Critical Care

TSU-net: Two-stage multi-scale cascade and multi-field fusion U-net for right ventricular segmentation.

Accurate segmentation of the right ventricle from cardiac magnetic resonance images (MRI) is a critical step in cardiac function analysis and disease diagnosis. It is still an open problem due to some difficulties, such as a large variety of object sizes and ill-defined borders. In this paper, we present a TSU-net network that grips deeper features and captures targets of different sizes with mult...

Multi-Modal Residual Perceptron Network for Audio-Video Emotion Recognition.

Emotion recognition is an important research field for human-computer interaction. Audio-video emotion recognition is now attacked with deep neural network modeling tools. In published papers, as a rule, the authors show only cases of the superiority in multi-modality over audio-only or video-only modality. However, there are cases of superiority in uni-modality that can be found. In our research,...

Real-time prediction of intradialytic relative blood volume: a proof-of-concept for integrated cloud computing infrastructure.

BACKGROUND: Inadequate refilling from extravascular compartments during hemodialysis can lead to intradialytic symptoms, such as hypotension, nausea, ...

CNN-MoE Based Framework for Classification of Respiratory Anomalies and Lung Disease Detection.

This paper presents and explores a robust deep learning framework for auscultation analysis. This aims to classify anomalies in respiratory cycles and...

Imputation of the continuous arterial line blood pressure waveform from non-invasive measurements using deep learning.

In two-thirds of intensive care unit (ICU) patients and 90% of surgical patients, arterial blood pressure (ABP) is monitored non-invasively but interm...

COVID-19 diagnosis and severity detection from CT-images using transfer learning and back propagation neural network.

BACKGROUND: COVID-19 diagnosis in symptomatic patients is an important factor for arranging the necessary lifesaving facilities like ICU care and vent...

Dialysis adequacy predictions using a machine learning method.

Dialysis adequacy is an important survival indicator in patients with chronic hemodialysis. However, there are inconveniences and disadvantages to mea...

Interpretability of time-series deep learning models: A study in cardiovascular patients admitted to Intensive care unit.

Interpretability is fundamental in healthcare problems and the lack of it in deep learning models is currently the major barrier in the usage of such ...

Assessing the Adequacy of Hemodialysis Patients via the Graph-Based Takagi-Sugeno-Kang Fuzzy System.

Maintenance hemodialysis is the main method for the treatment of end-stage renal disease in China. The / value is the gold standard of hemodialysis ad...

MSDS-UNet: A multi-scale deeply supervised 3D U-Net for automatic segmentation of lung tumor in CT.

Lung cancer is one of the most common and deadly malignant cancers. Accurate lung tumor segmentation from CT is therefore very important for correct d...

Use of neurally adjusted ventilatory assist (NAVA) in a patient with severe SARS-CoV-2 pneumonia: A case report.

INTRODUCTION: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pneumonia may necessitate intubation and prolonged mechanical ventilation. ...

A model of modified -iodobenzylguanidine conjugated gold nanoparticles for neuroblastoma treatment.

Iodine-131 -iodobenzylguanidine (I-IBG) has been utilized as a standard treatment to minimize adverse side effects by targeting therapies to bind to t...

Efficacy and safety of an open lung ventilation strategy with staircase recruitment followed by comparison on two different modes of ventilation, in moderate ARDS in cirrhosis: A pilot randomized trial.

BACKGROUND: Mechanical ventilation in cirrhosis with acute respiratory distress syndrome (ARDS) is not widely studied. We aimed to study the effect of...

Three-stage segmentation of lung region from CT images using deep neural networks.

BACKGROUND: Lung region segmentation is an important stage of automated image-based approaches for the diagnosis of respiratory diseases. Manual metho...

Wireless Channel Modelling for Identifying Six Types of Respiratory Patterns With SDR Sensing and Deep Multilayer Perceptron.

Contactless or non-invasive technology has a significant impact on healthcare applications such as the prediction of COVID-19 symptoms. Non-invasive m...

Robot assisted minimally invasive esophagectomy: safety, perioperative morbidity and short-term oncological outcome-a single institution experience.

Robot assisted minimally invasive esophagectomy (RAMIE) has evolved over the past decade to become procedure of choice at many centers all over the wo...

Multi-Robot 2.5D Localization and Mapping Using a Monte Carlo Algorithm on a Multi-Level Surface.

Most indoor environments have wheelchair adaptations or ramps, providing an opportunity for mobile robots to navigate sloped areas avoiding steps. The...

Timesias: A machine learning pipeline for predicting outcomes from time-series clinical records.

The prediction of outcomes is a critical part of the clinical surveillance for hospitalized patients. Here, we present Timesias, a machine learning pi...

Comparison of deep learning, radiomics and subjective assessment of chest CT findings in SARS-CoV-2 pneumonia.

PURPOSE: Comparison of deep learning algorithm, radiomics and subjective assessment of chest CT for predicting outcome (death or recovery) and intensi...

Deep learning to predict long-term mortality in patients requiring 7 days of mechanical ventilation.

BACKGROUND: Among patients with acute respiratory failure requiring prolonged mechanical ventilation, tracheostomies are typically placed after approx...