AIDCOV: An Interpretable Artificial Intelligence Model for Detection of COVID-19 from Chest Radiography Images

M. Zokaeinikoo, Pooyan Kazemian, P. Mitra, S. Kumara
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引用次数: 30

Abstract

As the Coronavirus Disease 2019 (COVID-19) pandemic continues to grow globally, testing to detect COVID-19 and isolating individuals who test positive remains to be the primary strategy for preventing community spread of the disease. The current gold standard method of testing for COVID-19 is the reverse transcription polymerase chain reaction (RT-PCR) test. The RT-PCR test, however, has an imperfect sensitivity (around 70%), is time-consuming and labor-intensive, and is in short supply, particularly in resource-limited countries. Therefore, automatic and accurate detection of COVID-19 using medical imaging modalities such as chest X-ray and Computed Tomography, which are more widely available and accessible, can be beneficial. We develop a novel hierarchical attention neural network model to classify chest radiography images as belonging to a person with either COVID-19, other infections, or no pneumonia (i.e., normal). We refer to this model as Artificial Intelligence for Detection of COVID-19 (AIDCOV). The hierarchical structure in AIDCOV captures the dependency of features and improves model performance while the attention mechanism makes the model interpretable and transparent. Using a publicly available dataset of 5801 chest images, we demonstrate that our model achieves a mean cross-validation accuracy of 97.8%. AIDCOV has a sensitivity of 99.3%, a specificity of 99.98%, and a positive predictive value of 99.6% in detecting COVID-19 from chest radiography images. AIDCOV can be used in conjunction with or instead of RT-PCR testing (where RT-PCR testing is unavailable) to detect and isolate individuals with COVID-19 and prevent onward transmission to the general population and healthcare workers.
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AIDCOV:一种可解释的胸片图像COVID-19检测人工智能模型
随着2019冠状病毒病(COVID-19)在全球范围内的持续增长,检测COVID-19并隔离检测呈阳性的个体仍然是预防该疾病社区传播的主要策略。目前检测COVID-19的金标准方法是逆转录聚合酶链反应(RT-PCR)检测。然而,RT-PCR检测的灵敏度不完美(约为70%),耗时费力,而且供应短缺,特别是在资源有限的国家。因此,利用胸部x线和计算机断层扫描等医学成像方式自动准确检测COVID-19可能是有益的,这些方式更容易获得和获得。我们开发了一种新的分层注意力神经网络模型,将胸片图像分类为属于COVID-19,其他感染或非肺炎(即正常)的人。我们将这个模型称为COVID-19检测人工智能(AIDCOV)。AIDCOV的层次结构捕获了特征之间的依赖关系,提高了模型的性能;注意机制使模型具有可解释性和透明性。使用5801张胸部图像的公开数据集,我们证明我们的模型达到了97.8%的平均交叉验证准确率。AIDCOV在胸片图像中检测COVID-19的敏感性为99.3%,特异性为99.98%,阳性预测值为99.6%。AIDCOV可与RT-PCR检测结合使用或替代RT-PCR检测(在没有RT-PCR检测的情况下),以发现和隔离COVID-19患者,并防止向一般人群和卫生保健工作者传播。
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