, their usability for real-world applications remains unclear. In a prospective study, we analyze data from 63 COVID-19 suspects (33 positive) colle...