Critical Care

Direct machine learning reconstruction of respiratory variation waveforms from resting state fMRI data in a pediatric population.

In many functional magnetic resonance imaging (fMRI) studies, respiratory signals are unavailable or do not have acceptable quality due to issues with subject compliance, equipment failure or signal error. In large databases, such as the Human Connectome Projects, over half of the respiratory recordings may be unusable. As a result, the direct removal of low frequency respiratory variations from t...

Image augmentation and automated measurement of endotracheal-tube-to-carina distance on chest radiographs in intensive care unit using a deep learning model with external validation.

BACKGROUND: Chest radiographs are routinely performed in intensive care unit (ICU) to confirm the correct position of an endotracheal tube (ETT) relative to the carina. However, their interpretation is often challenging and requires substantial time and expertise. The aim of this study was to propose an externally validated deep learning model with uncertainty quantification and image segmentation...

Evaluations on supervised learning methods in the calibration of seven-hole pressure probes.

Machine learning method has become a popular, convenient and efficient computing tool applied to many industries at present. Multi-hole pressure probe...

Machine learning using multi-modal data predicts the production of selective laser sintered 3D printed drug products.

Three-dimensional (3D) printing is drastically redefining medicine production, offering digital precision and personalized design opportunities. One e...

Prospective Real-Time Validation of a Lung Ultrasound Deep Learning Model in the ICU.

OBJECTIVES: To evaluate the accuracy of a bedside, real-time deployment of a deep learning (DL) model capable of distinguishing between normal (A line...

Knowledge Graph Embeddings for ICU readmission prediction.

BACKGROUND: Intensive Care Unit (ICU) readmissions represent both a health risk for patients,with increased mortality rates and overall health deterio...

Two-Step Approach for Occupancy Estimation in Intensive Care Units Based on Bayesian Optimization Techniques.

Due to the high occupational pressure suffered by intensive care units (ICUs), a correct estimation of the patients' length of stay (LoS) in the ICU i...

Deep learning for improving ZTE MRI images in free breathing.

INTRODUCTION: Despite a growing interest in lung MRI, its broader use in a clinical setting remains challenging. Several factors limit the image quali...

NVTrans-UNet: Neighborhood vision transformer based U-Net for multi-modal cardiac MR image segmentation.

With the rapid development of artificial intelligence and image processing technology, medical imaging technology has turned into a critical tool for ...

A comparison of total thoracoscopic versus robotic approach for cardiac myxoma resection: a single-center retrospective study.

Advances in instrumentation and technique have facilitated minimally invasive surgeries for cardiac myxoma treatment. This study aims to compare the c...

Reliability of respiratory-gated real-time two-dimensional cine incorporating deep learning reconstruction for the assessment of ventricular function in an adult population.

This study aimed to assess the image quality and accuracy of respiratory-gated real-time two-dimensional (2D) cine incorporating deep learning reconst...

FedSepsis: A Federated Multi-Modal Deep Learning-Based Internet of Medical Things Application for Early Detection of Sepsis from Electronic Health Records Using Raspberry Pi and Jetson Nano Devices.

The concept of the Internet of Medical Things brings a promising option to utilize various electronic health records stored in different medical devic...

Machine Learning Models for the Automatic Detection of Exercise Thresholds in Cardiopulmonary Exercising Tests: From Regression to Generation to Explanation.

The cardiopulmonary exercise test (CPET) constitutes a gold standard for the assessment of an individual's cardiovascular fitness. A trend is emerging...

Compensation for respiratory motion-induced signal loss and phase corruption in free-breathing self-navigated cine DENSE using deep learning.

PURPOSE: To introduce a model that describes the effects of rigid translation due to respiratory motion in displacement encoding with stimulated echoe...

Haemodynamic and respiratory perioperative outcomes for open versus robot-assisted radical cystectomy: A double-blinded, randomised trial.

BACKGROUND: The clinical impact of prolonged steep Trendelenburg position and CO pneumoperitoneum during robot-assisted radical cystectomy (RC) on int...

Clustering of critically ill patients using an individualized learning approach enables dose optimization of mobilization in the ICU.

BACKGROUND: While early mobilization is commonly implemented in intensive care unit treatment guidelines to improve functional outcome, the characteri...

Database-based machine learning in sepsis deserves attention.

Artificial intelligence-based multi-omics analysis fuels cancer precision medicine.

With biotechnological advancements, innovative omics technologies are constantly emerging that have enabled researchers to access multi-layer informat...

An accurate deep learning model for wheezing in children using real world data.

Auscultation is an important diagnostic method for lung diseases. However, it is a subjective modality and requires a high degree of expertise. To ove...

Predicting Genetic Disorder and Types of Disorder Using Chain Classifier Approach.

Genetic disorders are the result of mutation in the deoxyribonucleic acid (DNA) sequence which can be developed or inherited from parents. Such mutati...