Gunraj Hayden, Sabri Ali, Koff David, Wong Alexander
Vision and Image Processing Lab, University of Waterloo, Waterloo, ON, Canada.
Department of Radiology, McMaster University, Hamilton, ON, Canada.
Front Med (Lausanne). 2022 Mar 10;8:729287. doi: 10.3389/fmed.2021.729287. eCollection 2021.
The COVID-19 pandemic continues to rage on, with multiple waves causing substantial harm to health and economies around the world. Motivated by the use of computed tomography (CT) imaging at clinical institutes around the world as an effective complementary screening method to RT-PCR testing, we introduced COVID-Net CT, a deep neural network tailored for detection of COVID-19 cases from chest CT images, along with a large curated benchmark dataset comprising 1,489 patient cases as part of the open-source COVID-Net initiative. However, one potential limiting factor is restricted data quantity and diversity given the single nation patient cohort used in the study. To address this limitation, in this study we introduce enhanced deep neural networks for COVID-19 detection from chest CT images which are trained using a large, diverse, multinational patient cohort. We accomplish this through the introduction of two new CT benchmark datasets, the largest of which comprises a multinational cohort of 4,501 patients from at least 16 countries. To the best of our knowledge, this represents the largest, most diverse multinational cohort for COVID-19 CT images in open-access form. Additionally, we introduce a novel lightweight neural network architecture called COVID-Net CT S, which is significantly smaller and faster than the previously introduced COVID-Net CT architecture. We leverage explainability to investigate the decision-making behavior of the trained models and ensure that decisions are based on relevant indicators, with the results for select cases reviewed and reported on by two board-certified radiologists with over 10 and 30 years of experience, respectively. The best-performing deep neural network in this study achieved accuracy, COVID-19 sensitivity, positive predictive value, specificity, and negative predictive value of 99.0%/99.1%/98.0%/99.4%/99.7%, respectively. Moreover, explainability-driven performance validation shows consistency with radiologist interpretation by leveraging correct, clinically relevant critical factors. The results are promising and suggest the strong potential of deep neural networks as an effective tool for computer-aided COVID-19 assessment. While not a production-ready solution, we hope the open-source, open-access release of COVID-Net CT-2 and the associated benchmark datasets will continue to enable researchers, clinicians, and citizen data scientists alike to build upon them.
新冠疫情仍在肆虐，多波疫情给全球健康和经济造成了巨大危害。受全球临床机构将计算机断层扫描（CT）成像用作逆转录聚合酶链反应（RT-PCR）检测的有效补充筛查方法的启发，我们推出了COVID-Net CT，这是一种专门用于从胸部CT图像中检测新冠病例的深度神经网络，并推出了一个精心整理的大型基准数据集，该数据集包含1489例患者病例，作为开源COVID-Net计划的一部分。然而，鉴于该研究中使用的是单一国家的患者队列，一个潜在的限制因素是数据量和多样性有限。为解决这一限制，在本研究中，我们引入了用于从胸部CT图像中检测新冠的增强深度神经网络，这些网络使用了一个大型、多样的跨国患者队列进行训练。我们通过引入两个新的CT基准数据集来实现这一点，其中最大的数据集包含来自至少16个国家的4501名患者的跨国队列。据我们所知，这是以开放获取形式呈现的最大、最多样化的新冠CT图像跨国队列。此外，我们还引入了一种名为COVID-Net CT S的新型轻量级神经网络架构，它比之前推出的COVID-Net CT架构显著更小、更快。我们利用可解释性来研究训练模型的决策行为，并确保决策基于相关指标，部分病例的结果由两位分别拥有超过10年和30年经验的经委员会认证的放射科医生进行审查和报告。本研究中表现最佳的深度神经网络的准确率、新冠敏感性、阳性预测值、特异性和阴性预测值分别达到了99.0%/99.1%/98.0%/99.4%/99.7%。此外，通过可解释性驱动的性能验证表明，利用正确的、临床相关的关键因素，其与放射科医生的解读具有一致性。结果很有前景，表明深度神经网络作为计算机辅助新冠评估的有效工具具有巨大潜力。虽然这不是一个可直接投入生产的解决方案，但我们希望COVID-Net CT-2的开源、开放获取版本以及相关基准数据集将继续使研究人员、临床医生和公民数据科学家能够在此基础上开展工作。
