Artificial Intelligence (AI) methods have significant potential for diagnosis and prognosis of COVID-19 infections. Rapid identification of COVID-19 and its severity in individual patients is expected to enable better control of the disease individually and at-large. There has been remarkable interest by the scientific community in using imaging biomarkers to improve detection and management of COVID-19. Exploratory tools such as AI-based models may help explain the complex biological mechanisms and provide better understanding of the underlying pathophysiological processes. The present review focuses on AI-based COVID-19 studies as applies to chest x-ray (CXR) and computed tomography (CT) imaging modalities, and the associated challenges. Explicit radiomics, deep learning methods, and hybrid methods that combine both deep learning and explicit radiomics have the potential to enhance the ability and usefulness of radiological images to assist clinicians in the current COVID-19 pandemic. The aims of this review are: first, to outline COVID-19 AI-analysis workflows, including acquisition of data, feature selection, segmentation methods, feature extraction, and multi-variate model development and validation as appropriate for AI-based COVID-19 studies. Secondly, existing limitations of AI-based COVID-19 analyses are discussed, highlighting potential improvements that can be made. Finally, the impact of AI and radiomics methods and the associated clinical outcomes are summarized. In this review, pipelines that include the key steps for AI-based COVID-19 signatures identification are elaborated. Sample size, non-standard imaging protocols, segmentation, availability of public COVID-19 databases, combination of imaging and clinical information and full clinical validation remain major limitations and challenges. We conclude that AI-based assessment of CXR and CT images has significant potential as a viable pathway for the diagnosis, follow-up and prognosis of COVID-19.

人工智能 (AI) 方法在 COVID-19 感染的诊断和预后方面具有巨大潜力。快速识别个体患者的 COVID-19 及其严重程度有望更好地控制个体和整体疾病。科学界对使用成像生物标记物来改善 COVID-19 的检测和管理表现出浓厚的兴趣。基于人工智能的模型等探索性工具可能有助于解释复杂的生物学机制，并更好地理解潜在的病理生理过程。本综述重点关注适用于胸部 X 光 (CXR) 和计算机断层扫描 (CT) 成像模式的基于 AI 的 COVID-19 研究以及相关挑战。显式放射组学、深度学习方法以及结合深度学习和显式放射组学的混合方法有可能增强放射图像的能力和实用性，以帮助临床医生应对当前的 COVID-19 大流行。本次审查的目的是：首先，概述 COVID-19 人工智能分析工作流程，包括数据采集、特征选择、分割方法、特征提取以及适用于基于人工智能的 COVID-19 的多变量模型开发和验证学习。其次，讨论了基于人工智能的 COVID-19 分析的现有局限性，强调了可以做出的潜在改进。最后，总结了人工智能和放射组学方法的影响以及相关的临床结果。在本次审查中，详细阐述了包括基于人工智能的 COVID-19 签名识别关键步骤的流程。样本量、非标准成像协议、分割、公共 COVID-19 数据库的可用性、成像和临床信息的结合以及完整的临床验证仍然是主要的限制和挑战。我们的结论是，基于 AI 的 CXR 和 CT 图像评估具有作为 COVID-19 的诊断、随访和预后的可行途径的巨大潜力。