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.

Anomalous origin of the left coronary artery from the pulmonary artery (ALCAPA) is a rare congenital coronary abnormality also known as Bland-White-Garland syndrome. The incidence of ALCAPA is about 1 in every 300,000 live births, and constitutes 0.24% and 0.46% of all congenital cardiac disease. It has a high infant mortality rate reaching up to 90% if left untreated. For many years, the diagnosis of ALCAPA was by angiography or autopsy. However, multislice computed tomography (MSCT) is a non-invasive imaging tool that allows accurate, non-invasive diagnosis of ALCAPA. Here we report a case of ALCAPA in a six-month-old girl who presented with a two-week history of cough, fever, tachypnea, and sweating during feeding. During admission, an echocardiogram was performed that revealed ALCAPA, which was confirmed using CT. We discuss the role of MSCT in its diagnosis.

72-year-old hypertensive presented with two weeks history of left sided chest pain and hoarseness. Workup demonstrated a pseudoaneurysm in the lesser curvature of the distal aortic arch opposite the origin of the left subclavian artery from a penetrating atherosclerotic ulcer. Following a left carotid-subclavian bypass, endovascular stenting of the aorta was performed excluding the pseudoaneurysm. Patient had excellent angiographic results post-stenting. Follow up at 12 weeks demonstrated complete resolution of his symptoms and good stent position with no endo-leak. Ortner's syndrome describes vocal changes caused by cardiovascular pathology. It should be included in the differential diagnosis of patients with cardiovascular risk factors presenting with hoarseness. This case demonstrates the use of endovascular stents to treat the causative pathology with resolution of symptoms. In expert hands, it represents low risk, minimally invasive therapeutic strategy with excellent early results in patients who are high risk for open procedure.

The Society for Cardiovascular Magnetic Resonance (SCMR) is an international society focused on the research, education, and clinical application of cardiovascular magnetic resonance (CMR). Case of the week is a case series hosted on the SCMR website (https://www.scmr.org) that demonstrates the utility and importance of CMR in the clinical diagnosis and management of cardiovascular disease. Each case consists of the clinical presentation and a discussion of the condition and the role of CMR in diagnosis and guiding clinical management. The cases are all instructive and helpful in the approach to patient management. We present a digital archive of the 2020 Case of the Week series of 11 cases as a means of further enhancing the education of those interested in CMR and as a means of more readily identifying these cases using a PubMed or similar search engine.

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