Cardiovascular Disease, Sleep-Disordered Breathing, and Artificial Intelligence: From Neutral Trials to Precision...

Area of Science:

• Cardiology
• Sleep Medicine
• Artificial Intelligence

Background:

• Sleep-disordered breathing (SDB), encompassing obstructive and central sleep apnea, is common in cardiovascular disease (CVD) patients.
• SDB exacerbates hypertension, coronary artery disease, heart failure, and cardiovascular events through mechanisms like hypoxemia and inflammation.
• Current SDB treatments show neutral or adverse cardiovascular outcomes, highlighting the need for personalized approaches.

Purpose of the Study:

• To review the mechanistic links between SDB and cardiovascular health.
• To critically examine artificial intelligence (AI) applications for SDB management in CVD.
• To explore AI's role in enhancing screening, risk prediction, and treatment optimization for SDB in CVD.

Main Methods:

• Review of mechanistic pathways linking SDB to cardiovascular pathology.
• Evaluation of AI-based methods for SDB detection and risk stratification.
• Analysis of AI applications in personalizing SDB therapy and clinical workflow integration.

Main Results:

• AI facilitates automated SDB detection from clinical and biosignal data.
• Machine learning models predict cardiovascular risk using sleep parameters.
• AI aids in identifying high-risk phenotypes and personalizing SDB treatment.

Conclusions:

• AI tools show potential for earlier SDB diagnosis and tailored interventions in CVD.
• AI can bridge the gap between understanding SDB's pathophysiology and improving patient outcomes.
• Future research should focus on prospective validation and equitable deployment of AI in SDB management.