Singh Mahavir, Babbarwal Anmol, Pushpakumar Sathnur, Tyagi Suresh C
Department of Physiology, School of Medicine, University of Louisville, Louisville, Kentucky, USA.
Center for Predictive Medicine (CPM) for Biodefense and Emerging Infectious Diseases, School of Medicine, University of Louisville, Louisville, Kentucky, USA.
Physiol Rep. 2025 Jan;13(1):e70146. doi: 10.14814/phy2.70146.
"I see, I forget, I read aloud, I remember, and when I do read purposefully by writing it, I do not forget it." This phenomenon is known as "interoception" and refers to the sensing and interpretation of internal body signals, allowing the brain to communicate with various body systems. Dysfunction in interoception is associated with cardiovascular disorders. We delve into the concept of interoception and its impact on heart failure (HF) by reviewing and exploring neural mechanisms underlying interoceptive processing. Furthermore, we review the potential of artificial intelligence (AI) in diagnosis, biomarker development, and HF treatment. In the context of HF, AI algorithms can analyze and interpret complex interoceptive data, providing valuable insights for diagnosis and treatment. These algorithms can identify patterns of disease markers that can contribute to early detection and diagnosis, enabling timely intervention and improved outcomes. These biomarkers hold significant potential in improving the precision/efficacy of HF. Additionally, AI-powered technologies offer promising avenues for treatment. By leveraging patient data, AI can personalize therapeutic interventions. AI-driven technologies such as remote monitoring devices and wearable sensors enable the monitoring of patients' health. By harnessing the power of AI, we should aim to advance the diagnosis and treatment strategies for HF. This review explores the potential of AI in diagnosing, developing biomarkers, and managing HF.
“我看了，我忘了；我读了，我记住了；当我通过书写有目的地阅读时，我就不会忘记。”这种现象被称为“内感受”，指的是对身体内部信号的感知和解读，使大脑能够与身体的各个系统进行交流。内感受功能障碍与心血管疾病有关。我们通过回顾和探索内感受处理的神经机制，深入研究内感受的概念及其对心力衰竭（HF）的影响。此外，我们还回顾了人工智能（AI）在诊断、生物标志物开发和HF治疗方面的潜力。在HF的背景下，AI算法可以分析和解释复杂的内感受数据，为诊断和治疗提供有价值的见解。这些算法可以识别疾病标志物的模式，有助于早期检测和诊断，实现及时干预并改善治疗效果。这些生物标志物在提高HF的精准度/疗效方面具有巨大潜力。此外，人工智能驱动的技术为治疗提供了有前景的途径。通过利用患者数据，AI可以实现治疗干预的个性化。诸如远程监测设备和可穿戴传感器等AI驱动的技术能够对患者的健康状况进行监测。通过利用AI的力量，我们应该致力于推进HF的诊断和治疗策略。这篇综述探讨了AI在诊断、开发生物标志物和管理HF方面的潜力。
