AI-Powered Anaesthesia Monitoring Systems: Integrating Machine Learning with Physiological Data for Optimal Patient Care

Authors

Swamy Prasadarao Velaga, Sasikanth Reddy Mandati

Abstract

Anaesthesia monitoring is essential for ensuring patient safety during surgical procedures. Current methods often lack the capability for real-time, comprehensive analysis of physiological data. This study introduces an AI-powered anaesthesia monitoring system that utilizes machine learning to analyze data from electroencephalography (EEG), electrocardiography (ECG), and pulse oximetry. The proposed system provides real-time assessments of patient status and anaesthesia depth, aiming to enhance the accuracy and effectiveness of monitoring. A machine learning model was developed to process extensive physiological data, detecting patterns indicative of anaesthesia depth. The system was validated through clinical trials, showing improved accuracy over traditional monitoring methods. By integrating AI, the system offers real-time feedback, supporting anesthesiologists in making informed decisions and enhancing patient safety. This research demonstrates the potential for AI to revolutionize anaesthesia monitoring, providing precise and individualized patient care. The findings suggest that AI-powered systems can significantly reduce the risk of anaesthesia-related complications. Future work will focus on refining the model and exploring its application in diverse clinical settings.

Keywords

Anaesthesia monitoring, Artificial Intelligence, Machine Learning

AI-Powered Anaesthesia Monitoring Systems: Integrating Machine Learning with Physiological Data for Optimal Patient Care. Swamy Prasadarao Velaga, Sasikanth Reddy Mandati. 2024. IJIRCT, Volume 10, Issue 3. Pages 1-13. https://www.ijirct.org/viewPaper.php?paperId=2406042