AIMC Topic: Monitoring, Intraoperative

Clear Filters Showing 1 to 10 of 50 articles

Harnessing operating room signals to estimate mean arterial pressure with AnesthNet.

Scientific reports
Monitoring mean arterial pressure (MAP) is essential for ensuring safe general anesthesia. Current practices rely either on non-invasive cuff measurements, which suffer from poor temporal resolution, or invasive arterial lines, which provide excellen...

Real-Time Estimation of Arterial Partial Pressure of Carbon Dioxide in Patients Undergoing General Anesthesia: Predictive Modeling Study.

JMIR medical informatics
BACKGROUND: Adequate ventilation in mechanically ventilated patients is contingent upon the monitoring of the arterial partial pressure of carbon dioxide (PaCO2) during general anesthesia. Despite its significance, continuous monitoring remains chall...

Comparing supervised machine learning algorithms for the prediction of partial arterial pressure of oxygen during craniotomy.

BMC medical informatics and decision making
BACKGROUND AND OBJECTIVES: Brain tissue oxygenation is usually inferred from arterial partial pressure of oxygen (paO), which is in turn often inferred from pulse oximetry measurements or other non-invasive proxies. Our aim was to evaluate the feasib...

Intraoperative hypotension prediction in cardiac and noncardiac procedures: is HPI truly worthwhile? A systematic review and meta-analysis.

BMC anesthesiology
BACKGROUND: Intraoperative hypotension (IOH), defined as a mean arterial pressure (MAP) below 65 mmHg, is a common complication during surgery and is associated with significant postoperative morbidity, including acute kidney injury, myocardial injur...

Identifying patterns of high intraoperative blood pressure variability in noncardiac surgery using explainable machine learning: a retrospective cohort study.

Annals of medicine
BACKGROUND: High intraoperative blood pressure variability (HIBPV) is significantly associated with postoperative adverse complications. However, practical tools to characterize perioperative factors associated with HIBPV remain limited. This study a...

The Role of Artificial Intelligence in Anesthesia Monitoring and Surveillance.

Anesthesiology clinics
Artificial intelligence (AI) has the potential to significantly improve monitoring in the operating room, allowing us to detect and predict changes in the patient's physiology sooner and better optimize patient care. Currently, clinically available a...

Diastolic Versus Systolic or Mean Intraoperative Hypotension as Predictive of Perioperative Myocardial Injury in a White-Box Machine-Learning Model.

Anesthesia and analgesia
BACKGROUND: Intraoperative hypotension (IOH) and tachycardia are associated with perioperative myocardial injury (PMI), and thereby increased postoperative mortality. Patients undergoing vascular surgery are specifically at risk of developing cardiac...

A Multimodal Deep Learning Approach to Intraoperative Nociception Monitoring: Integrating Electroencephalogram, Photoplethysmography, and Electrocardiogram.

Sensors (Basel, Switzerland)
Monitoring nociception under general anesthesia remains challenging due to the complexity of pain pathways and the limitations of single-parameter methods. In this study, we introduce a multimodal approach that integrates electroencephalogram (EEG), ...

Characterizing drivers of change in intraoperative cerebral saturation using supervised machine learning.

Journal of clinical monitoring and computing
Regional cerebral oxygen saturation (rSO) is used to monitor cerebral perfusion with emerging evidence that optimization of rSO may improve neurological and non-neurological outcomes. To manipulate rSO an understanding of the variables that drive its...