Rivera Boadla Marlon E, Sharma Nava R, Varghese Jeffy, Lamichhane Saral, Khan Muhammad H, Gulati Amit, Khurana Sakshi, Tan Samuel, Sharma Anupam
Internal Medicine, Maimonides Medical Center, Brooklyn, USA.
Medicine, Manipal College of Medical Sciences, Pokhara, NPL.
Cureus. 2024 Jul 10;16(7):e64272. doi: 10.7759/cureus.64272. eCollection 2024 Jul.
Cardiovascular disease remains a leading global health challenge, necessitating advanced diagnostic approaches. This review explores the integration of artificial intelligence (AI) in multimodal cardiac imaging, tracing its evolution from early X-rays to contemporary techniques such as CT, MRI, and nuclear imaging. AI, particularly machine learning and deep learning, significantly enhances cardiac diagnostics by estimating biological heart age, predicting disease risk, and optimizing heart failure management through adaptive algorithms without explicit programming or feature engineering. Key contributions include AI's transformative role in non-invasive coronary artery disease diagnosis, arrhythmia detection via wearable devices, and personalized treatment strategies. Despite substantial progress, challenges including data standardization, algorithm validation, regulatory approval, and ethical considerations must be addressed to fully harness AI's potential. Collaborative efforts among clinicians, scientists, industry stakeholders, and regulatory bodies are essential for the safe and effective deployment of AI in cardiac imaging, promising enhanced diagnostics and personalized patient care.
心血管疾病仍然是全球主要的健康挑战，需要先进的诊断方法。本综述探讨了人工智能（AI）在多模态心脏成像中的整合，追溯其从早期X射线到当代技术（如CT、MRI和核成像）的发展历程。人工智能，特别是机器学习和深度学习，通过估计生物心脏年龄、预测疾病风险以及通过自适应算法优化心力衰竭管理，而无需显式编程或特征工程，显著增强了心脏诊断能力。关键贡献包括人工智能在非侵入性冠状动脉疾病诊断、通过可穿戴设备检测心律失常以及个性化治疗策略方面的变革性作用。尽管取得了重大进展，但仍需应对包括数据标准化、算法验证、监管批准和伦理考量等挑战，以充分发挥人工智能的潜力。临床医生、科学家、行业利益相关者和监管机构之间的合作对于在心脏成像中安全有效地部署人工智能至关重要，有望实现增强诊断和个性化患者护理。
