AIMC Topic: Anesthesia

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Comparison of AI applications and anesthesiologist's anesthesia method choices.

BMC anesthesiology
BACKGROUND: In medicine, Artificial intelligence has begun to be utilized in nearly every domain, from medical devices to the interpretation of imaging studies. There is still a need for more experience and more studies related to the comprehensive u...

Classification of Three Anesthesia Stages Based on Near-Infrared Spectroscopy Signals.

IEEE journal of biomedical and health informatics
Proper monitoring of anesthesia stages can guarantee the safe performance of clinical surgeries. In this study, different anesthesia stages were classified using near-infrared spectroscopy (NIRS) signals with machine learning. The cerebral hemodynami...

Artificial intelligence-assisted interventions for perioperative anesthetic management: a systematic review and meta-analysis.

BMC anesthesiology
BACKGROUND: Integration of artificial intelligence (AI) into medical practice has increased recently. Numerous AI models have been developed in the field of anesthesiology; however, their use in clinical settings remains limited. This study aimed to ...

Can the Pioneering Impact of Artificial Intelligence in Anaesthetic Practice Uphold Good Medical Practice?

British journal of hospital medicine (London, England : 2005)
The potential applications of Artificial Intelligence (AI) in anaesthesia are expansive.~However, like any technological advancement, the integration of AI in anaesthetic practice comes with both benefits and potential risks. This article seeks to se...

SQI-DOANet: electroencephalogram-based deep neural network for estimating signal quality index and depth of anaesthesia.

Journal of neural engineering
. Monitoring the depth of anaesthesia (DOA) during surgery is of critical importance. However, during surgery electroencephalography (EEG) is usually subject to various disturbances that affect the accuracy of DOA. Therefore, accurately estimating no...

Safety improvement requires data: the case for automation and artificial intelligence during incident reporting.

British journal of anaesthesia
The reporting of incidents has a long association with safety in healthcare and anaesthesia, yet many incident reporting systems substantially under-report critical events. Better understanding the underlying reasons for low levels of critical incide...

Effect of dexamethasone pretreatment using deep learning on the surgical effect of patients with gastrointestinal tumors.

PloS one
To explore the application efficacy and significance of deep learning in anesthesia management for gastrointestinal tumors (GITs) surgery, 80 elderly patients with GITs who underwent surgical intervention at our institution between January and Septem...

Machine learning: implications and applications for ambulatory anesthesia.

Current opinion in anaesthesiology
PURPOSE OF REVIEW: This review explores the timely and relevant applications of machine learning in ambulatory anesthesia, focusing on its potential to optimize operational efficiency, personalize risk assessment, and enhance patient care.

Artificial intelligence and nonoperating room anesthesia.

Current opinion in anaesthesiology
PURPOSE OF REVIEW: The integration of artificial intelligence (AI) in nonoperating room anesthesia (NORA) represents a timely and significant advancement. As the demand for NORA services expands, the application of AI is poised to improve patient sel...

Validation of a natural language processing algorithm using national reporting data to improve identification of anesthesia-related ADVerse evENTs: The "ADVENTURE" study.

Anaesthesia, critical care & pain medicine
BACKGROUND: Reporting and analysis of adverse events (AE) is associated with improved health system learning, quality outcomes, and patient safety. Manual text analysis is time-consuming, costly, and prone to human errors. We aimed to demonstrate the...