Anesthesiology

Effect of machine learning models on clinician prediction of postoperative complications: the Perioperative ORACLE randomised clinical trial.

BACKGROUND: Anaesthesiologists might be able to mitigate risk if they know which patients are at gre...

Topology-preserving segmentation of abdominal muscle layers from ultrasound images.

BACKGROUND: In clinical anesthesia, precise segmentation of muscle layers from abdominal ultrasound ...

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

Proper monitoring of anesthesia stages can guarantee the safe performance of clinical surgeries. In ...

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

BACKGROUND: Integration of artificial intelligence (AI) into medical practice has increased recently...

Can artificial intelligence make clinical decisions in regional anesthesia? An infographic.

Danger, Danger, Gaston Labat! Does zero-shot artificial intelligence correlate with anticoagulation guidelines recommendations for neuraxial anesthesia?

INTRODUCTION: Artificial intelligence and large language models (LLMs) have emerged as potentially d...

Artificial Intelligence: Predicting Perioperative Problems.

The rapidly developing field of artificial intelligence (AI) may soon equip clinicians with algorith...

Augmenting patient monitoring during intravenous moderate sedation with artificial intelligence: A pilot study.

PURPOSE/OBJECTIVES: A precordial stethoscope (PS) is essential for ensuring clear breath sounds duri...

The evaluation of the performance of ChatGPT in the management of labor analgesia.

UNLABELLED: ChatGPT4 is a leading large language model (LLM) chatbot released by OpenAI in 2023. Cha...

Machine learning-based identification of the risk factors for postoperative nausea and vomiting in adults.

Postoperative nausea and vomiting (PONV) is a common adverse effect of anesthesia. Identifying risk ...

The metabolic clock of ketamine abuse in rats by a machine learning model.

Ketamine has recently become an anesthetic drug used in human and veterinary clinical medicine for i...

Spectrochemical and explainable artificial intelligence approaches for molecular level identification of the status of critically ill patients with COVID-19.

This study explores the molecular alterations and disease progression in COVID-19 patients using ATR...

Machine Learning Predicts Unplanned Care Escalations for Post-Anesthesia Care Unit Patients during the Perioperative Period: A Single-Center Retrospective Study.

BACKGROUND:  Despite low mortality for elective procedures in the United States and developed countr...

Pediatric cardiac surgery: machine learning models for postoperative complication prediction.

PURPOSE: Managing children undergoing cardiac surgery with cardiopulmonary bypass (CPB) presents a s...

Artificial Intelligence to support ethical decision-making for incapacitated patients: a survey among German anesthesiologists and internists.

BACKGROUND: Artificial intelligence (AI) has revolutionized various healthcare domains, where AI alg...

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

To explore the application efficacy and significance of deep learning in anesthesia management for g...

Discovering transformation products of pharmaceuticals in domestic wastewaters and receiving rivers by using non-target screening and machine learning approaches.

Wastewater treatment plants (WWTPs) are an important source of pharmaceuticals in surface water, but...

Machine learning: implications and applications for ambulatory anesthesia.

PURPOSE OF REVIEW: This review explores the timely and relevant applications of machine learning in ...

Explainable artificial intelligence (XAI) for predicting the need for intubation in methanol-poisoned patients: a study comparing deep and machine learning models.

The need for intubation in methanol-poisoned patients, if not predicted in time, can lead to irrepar...

Do all sedatives promote biological sleep electroencephalogram patterns? A machine learning framework to identify biological sleep promoting sedatives using electroencephalogram.

BACKGROUND: Sedatives are commonly used to promote sleep in intensive care unit patients. However, i...

LightGBM is an Effective Predictive Model for Postoperative Complications in Gastric Cancer: A Study Integrating Radiomics with Ensemble Learning.

Postoperative complications of radical gastrectomy seriously affect postoperative recovery and requi...