Interoception, cardiac health, and heart failure: The potential for artificial intelligence (AI)-driven diagnosis and treatment

Abstract

"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.

Keywords: automation; cardiovascular medicine; machine learning.

FIGURE 1

Interoception, Artificial Intelligence (AI), and the human brain. The brain is responsible for interoception, which involves sensing internal bodily signals. Practices like yoga can improve interoception, helping us develop innovative diagnostics and personalized interventions to enhance well‐being and human performance. In this context, advances in AI can certainly provide new research opportunities to deepen our understanding of the interoception.

FIGURE 2

Cardiac Pressure‐Volume Loop. This loop illustrates the dynamics of the heart, showing how it balances preload and afterload for effective pumping. Understanding these variations can help us develop targeted interventions for heart failure (HF), leading to better patient outcomes. BHB refers to the blood‐heart barrier.

FIGURE 3

Molecular Footprints in Heart Failure (HF). DNA regulation includes epigenetic processes and transcription control, while proteins and RNA processing affect gene expression and cellular function. Understanding these mechanisms is essential for effectively managing HF. ADAR, adenosine deaminases acting on RNA; FTO, fat mass and obesity‐associated protein; HDAC, histone deacetylase; MT, methyl transferases; PCSK9, proprotein convertase subtilisin/kexin type 9; RNA, ribonucleic acid; SIRT, sirtuin; TET, ten‐eleven translocation proteins; TMPRSS2, transmembrane serine protease 2.

FIGURE 4

Dysrhythmic Gene Modifiers in HFrEF. Heart failure (HF) with reduced ejection fraction (HFrEF) is linked to dysregulated gene expression and epigenetic changes. Key factors and metabolic processes influence these epigenetic modifications. Understanding these elements is vital for developing effective interventions. DNMT, DNA methyltransferase, FTO, fat mass and obesity‐associated protein; MTHFR, methylenetetrahydrofolate reductase; TET, ten‐eleven translocation proteins.

FIGURE 5

Interoception and AI. Schematic of major inter‐organ communication networks of the body (please see the text for details).