Cardiovascular diseases are the leading cause of death, with many lives being affected by critical emergencies like heart attacks, strokes, and other acute conditions. Recognizing the early warning signs is crucial for highlighting the need for immediate medical attention, especially since a quick intervention may significantly improve short and long-term patient outcome. Artificial intelligence (AI) has become a key technology in healthcare, and especially in the cardiovascular field. AI, and in particular deep learning is well suited for automatically analyzing medical images, signals, and data. Its success rests on the availability of large amounts of curated data, and the access to high performance computing infrastructures for training the deep-learning algorithms. Thus, in cardiovascular care, AI plays a dynamic role in disease detection, predicting disease outcome, and guiding treatment decisions. This review paper details and discusses the current role of AI for the most common cardiovascular emergencies. It provides insight into the specific issues, risk factors, different subtypes of the diseases, and algorithms developed to date, followed by an outlook.

Background: Autologous vascular arterial or venous graft are not available in 10-40% of patients, due to vascular pathologies, and the utility of decellularized biological scaffolds would be a solution for those cases. The purpose of this research was to obtain a functional acellular xenograft, prior to in-vivo testing as a vascular graft in an experimental animal. Materials and method: Two batches of carotid vasculo-nervous bundles were collected from porcine models from a local slaughterhouse. The arterial grafts were dissected and isolated, obtaining carotid arteries with a caliber of 5–6 mm and a length of approximately 10–12 cm. Two decellularization protocols were used, immersion (n = 10) and perfusion (n = 9). The resulting grafts underwent histological examination, DNA analysis, electrophoresis and spectrophotometry. Results: Due to severe tissue damage and friability, the batch that was decellularized using perfusion was not examined. The histological examination of grafts stained with hematoxylin- eosin and DAPI highlighted the absence of nuclei. Spectrophotometry revealed a 90% decellularization, and electrophoresis of revealed the migration band of the material extracted from the fresh tissue, as well as the absence of migration bands in the case of the material extracted from decellularized tissues. Conclusion: We successfully used the immersion protocol to obtain a functional acellular vascular graft, in contrast to perfusion decellularization, where intraluminal high pressures damage the extracellular matrix.