Background. Atherosclerosis is a systemic arterial disease with heterogeneous involvement in all vascular beds; however, studies examining the relationship between coronary and radial artery calcification are lacking. The purpose of this study was to assess the relationship between the two sites and the prognostic value of radial artery calcification (RC) for coronary artery disease. Methods. This is a single-center, retrospective cross-sectional study based on Doppler ultrasound of radial artery (RUS) and coronary artery angiography (CAG). We included a total of 202 patients undergoing RUS during distal radial access and CAG at the same procedure, between December 2020 and May 2021, from which 103 were found having RC during RUS (RC group) and 99 without (NRC group). Coronary calcifications were evaluated either by angiography examination (moderate and severe), positive CT (>100 Agatson units), or intracoronary imaging (IVUS, OCT). Results. A significant correlation was observed between radial calcification and coronary calcification variables (67.3% vs. 32.7%, p = 0.001 ). The correlation between risk factors such as age, smoking, chronic kidney disease, and diabetes mellitus was higher while sex did not play a role. The need of PCI and/or CABG was higher in the RC group (60% vs. 44%, p = 0.02 ). RC, therefore, predicts the extent and severity of coronary artery disease. Conclusion. RC may be frequently associated with calcific coronary plaques. These findings highlight the potential beneficial examination of radial arteries whenever CAD is suspected.

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Variations in COVID-19 lesions such as glass ground opacities (GGO), consolidations, and crazy paving can compromise the ability of solo-deep learning (SDL) or hybrid-deep learning (HDL) artificial intelligence (AI) models in predicting automated COVID-19 lung segmentation in Computed Tomography (CT) from unseen data leading to poor clinical manifestations. As the first study of its kind, “COVLIAS 1.0-Unseen” proves two hypotheses, (i) contrast adjustment is vital for AI, and (ii) HDL is superior to SDL. In a multicenter study, 10,000 CT slices were collected from 72 Italian (ITA) patients with low-GGO, and 80 Croatian (CRO) patients with high-GGO. Hounsfield Units (HU) were automatically adjusted to train the AI models and predict from test data, leading to four combinations—two Unseen sets: (i) train-CRO:test-ITA, (ii) train-ITA:test-CRO, and two Seen sets: (iii) train-CRO:test-CRO, (iv) train-ITA:test-ITA. COVILAS used three SDL models: PSPNet, SegNet, UNet and six HDL models: VGG-PSPNet, VGG-SegNet, VGG-UNet, ResNet-PSPNet, ResNet-SegNet, and ResNet-UNet. Two trained, blinded senior radiologists conducted ground truth annotations. Five types of performance metrics were used to validate COVLIAS 1.0-Unseen which was further benchmarked against MedSeg, an open-source web-based system. After HU adjustment for DS and JI, HDL (Unseen AI) > SDL (Unseen AI) by 4% and 5%, respectively. For CC, HDL (Unseen AI) > SDL (Unseen AI) by 6%. The COVLIAS-MedSeg difference was < 5%, meeting regulatory guidelines.Unseen AI was successfully demonstrated using automated HU adjustment. HDL was found to be superior to SDL. Supplementary Information The online version contains supplementary material available at 10.1007/s10916-022-01850-y.

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