The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of COVID-19 patients is attracting much interest from both the clinical and the AI community. In this study we provide insights and also raise warnings on what is reasonable to expect by applying deep learning to COVID classification of CXR images. We provide a methodological guide and critical reading of an extensive set of statistical results that can be obtained using currently available datasets. In particular, we take the challenge posed by current small size COVID data and show how significant can be the bias introduced by transfer-learning using larger public non-COVID CXR datasets. We also contribute by providing results on a medium size COVID CXR dataset, just collected by one of the major emergency hospitals in Northern Italy during the peak of the COVID pandemic. These novel data allow us to contribute to validate the generalization capacity of preliminary results circulating in the scientific community. Our conclusions shed some light into the possibility to effectively discriminate COVID using CXR.

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The inclusion of stress CTP for the evaluation of patients with an intermediate to high risk for CAD is feasible and improved the diagnostic performance of coronary CTA for detecting functionally significant CAD.

The addition of stress-CTP with visual evaluation to cCTA imaging has similar diagnostic performance when compared with the quantitative analysis of myocardial perfusion based on TPR measurement.

Hp phenotype is an independent predictor of MVO. Therefore, Hp phenotyping could be used for risk stratification and may be useful in assessing new therapies to reduce myocardial reperfusion injury in patients with STEMI.