Comprehensive Analysis of Cardiovascular Diseases: Symptoms, Diagnosis, and AI InnovationsShow others and affiliations
2024 (English)In: Bioengineering, E-ISSN 2306-5354, Vol. 11, no 12, p. 1239-1239Article in journal (Refereed) Published
Abstract [en]
Cardiovascular diseases are some of the underlying reasons contributing to the relentlessrise in mortality rates across the globe. In this regard, there is a genuine need to integrate advancedtechnologies into the medical realm to detect such diseases accurately. Moreover, numerous academicstudies have been published using AI-based methodologies because of their enhanced accuracy indetecting heart conditions. This research extensively delineates the different heart conditions, e.g.,coronary artery disease, arrhythmia, atherosclerosis, mitral valve prolapse/mitral regurgitation, andmyocardial infarction, and their underlying reasons and symptoms and subsequently introducesAI-based detection methodologies for precisely classifying such diseases. The review shows thatthe incorporation of artificial intelligence in detecting heart diseases exhibits enhanced accuraciesalong with a plethora of other benefits, like improved diagnostic accuracy, early detection and prevention,reduction in diagnostic errors, faster diagnosis, personalized treatment schedules, optimizedmonitoring and predictive analysis, improved efficiency, and scalability. Furthermore, the reviewalso indicates the conspicuous disparities between the results generated by previous algorithms andthe latest ones, paving the way for medical researchers to ascertain the accuracy of these resultsthrough comparative analysis with the practical conditions of patients. In conclusion, AI in heartdisease detection holds paramount significance and transformative potential to greatly enhancepatient outcomes, mitigate healthcare expenditure, and amplify the speed of diagnosis.
Place, publisher, year, edition, pages
2024. Vol. 11, no 12, p. 1239-1239
Keywords [en]
cardiovascular disease, electrocardiography, artificial intelligence, diagnostic methods, machine learning, deep learning
National Category
Clinical Medicine
Identifiers
URN: urn:nbn:se:mdh:diva-69528DOI: 10.3390/bioengineering11121239OAI: oai:DiVA.org:mdh-69528DiVA, id: diva2:1920672
2024-12-122024-12-122024-12-12Bibliographically approved