Anesthesiology

AI-Supported, Integrative Prediction of Postoperative Delirium: Protocol for the CONFUSED Study.

BACKGROUND: Postoperative delirium (POD) is a frequent and serious complication in older surgical patients, characterized by acute cognitive dysfunction and fluctuating levels of consciousness. POD is associated with prolonged hospitalization, long-term cognitive decline, reduced quality of life, and increased mortality. Despite its clinical relevance, the underlying pathophysiological mechanisms ...

Diagnostic value of machine-learning using conventional magnetic resonance imaging markers for pediatric idiopathic intracranial hypertension: a retrospective study.

BACKGROUND: Pediatric idiopathic intracranial hypertension can be challenging to diagnose; magnetic resonance imaging (MRI) signs are considered supportive, while lumbar puncture remains the diagnostic reference. OBJECTIVE: We evaluated whether a compact set of conventional magnetic resonance imaging markers, analyzed with supervised machine-learning, can assist in the diagnosis of idiopathic intr...

Integration of a collaborative robotic platform in laparoscopic cholecystectomy: safety, feasibility, and operative time variability.

Laparoscopic cholecystectomy is a high-volume procedure with relatively short operative times, leaving limited margin for further reduction in mean du...

Interpretable machine learning to predict postoperative adverse outcomes in cardiac surgery.

BACKGROUND: Cardiac surgery is associated with significant mortality and complication risks. This study aims to develop an interpretable machine learn...

Conversational artificial intelligence in the operating room: an exploratory cross-sectional single-center survey among anesthesiology residents.

BACKGROUND: Conversational artificial intelligence (AI) tools, such as large language models and chat-based systems, are increasingly used by clinicia...

Understanding Blood-Injection-Injury Phobia: Do Not Miss the Point.

Needle and blood-injection-injury phobia is commonly encountered in the perioperative setting. It can significantly disrupt operating room throughput,...

Recovering Sensitive Medical Text in Federated Learning.

Federated Learning (FL) allows institutions to train shared models without exchanging raw data, making it a promising approach for healthcare applicat...

Empirical Comparison of Causal Machine Learning and Post-Hoc AI Interpretability Models for Risk Factor Analysis: An Application to Medical Specialty Choice.

How medical students choose specialties shapes access to care. Prior work mostly describes patterns; newer prediction tools can rank influential facto...

Machine learning prediction of surgical approach and clinical heterogeneity in cervical spondylotic myelopathy.

BACKGROUND: Preoperative planning of the surgical approach for cervical spondylotic myelopathy (CSM) is central to precision medicine. This study used...

Innovations in pediatric imaging: a scoping review of the past decade with case illustrations.

BACKGROUND: Imaging plays a fundamental and increasing role in the diagnostic work-up of pediatric patients. Non-invasive imaging methods include ultr...

An evidence-based nursing management app prototype for adult urology day surgery: an AI-driven development pathway.

OBJECTIVES: The nursing management of adult urology patients in day surgical settings has undergone rapid development. This study aimed to (1) retriev...

AI-Driven Prediction of Cancer Pain Episodes: A Hybrid Decision Support Approach.

Lung cancer patients frequently experience breakthrough pain episodes, with up to 91% requiring timely intervention. To enable proactive pain manageme...

The multilevel exploration test, a novel paradigm to measure exploratory behavior in depression animal models and the involvement of the PL-ZI circuit.

Diminished drive is one of the core symptoms of major depressive disorder (MDD) diagnosis, yet its underlying neural mechanisms remain elusive, primar...

Network toxicology and bioinformatics analysis predict potential molecular targets and mechanisms by which sevoflurane and propofol influence type 2 diabetes mellitus.

BACKGROUND: Sevoflurane and propofol are commonly used anesthetics that may exert pronounced effects on glucose metabolism and cardiovascular function...

Machine-Learning-Based Predictive Model for Trifecta Achievement in Robot-Assisted Partial Nephrectomy.

OBJECTIVE: To develop and internally validate a machine-learning-based nomogram for predicting trifecta achievement in patients undergoing robot-assis...

Intraoperative neurophysiological monitoring (IONM) in neurosurgery: A critical appraisal of established practices, ongoing controversies, and future trajectories.

Intraoperative neurophysiological monitoring (IONM) has evolved from a novel technique into an evidence-based standard treatment method for high-risk ...

Hidden Preoperative Lower-Extremity Venous Thrombosis in Pelvic Organ Prolapse Surgery: Prevalence and Development of a Clinical Prediction Model.

INTRODUCTION AND HYPOTHESIS: Preoperative lower-extremity venous thrombosis (LEVT) is frequently overlooked in women undergoing pelvic organ prolapse ...

AI literacy and school tier moderate educators' ethical coping mechanisms through sedative and reverse Matthew effects.

The rapid penetration of artificial intelligence in education has triggered unprecedented ethical risk perception and response challenges for educator...

Development and nested cross-validation of machine learning models for predicting transfusion requirements in pediatric craniosynostosis surgery.

Blood transfusion is common during pediatric craniosynostosis surgery; however, transfusion volumes and use of cell salvage systems can vary considera...

An interpretable machine learning model for predicting emergence agitation in children: a multicenter development and validation study.

BACKGROUND: Postoperative agitation (EA) is a common complication in pediatric patients, and its early identification is crucial for improving periope...