Automated Frame-by-Frame Assessment of Lung Ultrasound Imaging in Severe COVID-19 Patients Using Machine Learning

使用机器学习对重症 COVID-19 患者的肺部超声成像进行自动逐帧评估

• 批准号: 550470-2020
• 负责人: Punithakumar, Kumaradevan
• 金额: $ 3.64万
• 依托单位: University of Alberta
• 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=701804
• 关键词: Automated; Frame; Assessment; Lung; Ultrasound

The Coronavirus disease (COVID-19) has become the most pressing concern for the Canadian healthcare system due to the alarmingly increasing number of new cases and deaths every day. Reports by healthcare agencies indicate that around 7-14% of all confirmed COVID-19 patients have been hospitalized in Canada. COVID-19 lung involvement is an ominous sign as patients can decompensate rapidly and within a matter of hours need urgent admission to an intensive care unit (ICU) for mechanical ventilation to maintain lung function until they recover. Therefore, it is critical to accurately diagnose and monitor the disease in patients for better management of hospitals and ICUs, given the limited available resources such as beds and mechanical ventilators. Lung conditions could be assessed using chest radiographs or computed tomography (CT). Repeated radiographs or CT scans of younger patients also represent a high radiation dose known to lead to cancer. About 12% of all hospital admissions for COVID-19 patients are younger than forty years old. In addition, radiation-free non-invasive scanning is essential for COVID-19 patients who are pregnant. In this project, we proposed to use ultrasound imaging with machine learning algorithm development to detect and monitor pneumonia in severe COVID-19 patients. The proposed project will be undertaken by the partnership between the University of Alberta and MEDO.ai in Edmonton, Canada. Ultrasound imaging is inexpensive, non-invasive, free of ionization radiation and portable. Due to their small size, ultrasound scanners are relatively easy to disinfect, which is critical in the case of the highly infectious COVID-19 virus. The initial machine learning algorithm development will rely on ultrasound scans obtained from 296 patients. We will integrate the learned machine models into a web-based diagnostic system to produce a tool that can be used effectively by a healthcare worker with limited training. The system could also be used in rural and remote areas with limited hospital facilities.

由于每天新增病例和死亡人数惊人地增加，冠状病毒病(新冠肺炎)已成为加拿大医疗系统最紧迫的担忧。医疗机构的报告表明，在所有确诊的新冠肺炎患者中，约有7%-14%在加拿大住院。新冠肺炎肺部受累是一个不祥的征兆，因为患者会迅速失代偿，几小时内需要紧急送入重症监护病房(ICU)进行机械通气，以维持肺功能，直到他们恢复为止。因此，鉴于病床和机械呼吸机等可用资源有限，准确诊断和监测患者的疾病对于更好地管理医院和ICU至关重要。肺部状况可以使用胸片或计算机体层摄影(CT)进行评估。年轻患者的重复放射照片或CT扫描也代表着已知的高辐射剂量会导致癌症。在所有入院治疗的新冠肺炎患者中，约有12%的患者年龄在40岁以下。此外，无辐射无创扫描对于怀孕的新冠肺炎患者来说是必不可少的。在这个项目中，我们提出使用超声成像和机器学习算法开发来检测和监测重症新冠肺炎患者的肺炎。拟议的项目将由艾伯塔大学和加拿大埃德蒙顿的MEDO.ai合作进行。超声成像具有价格低廉、非侵入性、无电离辐射和便携等优点。由于体积较小，超声波扫描仪相对容易消毒，这在高传染性新冠肺炎病毒的情况下至关重要。最初的机器学习算法开发将依赖于从296名患者那里获得的超声波扫描。我们将把学到的机器模型集成到一个基于网络的诊断系统中，以产生一个可以被有限培训的医护人员有效使用的工具。该系统还可以用于农村和偏远地区，医院设施有限。