Critical Care

3D multi-scale, multi-task, and multi-label deep learning for prediction of lymph node metastasis in T1 lung adenocarcinoma patients' CT images.

The diagnosis of preoperative lymph node (LN) metastasis is crucial to evaluate possible therapy options for T1 lung adenocarcinoma patients. Radiologists preoperatively diagnose LN metastasis by evaluating signs related to LN metastasis, like spiculation or lobulation of pulmonary nodules in CT images. However, this type of evaluation is subjective and time-consuming, which may result in poor con...

Immediate effects of manual hyperinflation on cardiorespiratory function and sputum clearance in mechanically ventilated pediatric patients: A randomized crossover trial.

BACKGROUND: In developing countries, lower respiratory tract infection is a major cause of death in children, with severely ill patients being admitted to the critical-care unit. While physical therapists commonly use the manual hyperinflation (MHI) technique for secretion mass clearance in critical-care patients, its efficacy has not been determined in pediatric patients.

Transhiatal robot-assisted minimally invasive esophagectomy: unclear benefits compared to traditional transhiatal esophagectomy.

Esophagectomy is a high-risk operation, regardless of technique. Minimally invasive transthoracic esophagectomy could reduce length of stay and pulmon...

eARDS: A multi-center validation of an interpretable machine learning algorithm of early onset Acute Respiratory Distress Syndrome (ARDS) among critically ill adults with COVID-19.

We present an interpretable machine learning algorithm called 'eARDS' for predicting ARDS in an ICU population comprising COVID-19 patients, up to 12-...

Short-term and pathologic outcomes of robotic versus open pancreatoduodenectomy for periampullary and pancreatic head malignancy: an early experience.

Open pancreatoduodenectomy (OPD) is associated with high perioperative morbidity. Adoption of robot-assisted pancreatoduodenectomy (RAPD) has been slo...

FluNet: An AI-Enabled Influenza-Like Warning System.

Influenza is an acute viral respiratory disease that is currently causing severe financial and resource strains worldwide. With the COVID-19 pandemic ...

Federated learning for predicting clinical outcomes in patients with COVID-19.

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity,...

Wearable RF Near-Field Cough Monitoring by Frequency-Time Deep Learning.

Coughing is a common symptom for many respiratory disorders, and can spread droplets of various sizes containing bacterial and viral pathogens. Mild c...

Predicting clinical outcomes in COVID-19 using radiomics on chest radiographs.

OBJECTIVES: For optimal utilization of healthcare resources, there is a critical need for early identification of COVID-19 patients at risk of poor pr...

DMC-Fusion: Deep Multi-Cascade Fusion With Classifier-Based Feature Synthesis for Medical Multi-Modal Images.

Multi-modal medical image fusion is a challenging yet important task for precision diagnosis and surgical planning in clinical practice. Although sing...

Automated segmentation of biventricular contours in tissue phase mapping using deep learning.

Tissue phase mapping (TPM) is an MRI technique for quantification of regional biventricular myocardial velocities. Despite its potential, clinical use...

Use of a Digital Chronic Obstructive Pulmonary Disease Respiratory Tracker in a Primary Care Setting: A Feasibility Study.

INTRODUCTION: Telemonitoring is a promising self-management strategy to improve health care outcomes. This study evaluated real-world adoption of the ...

Accurate diagnosis of sepsis using a neural network: Pilot study using routine clinical variables.

BACKGROUND AND OBJECTIVES: Sepsis is a severe infection that increases mortality risk and is one if the main causes of death in intensive care units. ...

Cross-Modality Interaction Network for Equine Activity Recognition Using Imbalanced Multi-Modal Data.

With the recent advances in deep learning, wearable sensors have increasingly been used in automated animal activity recognition. However, there are t...

Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning.

Auscultation has been essential part of the physical examination; this is non-invasive, real-time, and very informative. Detection of abnormal respira...

Two-phase non-invasive multi-disease detection via sublingual region.

Non-invasive multi-disease detection is an active technology that detects human diseases automatically. By observing images of the human body, compute...

Nasopharyngeal metabolomics and machine learning approach for the diagnosis of influenza.

BACKGROUND: Respiratory virus infections are significant causes of morbidity and mortality, and may induce host metabolite alterations by infecting re...

An approach to rapidly assess sepsis through multi-biomarker host response using machine learning algorithm.

Sepsis is a life-threatening condition and understanding the disease pathophysiology through the use of host immune response biomarkers is critical fo...

Piezoelectric Smart Patch Operated with Machine-Learning Algorithms for Effective Detection and Elimination of Condensation.

Timely detection and elimination of surface condensation is crucial for diverse applications in agriculture, automotive, oil and gas industries, and r...

Machine learning identifies ICU outcome predictors in a multicenter COVID-19 cohort.

BACKGROUND: Intensive Care Resources are heavily utilized during the COVID-19 pandemic. However, risk stratification and prediction of SARS-CoV-2 pati...