Objectives We sought to explore the spectrum of cardiac abnormalities in student-athletes who returned to university campus in July 2020 with an uncomplicated Coronavirus disease 2019 (COVID-19). Background There is limited information regarding cardiovascular involvement in young individuals with mild or asymptomatic COVID-19. Methods Screening echocardiograms were performed in 54 consecutive student-athletes (mean age: 19 years, 85% males) who tested positive on reverse transcription–polymerase chain reaction nasal swab testing of the upper respiratory tract or IgG antibodies against SARS-CoV-2. A sequential cardiac magnetic resonance (CMR) imaging was performed in 48 (89%) subjects. Results A total of 16 (30%) athletes were asymptomatic while 36 (66%) and 2 (4%) reported mild and moderate COVID-19 related symptoms, respectively. For the 48 athletes completing both imaging studies, abnormal findings were identified in 27 (56.3%) individuals. This included 19 (39.5%) showing pericardial late enhancements with associated pericardial effusion. Of the individuals with pericardial enhancements, 6 (12.5%) had reduced global longitudinal strain (GLS) and/or an increased native T1. One patient showed myocardial enhancement and reduced left ventricular ejection fraction or reduced GLS with or without increased native T1 were also identified in additional 7 (14.6%) individuals. Native T2 were normal in all subjects and no specific imaging features of myocardial inflammation were identified. Hierarchical clustering of LV regional strain identified three unique myopericardial phenotypes that showed significant association with the CMR findings (P=0.03). Conclusion Over one in three previously healthy college-athletes recovering from COVID-19 infection showed imaging features of a resolving pericardial inflammation. Although subtle changes in myocardial structure and function were identified, no athlete showed specific imaging features to suggest an ongoing myocarditis. Further studies are needed to understand the clinical implications and long-term evolution of these abnormalities in uncomplicated COVID-19.

In this current digital landscape, artificial intelligence (AI) has established itself as a powerful tool in the commercial industry and is an evolving technology in healthcare. Cutting-edge imaging modalities outputting multi-dimensional data are becoming increasingly complex. In this era of data explosion, the field of cardiovascular imaging is undergoing a paradigm shift toward machine learning (ML) driven platforms. These diverse algorithms can seamlessly analyze information and automate a range of tasks. In this review article, we explore the role of ML in the field of cardiovascular imaging.

Central diabetes insipidus (CDI) is an infrequent complication of neurosarcoidosis (NS). Its presentation may be masked by adrenal insufficiency (AI) and uncovered by subsequent steroid replacement. A 45-year-old woman with a history of NS presented 2 weeks after abrupt cessation of prednisone with nausea, vomiting, decreased oral intake and confusion. She was diagnosed with secondary AI and intravenous hydrocortisone was promptly begun. Over the next few days, however, the patient developed severe thirst and polyuria exceeding 6 L of urine per day, accompanied by hypernatraemia and hypo-osmolar urine. She was presumed to have CDI due to NS, and intranasal desmopressin was administered. This eventually normalised her urine output and serum sodium. The patient was discharged improved on intranasal desmopressin and oral prednisone. AI may mask the manifestation of CDI because low serum cortisol impairs renal-free water clearance. Steroid replacement reverses this process and unmasks an underlying CDI.

Aims Sex-specific thresholds of aortic valve calcification (AVC) have been proposed and validated in Caucasians. Thus, we aimed to validate their accuracy in Asians. Methods and results Patients with calcific aortic stenosis (AS) from seven international centres were included. Exclusion criteria were ≥moderate aortic/mitral regurgitation and bicuspid valve. Optimal AVC and AVC-density sex-specific thresholds for severe AS were obtained in concordant grading and normal flow patients (CG/NF). We included 1263 patients [728 (57%) Asians, 573 (45%) women, 837 (66%) with CG/NF]. Mean gradient was 48 (26–64) mmHg and peak aortic velocity 4.5 (3.4–5.1) m/s. Optimal AVC thresholds were: 2145 Agatston Units (AU) in men and 1301 AU in women for Asians; and 1885 AU in men and 1129 AU in women for Caucasians. Overall, accuracy (% correctly classified) was high and comparable either using optimal or guidelines’ thresholds (2000 AU in men, 1200 AU in women). However, accuracy was lower in Asian women vs. Caucasian women (76–78% vs. 94–95%; P < 0.001). Accuracy of AVC-density (476 AU/cm2 in men and 292 AU/cm2 in women) was comparable to absolute AVC in Caucasians (91% vs. 91%, respectively, P = 0.74), but higher than absolute AVC in Asians (87% vs. 81%, P < 0.001). There was no interaction between AVC/AVC-density and ethnicity (all P > 0.41) with regards to AS haemodynamic severity. Conclusion AVC thresholds defining severe AS are comparable in Asian and Caucasian populations, and similar to those proposed in the guidelines. However, accuracy of AVC to identify severe AS in Asians (especially women) is sub-optimal. Therefore, the use of AVC-density is preferable in Asians.

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