Given the spread of COVID-19 to vast geographical regions and populations, it is not feasible to undergo or recommend the RT-PCR based tests to all individuals with flu-like symptoms. The reach of RT-PCR based testing is still limited due to the high cost of the test and huge population in few countries. Thus, alternative methods for COVID-19 infection risk prediction can be useful. We built an explainable artificial intelligence (AI) based integrated web-based prospective framework for COVID-19 risk prediction. We employed a two-step procedure for the non-clinical prediction of COVID19 infection risk. In the first step we assess the initial risk of COVID19 infection based on carefully selected parameters associated with COVID-19 positive symptoms from recent research. Generally, X-ray scans are cheaper and easily available in most government and private health centres. Therefore, based on the outcome of the computed initial risk in first step, we further provide an optional prediction using the chest X-ray scans in the second step of our proposed AI based prospective framework. Since there is a bottleneck to undergo an expensive RT-PCR based confirmatory test in economically backward nations, this is a crucial part of our explainable AI based prospective framework. The initial risk assessment outcome is analysed in combination with the advanced deep learning-based analysis of chest X-ray scans to provide an accurate prediction of COVID-19 infection risk. This prospective web-based AI framework can be employed in limited resource settings after clinical validation in future. The cost and time associated with the adoption of this prospective AI based prospective framework will be minimal and hence it will be beneficial to majority of the population living in low-income settings such as small towns and rural areas that have limited access to advanced healthcare facilities.

中文翻译：

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基于可解释的人工智能的COVID-19风险预测前瞻框架

考虑到COVID-19的分布在广大的地理区域和人口中，对所有具有流感样症状的个体进行或推荐基于RT-PCR的检测是不可行的。由于测试成本高且少数国家/地区人口众多，因此基于RT-PCR的测试的范围仍然受到限制。因此，用于COVID-19感染风险预测的替代方法可能会有用。我们为COVID-19风险预测建立了一个可解释的基于人工智能（AI）的集成基于Web的前瞻性框架。我们采用了分两步进行非临床预测COVID19感染风险的程序。第一步，我们根据最近研究的与COVID-19阳性症状相关的精心选择的参数，评估COVID19感染的初始风险。一般来说，X射线扫描更便宜，并且在大多数政府和私人医疗中心都很容易获得。因此，基于第一步中计算出的初始风险的结果，我们在基于AI的预期框架的第二步中使用胸部X线扫描进一步提供了可选的预测。由于在经济落后的国家中，要进行昂贵的基于RT-PCR的验证性测试存在瓶颈，因此这是我们可解释的基于AI的前瞻性框架的关键部分。结合初步的基于深度学习的胸部X射线扫描分析对初始风险评估结果进行分析，以提供对COVID-19感染风险的准确预测。在未来的临床验证之后，可以在有限的资源设置中使用这种基于Web的前瞻性AI框架。