Patel Syed J, Yousuf Salma, Padala Jaswanth V, Reddy Shruta, Saraf Pranav, Nooh Alaa, Fernandez Gutierrez Luis Miguel A, Abdirahman Abdirahman H, Tanveer Rameen, Rai Manju
Internal Medicine, S Nijalingappa Medical College and Hanagal Sri Kumareshwar Hospital and Research Centre, Bagalkot, IND.
Public Health, Jinnah Sindh Medical University, Karachi, PAK.
Cureus. 2024 May 11;16(5):e60119. doi: 10.7759/cureus.60119. eCollection 2024 May.
Coronary artery disease (CAD) is still a serious global health issue that has a substantial impact on death and illness rates. The goal of primary prevention strategies is to lower the risk of developing CAD. Nevertheless, current methods usually rely on simple risk assessment instruments that might overlook significant individual risk factors. This limitation highlights the need for innovative methods that can accurately assess cardiovascular risk and offer personalized preventive care. Recent advances in machine learning and artificial intelligence (AI) have opened up interesting new avenues for optimizing primary preventive efforts for CAD and improving risk prediction models. By leveraging large-scale databases and advanced computational techniques, AI has the potential to fundamentally alter how cardiovascular risk is evaluated and managed. This review looks at current randomized controlled studies and clinical trials that explore the application of AI and machine learning to improve primary preventive measures for CAD. The emphasis is on their ability to recognize and include a range of risk elements in sophisticated risk assessment models.
冠状动脉疾病(CAD)仍然是一个严重的全球健康问题,对死亡率和发病率有重大影响。一级预防策略的目标是降低患CAD的风险。然而,目前的方法通常依赖于简单的风险评估工具,这些工具可能会忽略重要的个体风险因素。这一局限性凸显了对创新方法的需求,这些方法能够准确评估心血管风险并提供个性化的预防护理。机器学习和人工智能(AI)的最新进展为优化CAD的一级预防措施和改进风险预测模型开辟了有趣的新途径。通过利用大规模数据库和先进的计算技术,AI有可能从根本上改变心血管风险的评估和管理方式。这篇综述探讨了当前探索将AI和机器学习应用于改善CAD一级预防措施的随机对照研究和临床试验。重点是它们在复杂风险评估模型中识别和纳入一系列风险因素的能力。