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Artificial intelligence (AI) has penetrated the field of medicine, particularly the field of radiology. Since its emergence, the highly virulent coronavirus disease 2019 (COVID-19) has infected over 10 million people, leading to over 500,000 deaths as of July 1 st , 2020 . Since the outbreak began, almost 28,000 articles about COVID-19 have been published ( https://pubmed.ncbi.nlm.nih.gov ); however, few have explored the role of imaging and artificial intelligence in COVID-19 patients—specifically, those with comorbidities. This paper begins by presenting the four pathways that can lead to heart and brain injuries following a COVID-19 infection. Our survey also offers insights into the role that imaging can play in the treatment of comorbid patients, based on probabilities derived from COVID-19 symptom statistics. Such symptoms include myocardial injury, hypoxia, plaque rupture, arrhythmias, venous thromboembolism, coronary thrombosis, encephalitis, ischemia, inflammation, and lung injury. At its core, this study considers the role of image-based AI, which can be used to characterize the tissues of a COVID-19 patient and classify the severity of their infection. Image-based AI is more important than ever as the pandemic surges and countries worldwide grapple with limited medical resources for detection and diagnosis.

Blood pressure (BP) exhibits seasonal variation with lower levels at higher environmental temperatures and higher at lower temperatures. This is a global phenomenon affecting both sexes, all age groups, normotensive individuals, and hypertensive patients. In treated hypertensive patients it may result in excessive BP decline in summer, or rise in winter, possibly deserving treatment modification. This Consensus Statement by the European Society of Hypertension Working Group on BP Monitoring and Cardiovascular Variability provides a review of the evidence on the seasonal BP variation regarding its epidemiology, pathophysiology, relevance, magnitude, and the findings using different measurement methods. Consensus recommendations are provided for health professionals on how to evaluate the seasonal BP changes in treated hypertensive patients and when treatment modification might be justified. (i) In treated hypertensive patients symptoms appearing with temperature rise and suggesting overtreatment must be investigated for possible excessive BP drop due to seasonal variation. On the other hand, a BP rise during cold weather, might be due to seasonal variation. (ii) The seasonal BP changes should be confirmed by repeated office measurements; preferably with home or ambulatory BP monitoring. Other reasons for BP change must be excluded. (iii) Similar issues might appear in people traveling from cold to hot places, or the reverse. (iv) BP levels below the recommended treatment goal should be considered for possible down-titration, particularly if there are symptoms suggesting overtreatment. SBP less than 110 mmHg requires consideration for treatment down-titration, even in asymptomatic patients. Further research is needed on the optimal management of the seasonal BP changes.

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Background: Most cardiovascular (CV)/stroke risk calculators using the integration of carotid ultrasound image-based phenotypes (CUSIP) with conventional risk factors (CRF) have shown improved risk stratification compared with either method. However such approaches have not yet leveraged the potential of machine learning (ML). Most intelligent ML strategies use follow-ups for the endpoints but are costly and time-intensive. We introduce an integrated ML system using stenosis as an endpoint for training and determine whether such a system can lead to superior performance compared with the conventional ML system. Methods: The ML-based algorithm consists of an offline and online system. The offline system extracts 47 features which comprised of 13 CRF and 34 CUSIP. Principal component analysis (PCA) was used to select the most significant features. These offline features were then trained using the event-equivalent gold standard (consisting of percentage stenosis) using a random forest (RF) classifier framework to generate training coefficients. The online system then transforms the PCA-based test features using offline trained coefficients to predict the risk labels on test subjects. The above ML system determines the area under the curve (AUC) using a 10-fold cross-validation paradigm. The above system so-called "AtheroRisk-Integrated" was compared against "AtheroRisk-Conventional", where only 13 CRF were considered in a feature set. Results: Left and right common carotid arteries of 202 Japanese patients (Toho University, Japan) were retrospectively examined to obtain 395 ultrasound scans. AtheroRisk-Integrated system [AUC =0.80, P<0.0001, 95% confidence interval (CI): 0.77 to 0.84] showed an improvement of ~18% against AtheroRisk-Conventional ML (AUC =0.68, P<0.0001, 95% CI: 0.64 to 0.72). Conclusions: ML-based integrated model with the event-equivalent gold standard as percentage stenosis is powerful and offers low cost and high performance CV/stroke risk assessment.