Detection of COVID-19 using AI - Deep learning and multiple medical imaging modalities

使用 AI 检测 COVID-19 - 深度学习和多种医学成像模式

• 批准号: 552039-2020
• 负责人: Akhloufi, Moulay
• 金额: $ 3.64万
• 依托单位: Université de Moncton
• 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=703302
• 关键词: Detection; COVID; 19; using; AI

SARS-CoV2 is a new coronavirus identified as the cause of the 2019 coronavirus disease (COVID-19) which started in Wuhan, China in late 2019 and has spread around the world. This pandemic is impacting the daily lives of everyone around the world. Currently, the most common screening and diagnostic test for COVID-19 is a laboratory test called reverse transcription polymerase chain reaction (RT-PCR). This test uses samples collected from the patient (with a nasopharyngeal and or throat swab). The specificity of this test is considered high. However, its sensitivity can be as low as 60-70%, leading to a significant number of false negatives and increasing the risk of community transmission. Other concerns are the test result long turnaround time and the worldwide shortage of reagents and swabs, which limits the number of tests that can be conducted. Experts say that for social distancing measures to be safely lifted, we will need to run a large number of tests. Medical imaging modalities have also been used to detect signs of COVID-19. More recently, an international panel of experts evaluated the utility of imaging technologies in the management of COVID-19, especially chest radiography (CXR) and computed tomography (CT). Another interesting modality that shows a lot of promise for fast triage of new infections is chest ultrasound (CUS). Recent research shows that all these imaging modalities can be valuable in the fight against this pandemic and can be used along with RT-PCR laboratory tests. The main objective of this project is to develop an AI-based tool capable of detecting COVID-19 using medical imaging modalities of the chest (radiography (CXR), ultrasonography (CUS) and computerized tomography (ChCT)). In addition, we will develop an explainability algorithm to give a visual feedback showing the location of the signs of the disease. This visual feedback will be very useful in helping the confirmation and the stage of the disease by a health practitioner.

SARS-CoV 2是一种新型冠状病毒，被确定为2019年冠状病毒病（COVID-19）的病因，该疾病于2019年底在中国武汉开始，并已蔓延至全球。这一流行病正在影响世界各地每个人的日常生活。目前，COVID-19最常见的筛查和诊断测试是一种称为逆转录聚合酶链反应（RT-PCR）的实验室测试。该测试使用从患者（鼻咽和/或咽喉拭子）收集的样本。认为该试验的特异性较高。然而，其敏感性可低至60- 70%，导致大量假阴性，增加了社区传播的风险。其他问题是测试结果的周转时间长，以及全球试剂和拭子短缺，这限制了可以进行的测试数量。专家说，要安全地解除社交距离措施，我们需要进行大量的测试。 医学成像模式也被用于检测COVID-19的迹象。最近，一个国际专家小组评估了成像技术在COVID-19管理中的实用性，特别是胸部X线摄影（CXR）和计算机断层扫描（CT）。另一个有趣的方式，显示了很多新的感染快速分类的承诺是胸部超声（CUS）。最近的研究表明，所有这些成像方式可以在与这种流行病的斗争中是有价值的，可以沿着与RT-PCR实验室测试。该项目的主要目标是开发一种基于人工智能的工具，能够使用胸部医学成像模式（X射线照相术（CXR），超声检查（CUS）和计算机断层扫描（ChCT））检测COVID-19。此外，我们将开发一种可解释性算法，以提供显示疾病体征位置的视觉反馈。这种视觉反馈将是非常有用的，帮助确认和疾病的阶段由保健医生。