Development of machine learning techniques for accessible and inexpensive imaging of COVID-19 with ultrasound

开发机器学习技术，通过超声对 COVID-19 进行便捷且廉价的成像

• 批准号: 552686-2020
• 负责人: Rivaz, Hassan
• 金额: $ 3.64万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=703504
• 关键词: Development; machine; learning; techniques; accessible

Recent papers from Emergency Room (ER) researchers in Italy and China has shown several advantages of imaging the lungs with ultrasound instead of the current practice of using Computed Tomography (CT) or chest X-ray. These papers showed that COVID-19 infection can be detected using B-lines, horizontal lines in the ultrasound image that are created because of the COVID-19 infection. Clarius ultrasound devices can be used as inexpensive screening tools in COVID-19 test-pods, for pre-triage, and in bedside for monitoring patients. They can also be used for imaging pregnant women who cannot undergo CT or X-ray imaging due to radiation exposure. Finally, Clarius ultrasound devices are more than 200 times less expensive than CT scanners, which can substantially save healthcare costs. Despite these attractive features, ultrasound remains user-dependent, and requires a skilled sonographer to collect high-quality images. In addition, detecting B-lines in ultrasound images entails careful inspection of images by an expert sonographer, which further limits accessibility of this screening tool. Given the current shortage of medical professionals at ERs, this partnership has two goals to address these issues: 1. Simplify image collection by automatically selecting the best ultrasound image as a novice user moves and rotates the probe on the chest wall. 2. Simplify interpretation of images by automatically detecting and localize B-lines in the best frame. In close collaboration with Clarius, we will develop novel Neural Networks (NN) that address these problems. We will fabricate phantoms that mimic healthy and infected lungs to train and test our NN. We will exploit latest advances in machine learning to develop NNs that work for unseen data from different patients with different image settings.

来自意大利和中国急诊室（ER）研究人员的最新论文显示了使用超声对肺部成像的几个优势，而不是目前使用计算机断层扫描（CT）或胸部X射线的做法。这些论文表明，COVID-19感染可以使用B线检测，B线是由于COVID-19感染而产生的超声图像中的水平线。 Clarius超声设备可用作COVID-19测试舱中的廉价筛查工具，用于预分类，并用于床边监测患者。它们也可用于因辐射暴露而无法接受CT或X射线成像的孕妇成像。最后，Clarius超声设备比CT扫描仪便宜200倍以上，可以大幅节省医疗成本。 尽管有这些有吸引力的功能，超声仍然依赖于用户，并需要一个熟练的声谱仪收集高质量的图像。此外，检测超声图像中的B线需要由专家声谱仪仔细检查图像，这进一步限制了该筛查工具的可访问性。鉴于急诊室目前缺乏医疗专业人员，这种伙伴关系有两个目标来解决这些问题： 1.当新手用户在胸壁上移动和旋转探头时，通过自动选择最佳超声图像来优化图像收集。 2.通过在最佳帧中自动检测和定位B线，实现图像的快速解释。 通过与Clarius的密切合作，我们将开发新的神经网络（NN）来解决这些问题。我们将制造模拟健康和受感染肺部的幻影来训练和测试我们的神经网络。我们将利用机器学习的最新进展来开发NN，这些NN适用于来自不同患者的具有不同图像设置的不可见数据。