Abstract
Real-time monitoring and analysis of vital signs during surgery is critical for patient safety and optimal anesthetic management. Traditional monitoring systems alert clinicians to physiological deviations but often lack predictive capabilities and individualized insight. Artificial Intelligence (AI) offers the potential to transform intraoperative care by continuously analyzing multi-parameter physiological data—including heart rate, blood pressure, oxygen saturation, respiratory rate, and electroencephalography—to detect early signs of complications, predict adverse events, and provide actionable guidance. This thesis explores the integration of AI into surgical monitoring, detailing computational frameworks, clinical applications, advantages, challenges, and future directions. AI-enabled intraoperative monitoring enhances surgical outcomes, reduces complications, and supports data-driven, patient-centered perioperative care.
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