Critical Care

Latest AI and machine learning research in critical care for healthcare professionals.

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Enhancing neural encoding models for naturalistic perception with a multi-level integration of deep neural networks and cortical networks.

Cognitive neuroscience aims to develop computational models that can accurately predict and explain ...

PulmoNet: a novel deep learning based pulmonary diseases detection model.

Pulmonary diseases are various pathological conditions that affect respiratory tissues and organs, m...

LAMA: Lesion-Aware Mixup Augmentation for Skin Lesion Segmentation.

Deep learning can exceed dermatologists' diagnostic accuracy in experimental image environments. How...

Multicentre validation of a machine learning model for predicting respiratory failure after noncardiac surgery.

BACKGROUND: Postoperative respiratory failure is a serious complication that could benefit from earl...

moSCminer: a cell subtype classification framework based on the attention neural network integrating the single-cell multi-omics dataset on the cloud.

Single-cell omics sequencing has rapidly advanced, enabling the quantification of diverse omics prof...

Deep match: A zero-shot framework for improved fiducial-free respiratory motion tracking.

BACKGROUND AND PURPOSE: Motion management is essential to reduce normal tissue exposure and maintain...

Opening Pandora's box by generating ICU diaries through artificial intelligence: A hypothetical study protocol.

BACKGROUND: Patients and families on Intensive Care Units (ICU) benefit from ICU diaries, enhancing ...

An Accelerometer-Based Wearable Patch for Robust Respiratory Rate and Wheeze Detection Using Deep Learning.

Wheezing is a critical indicator of various respiratory conditions, including asthma and chronic obs...

Robot-assisted thoracic surgery for benign tumors at the cervicothoracic junction: a propensity-matched study.

This study aimed to assess the feasibility and safety of robot-assisted thoracic surgery (RATS) for ...

A multi-branch convolutional neural network for snoring detection based on audio.

Obstructive sleep apnea (OSA) is associated with various health complications, and snoring is a prom...

Machine learning models to evaluate mortality in pediatric patients with pneumonia in the intensive care unit.

OBJECTIVES: This study aimed to predict mortality in children with pneumonia who were admitted to th...

Optimization strategy of community planning for environmental health and public health in smart city under multi-objectives.

As population density increases, environmental hygiene and public health become increasingly severe....

scMGCN: A Multi-View Graph Convolutional Network for Cell Type Identification in scRNA-seq Data.

Single-cell RNA sequencing (scRNA-seq) data reveal the complexity and diversity of cellular ecosyste...

Overcoming the Challenge of Accurate Segmentation of Lung Nodules: A Multi-crop CNN Approach.

Lung nodules are generated based on the growth of small and round- or oval-shaped cells in the lung,...

DeepVAQ : an adaptive deep learning for prediction of vascular access quality in hemodialysis patients.

BACKGROUND: Chronic kidney disease is a prevalent global health issue, particularly in advanced stag...

Respiratory Diseases Diagnosis Using Audio Analysis and Artificial Intelligence: A Systematic Review.

Respiratory diseases represent a significant global burden, necessitating efficient diagnostic metho...

Fluorescent Neuronal Cells v2: multi-task, multi-format annotations for deep learning in microscopy.

Fluorescent Neuronal Cells v2 is a collection of fluorescence microscopy images and the correspondin...

Single-port vs multi-port robot-assisted partial nephrectomy: A single center propensity score-matched analysis.

INTRODUCTION AND OBJECTIVES: The aim of the study is to compare key outcomes of Single-Port (SP) and...

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