Key result
AI and deep learning show potential to detect disease and guide treatment in cardiovascular emergencies.
Why the study?
Cardiovascular emergencies require rapid recognition and intervention, and artificial intelligence has emerged as a key technology for disease detection, outcome prediction, and treatment guidance.
Artificial intelligence and deep learning show significant potential in improving the rapid diagnosis and management of cardiovascular emergencies such as myocardial infarction, sudden cardiac arrest, and stroke.
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.
No takes yet. Share an insight, caveat, or question.
Hatfaludi et al. (2023) conducted a review in Cardiovascular emergencies. Artificial Intelligence was evaluated. Artificial intelligence and deep learning algorithms demonstrate significant potential in detecting diseases, predicting outcomes, and guiding treatment decisions across cardiovascular emergencies.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: