Anesthesiology

3D Convolutional Neural Networks for Improved Detection of Intracranial bleeding in CT Imaging

Background: Intracranial bleeding (IB) is a life-threatening condition caused by traumatic brain injuries, including epidural, subdural, subarachnoid, and intraparenchymal hemorrhages. Rapid and accurate detection is crucial to prevent severe complications. Traditional imaging can be slow and prone to variability, especially in high-pressure scenarios. Artificial Intelligence (AI) provides a sol...

Multi-modal 3D Pose and Shape Estimation with Computed Tomography

In perioperative care, precise in-bed 3D patient pose and shape estimation (PSE) can be vital in optimizing patient positioning in preoperative planning, enabling accurate overlay of medical images for augmented reality-based surgical navigation, and mitigating risks of prolonged immobility during recovery. Conventional PSE methods relying on modalities such as RGB-D, infrared, or pressure maps ...

End-to-End Deep Learning for Real-Time Neuroimaging-Based Assessment of Bimanual Motor Skills

The real-time assessment of complex motor skills presents a challenge in fields such as surgical training and rehabilitation. Recent advancements in...

MANDARIN: Mixture-of-Experts Framework for Dynamic Delirium and Coma Prediction in ICU Patients: Development and Validation of an Acute Brain Dysfunction Prediction Model

Acute brain dysfunction (ABD) is a common, severe ICU complication, presenting as delirium or coma and leading to prolonged stays, increased mortali...

Artificial Intelligence Literacy Levels of Perioperative Nurses: The Case of Türkiye.

Artificial intelligence (AI) experience among nurses in perioperative settings is crucial for effective healthcare delivery. This study aimed to asses...

Examining the frequency of artificial intelligence generated content in anesthesiology and intensive care journal publications: A cross sectional study.

The emergence of artificial intelligence (AI)-based linguistic models has revolutionized academic writing, prompting concerns about integrity. In resp...

Proteomic Learning of Gamma-Aminobutyric Acid (GABA) Receptor-Mediated Anesthesia

Anesthetics are crucial in surgical procedures and therapeutic interventions, but they come with side effects and varying levels of effectiveness, c...

A machine-learning-guided hydrogen-bonded organic framework for long-term, ultrasound-triggered pain therapy

Effective treatment of chronic pain remains hindered by the lack of drug delivery systems that simultaneously achieve long-term stability, high spatia...

Imaging cellular activity simultaneously across all organs of a vertebrate reveals body-wide circuits

All cells in an animal collectively ensure, moment-to-moment, the survival of the whole organism in the face of environmental stressors1,2. Physiology...

Interpretable Machine Learning Identifies an Emergent Absence Seizure Mechanism

Absence epilepsy is a generalized seizure disorder marked by widespread spike-and-wave oscillations and sudden lapses in consciousness. Although no co...

Neural Activity Dynamics in Primate Cortex Across Consciousness Levels: Insights from High-Density Neuropixel Recording

This study investigates the anesthesia mechanisms induced by sevoflurane and how it modulates neural activity in the posterior parietal cortex (PPC) a...

Predicting Future Development of Stress-Induced Anhedonia From Cortical Dynamics and Facial Expression

The current state of mental health treatment for individuals diagnosed with major depressive disorder leaves billions of individuals with first-line t...

Genetic Influences on Neural Responses in Placebo Analgesia Circuitry

Placebo analgesia is a well-established medical phenomenon with overlapping neural representations between humans and rodents, but the genetic contrib...

Multi-modal, multi-species, and multi-task latent-space model for decoding level of consciousness

Assessing the degree and characteristics of consciousness is central to caring for patients with Disorders of Consciousness (DoC), yet current standar...

Protocol for the development and validation of machine-learning models for predicting the risk of hypertriglyceridemia in critically ill patients receiving propofol sedation using retrospective data

Propofol is a widely used sedative-hypnotic agent for critically-ill patients requiring invasive mechanical ventilation (IMV). Despite its clinical be...

Heart rate fragmentation improves general anesthesia state classification using machine learning

Accurate assessment of consciousness during general anesthesia is crucial for optimizing anesthetic dosage and patient safety. Current electroencephal...

Deep Learning–Based Early Detection of Major Adverse Cerebral Injuries in Cardiothoracic and Vascular Surgery

Despite advances in central nervous system (CNS)-protective anesthetic and surgical strategies, perioperative stroke remains a significant concern in ...

A pragmatic randomized controlled trial of artificial intelligence (AI)-based predictive analytics monitoring for early detection of clinical deterioration

This pragmatic randomized controlled trial aimed to assess the effect of a passive display of artificial intelligence (AI)-based predictive analytics ...

NutriSighT: Interpretable Transformer Model for Dynamic Prediction of Hypocaloric Enteral Nutrition in Mechanically Ventilated Patients

Achieving adequate enteral nutrition among mechanically ventilated patients is challenging, yet critical. We developed NutriSighT, a transformer model...

Bridging the Anesthesia Digital Data Gap in Low-Middle-Income Countries: Computer Vision-Ready Paper Health Records

Surgical mortality is the third leading cause of death globally, with mortality rates in Africa double those of high-income countries despite patients...