Nechita Luiza Camelia, Tutunaru Dana, Nechita Aurel, Voipan Andreea Elena, Voipan Daniel, Tupu Ancuta Elena, Musat Carmina Liana
Faculty of Medicine and Pharmacy, 'Dunarea de Jos' University of Galati, 800008 Galati, Romania.
Faculty of Automation, Computers, Electrical Engineering and Electronics, 'Dunarea de Jos' University of Galati, 800008 Galati, Romania.
Diagnostics (Basel). 2025 Mar 20;15(6):787. doi: 10.3390/diagnostics15060787.
The increasing prevalence of cardiovascular complications in cancer patients due to cardiotoxic treatments has necessitated advanced monitoring and predictive solutions. Cardio-oncology is an evolving interdisciplinary field that addresses these challenges by integrating artificial intelligence (AI) and smart cardiac devices. This comprehensive review explores the integration of artificial intelligence (AI) and smart cardiac devices in cardio-oncology, highlighting their role in improving cardiovascular risk assessment and the early detection and real-time monitoring of cardiotoxicity. AI-driven techniques, including machine learning (ML) and deep learning (DL), enhance risk stratification, optimize treatment decisions, and support personalized care for oncology patients at cardiovascular risk. Wearable ECG patches, biosensors, and AI-integrated implantable devices enable continuous cardiac surveillance and predictive analytics. While these advancements offer significant potential, challenges such as data standardization, regulatory approvals, and equitable access must be addressed. Further research, clinical validation, and multidisciplinary collaboration are essential to fully integrate AI-driven solutions into cardio-oncology practices and improve patient outcomes.
由于心脏毒性治疗,癌症患者心血管并发症的患病率不断上升,这就需要先进的监测和预测解决方案。心脏肿瘤学是一个不断发展的跨学科领域,通过整合人工智能(AI)和智能心脏设备来应对这些挑战。这篇综述探讨了人工智能(AI)和智能心脏设备在心脏肿瘤学中的整合,强调了它们在改善心血管风险评估以及心脏毒性的早期检测和实时监测方面的作用。包括机器学习(ML)和深度学习(DL)在内的人工智能驱动技术,增强了风险分层,优化了治疗决策,并为有心血管风险的肿瘤患者提供个性化护理。可穿戴式心电图贴片、生物传感器以及集成了人工智能的植入式设备能够实现持续的心脏监测和预测分析。虽然这些进展具有巨大潜力,但数据标准化、监管批准和公平获取等挑战仍须加以解决。进一步的研究、临床验证和多学科合作对于将人工智能驱动的解决方案全面整合到心脏肿瘤学实践中并改善患者预后至关重要。