AIMC Topic: Anesthesia, General
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Use of Virtual Reality in the Pediatric Perioperative Setting and for Induction of Anesthesia: Mixed Methods Pilot Feasibility Study.
JMIR perioperative medicine
May 16, 2025
BACKGROUND: Children commonly experience high levels of anxiety prior to surgery. This distress is associated with postoperative maladaptive behaviors. Virtual reality (VR) is an innovative tool for reducing anxiety and pain during various medical pr...
Predicting Intraoperative Burst Suppression Using Preoperative EEG and Patient Characteristics.
International journal of neural systems
Apr 16, 2025
Burst suppression (BS) is an electroencephalogram (EEG) pattern observed in patients undergoing general anesthesia. The occurrence of BS is associated with adverse outcomes such as postoperative delirium, extended recovery time, and increased postope...
Post-Anesthesia Care Unit (PACU) readiness predictions using machine learning: a comparative study of algorithms.
BMC medical informatics and decision making
Mar 25, 2025
INTRODUCTION: Accurate and timely discharge from the Post-Anesthesia Care Unit (PACU) is essential to prevent postoperative complications and optimize hospital resource utilization. Premature discharge can lead to severe issues such as respiratory or...
Predicting postoperative nausea and vomiting using machine learning: a model development and validation study.
BMC anesthesiology
Mar 20, 2025
BACKGROUND: Postoperative nausea and vomiting (PONV) is a frequently observed complication in patients undergoing surgery under general anesthesia. Moreover, it is a frequent cause of distress and dissatisfaction in the early postoperative period. Cu...
Deep reinforcement learning for multi-targets propofol dosing.
Journal of clinical monitoring and computing
Mar 6, 2025
The administration of propofol for sedation or general anesthesia presents challenges due to the complex relationship between patient factors and real-time physiological responses. This study explores the application of deep reinforcement learning (D...
A Multimodal Deep Learning Approach to Intraoperative Nociception Monitoring: Integrating Electroencephalogram, Photoplethysmography, and Electrocardiogram.
Sensors (Basel, Switzerland)
Feb 13, 2025
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), ...
Machine learning-based prediction of the risk of moderate-to-severe catheter-related bladder discomfort in general anaesthesia patients: a prospective cohort study.
BMC anesthesiology
Sep 19, 2024
BACKGROUND: Catheter-related bladder discomfort (CRBD) commonly occurs in patients who have indwelling urinary catheters while under general anesthesia. And moderate-to-severe CRBD can lead to significant adverse events and negatively impact patient ...
Accurate Machine Learning-based Monitoring of Anesthesia Depth with EEG Recording.
Neuroscience bulletin
Sep 17, 2024
General anesthesia, pivotal for surgical procedures, requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments. Traditional assessment methods, relying on physiological indicators...
Machine learning-based identification of the risk factors for postoperative nausea and vomiting in adults.
PloS one
Aug 15, 2024
Postoperative nausea and vomiting (PONV) is a common adverse effect of anesthesia. Identifying risk factors for PONV is crucial because it is associated with a longer stay in the post-anesthesia care unit, readmissions, and perioperative costs. This ...
Predicting Dental General Anesthesia Use among Children with Behavioral Health Conditions.
JDR clinical and translational research
Jun 14, 2024
OBJECTIVES: To evaluate how different data sources affect the performance of machine learning algorithms that predict dental general anesthesia use among children with behavioral health conditions.
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