Latest AI and machine learning research in anesthesiology for healthcare professionals.
Background: Perioperative observational studies are increasingly used to evaluate anesthesia practices that are difficult to test in randomized trials, but treatment selection is influenced by surgical procedure type. Scalable and robust methods are needed to adjust for procedure-level confounding across heterogeneous surgical cohorts. Methods: We developed and assessed the utility of a surgical-n...
Aperiodic (1/f-like) EEG activity has rapidly become a popular noninvasive marker of cortical network state, proposed to index excitation-inhibition (E/I) balance and increasingly applied across neurological and psychiatric disorders. However, whether this approach remains reliable in the pathological brain, where disease progressively reorganizes neural networks, alters signal morphology, and dri...
Perioperative risk prediction models are often limited by narrow surgical populations, incomplete intraoperative data, poor calibration, and limited i...
Accurate assessment of pain in animal models is essential for understanding pain mechanisms, developing analgesics, and ensuring animal welfare. The M...
Anesthetic-induced unconsciousness may arise partly from a change in how brain areas can manipulate each other's activity. To quantify this change, we...
BACKGROUND: Liberation from invasive mechanical ventilation (IMV) is a central therapeutic objective in acute respiratory failure (ARF). While lung-pr...
Postoperative adverse events, including mortality and morbidity, remain a major global burden, many of which are preventable through early identificat...
Exogenous opioids that activate mu-opioid receptors (MORs) in nociceptive circuits mediate transient pain relief lasting minutes to hours but have mor...
Chemotherapy-induced peripheral neuropathy (CIPN) is a common and painful side effect of paclitaxel (PTX) treatment. The most common measures of painf...
Background: Computer vision-enabled airway workflows can turn airway video into timestamped model-observation fields, but later blinded review and tra...
Burst suppression (BS) is a clinically relevant electroencephalographic (EEG) pattern used to monitor sedation depth and brain activity in critically ...
Objective. To preserve the encoding of visual information in prosthetic vision as close to natural as possible, subretinal photovoltaic implants, whic...
Distinguishing causal adverse drug events (ADEs) from spurious correlations remains a central challenge in pharmacovigilance. The InferBERT framework ...
Caenorhabditis elegans is a premier model organism for aging and neurobiology research, valued for its short lifespan, optical transparency, genetic t...
Neuroimaging based pain decoding faces two underappreciated challenges: between subject variability that prevents classifiers from generalizing across...
Background Patients worldwide receive healthcare in many languages, yet medical AI systems are validated almost exclusively in high-resource languages...
Hand-surface interactions between clinicians, patients, and medical equipment play a central role in pathogen transmission during medical procedures. ...
Nasotracheal intubation (NTI) is a critical clinical procedure for establishing and maintaining patient airway patency. Machine-assisted NTI has emerg...
Yawning is a highly conserved behavior, yet its neural dynamics across arousal state transitions remain poorly understood. Classical reflex models fai...
Background. Large language models (LLMs) are increasingly used by clinicians to generate executable code for pharmacokinetic (PK) simulation. Whether ...