Artificial intelligence-driven assessment of radiological images for COVID-19. (September 2021)

• Record Type: Journal Article
• Authors: Bouchareb, Yassine; Moradi Khaniabadi, Pegah; Al Kindi, Faiza; Al Dhuhli, Humoud; Shiri, Isaac; Zaidi, Habib; Rahmim, Arman
• Is Part Of: Computers in biology and medicine. Volume 136(2021)
• Publication Date: 2021-09
• Subjects: COVID-19 -- Computed tomography -- Chest x-ray -- Artificial intelligence -- Radiomics -- Deep learning -- Deep radiomics; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2021.104665
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: 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. Highlights: Exploring AI-driven of CT & CXR that blends methods of radiomics, explicit hand-crafted based, deep learning, & hybrid. The AI methodological workflow for analysing CXR and CT COVID-19 images is comprehensively described and presented. Clinical impact of relevant AI-based COVID-19 analysis is reviewed and most recent results are highlighted. Recommendation extracted from associated AI-based COVID-19 research studies using CT and CXR imaging are made.