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

Sleep-disordered breathing (SDB), which includes both obstructive and central sleep apnea, is highly prevalent among patients with cardiovascular disease (CVD). Moreover, SDB contributes significantly to the development and progression of hypertension, coronary artery disease, arrhythmias, heart failure, and various cardiovascular and cerebrovascular events. However, despite strong mechanistic links involving intermittent hypoxemia, sympathetic activation, intrathoracic pressure fluctuations, and systemic inflammation, clinical trials of SDB treatment have yielded in neutral or even adverse cardiovascular outcomes. These results underscore the need for refined phenotyping, risk stratification, and personalized management. Artificial intelligence (AI) has emerged as a promising tool to address these challenges. In this review, we evaluate the mechanistic pathways through which SDB affects cardiovascular health and critically examine AI-based methods to enhance screening, outcome prediction, and treatment optimization. Applications include automated detection using clinical and biosignal data, cardiovascular risk prediction through machine-learning models based on sleep parameters, and AI-guided therapy personalization. Furthermore, we emphasize translational relevance by comparing model performance, identifying high-risk phenotypes, and exploring the potential for integration into clinical workflows. AI-enabled tools may help bridge the gap between pathophysiological understanding and improved outcomes by facilitating earlier diagnosis, tailored interventions, and proactive monitoring. Future studies should focus on prospective validation, regulatory pathways, and equitable deployment across populations.