Dasgupta Abhijit, Bakshi Abhisek, Mukherjee Srijani, Das Kuntal, Talukdar Soumyajeet, Chatterjee Pratyayee, Mondal Sagnik, Das Puspita, Ghosh Subhrojit, Som Archisman, Roy Pritha, Kundu Rima, Sarkar Akash, Biswas Arnab, Paul Karnelia, Basak Sujit, Manna Krishnendu, Saha Chinmay, Mukhopadhyay Satinath, Bhattacharyya Nitai P, De Rajat K
Department of Data Science, School of Interdisciplinary Studies University of Kalyani, Kalyani Nadia West Bengal India.
Department of Information Technology Bengal Institute of Technology Kolkata West Bengal India.
Wiley Interdiscip Rev Data Min Knowl Discov. 2022 Jul-Aug;12(4):e1462. doi: 10.1002/widm.1462. Epub 2022 Jun 28.
World is now experiencing a major health calamity due to the coronavirus disease (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus clade 2. The foremost challenge facing the scientific community is to explore the growth and transmission capability of the virus. Use of artificial intelligence (AI), such as deep learning, in (i) rapid disease detection from x-ray or computed tomography (CT) or high-resolution CT (HRCT) images, (ii) accurate prediction of the epidemic patterns and their saturation throughout the globe, (iii) forecasting the disease and psychological impact on the population from social networking data, and (iv) prediction of drug-protein interactions for repurposing the drugs, has attracted much attention. In the present study, we describe the role of various AI-based technologies for rapid and efficient detection from CT images complementing quantitative real-time polymerase chain reaction and immunodiagnostic assays. AI-based technologies to anticipate the current pandemic pattern, prevent the spread of disease, and face mask detection are also discussed. We inspect how the virus transmits depending on different factors. We investigate the deep learning technique to assess the affinity of the most probable drugs to treat COVID-19. This article is categorized under:Application Areas > Health CareAlgorithmic Development > Biological Data MiningTechnologies > Machine Learning.
由于严重急性呼吸综合征冠状病毒2型引起的冠状病毒病（COVID-19）大流行，世界正经历一场重大的健康灾难。科学界面临的首要挑战是探索该病毒的生长和传播能力。人工智能（AI）的应用，如深度学习，在以下方面发挥了作用：（i）从X射线、计算机断层扫描（CT）或高分辨率CT（HRCT）图像中快速检测疾病；（ii）准确预测全球范围内的疫情模式及其饱和度；（iii）根据社交网络数据预测疾病对人群的影响以及心理影响；（iv）预测药物与蛋白质的相互作用以重新利用药物，这些都引起了广泛关注。在本研究中，我们描述了各种基于AI的技术在从CT图像中进行快速高效检测方面的作用，以补充定量实时聚合酶链反应和免疫诊断检测。还讨论了基于AI的技术在预测当前大流行模式、防止疾病传播和检测口罩方面的应用。我们研究了病毒如何根据不同因素进行传播。我们研究了深度学习技术以评估最有可能治疗COVID-19的药物的亲和力。本文分类如下：应用领域>医疗保健；算法开发>生物数据挖掘；技术>机器学习。
