Pulmonology

The smell of lung disease: a review of the current status of electronic nose technology.

There is a need for timely, accurate diagnosis, and personalised management in lung diseases. Exhaled breath reflects inflammatory and metabolic processes in the human body, especially in the lungs. The analysis of exhaled breath using electronic nose (eNose) technology has gained increasing attention in the past years. This technique has great potential to be used in clinical practice as a real-t...

Genetic dissection of complex traits using hierarchical biological knowledge.

Despite the growing constellation of genetic loci linked to common traits, these loci have yet to account for most heritable variation, and most act through poorly understood mechanisms. Recent machine learning (ML) systems have used hierarchical biological knowledge to associate genetic mutations with phenotypic outcomes, yielding substantial predictive power and mechanistic insight. Here, we use...

Cefepime Induced Neurotoxicity Following A Regimen Dose-Adjusted for Renal Function: Case Report and Review of the Literature.

Cefepime induced neurotoxicity (CIN) is commonly associated with renal dysfunction, however CIN can occur in patients with normal renal function or r...

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...

New Technique for Introducing a Surgical Stapler during Robot-Assisted Lobectomy for Lung Cancer.

BACKGROUND: The da Vinci Si version robot lacks a vascular stapler that can be controlled by the operating surgeon at the surgical console when dividi...

An [18F]FDG-PET/CT deep learning method for fully automated detection of pathological mediastinal lymph nodes in lung cancer patients.

PURPOSE: The identification of pathological mediastinal lymph nodes is an important step in the staging of lung cancer, with the presence of metastase...

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...

Deformable registration of chest CT images using a 3D convolutional neural network based on unsupervised learning.

PURPOSE: The deformable registration of 3D chest computed tomography (CT) images is one of the most important tasks in the field of medical image regi...

A New General Maximum Intensity Projection Technology via the Hybrid of U-Net and Radial Basis Function Neural Network.

Maximum intensity projection (MIP) technology is a computer visualization method that projects three-dimensional spatial data on a visualization plane...

A Hybrid Method to Predict Postoperative Survival of Lung Cancer Using Improved SMOTE and Adaptive SVM.

Predicting postoperative survival of lung cancer patients (LCPs) is an important problem of medical decision-making. However, the imbalanced distribut...

Technical details for a robot-assisted hand-sewn esophago-gastric anastomosis during minimally invasive Ivor Lewis esophagectomy.

BACKGROUND: Minimally invasive Ivor Lewis esophagectomy (MIILE) provides better outcomes than open techniques, particularly in terms of post-operative...

DR-MIL: deep represented multiple instance learning distinguishes COVID-19 from community-acquired pneumonia in CT images.

BACKGROUND AND OBJECTIVE: Given that the novel coronavirus disease 2019 (COVID-19) has become a pandemic, a method to accurately distinguish COVID-19 ...

Automated machine learning for endemic active tuberculosis prediction from multiplex serological data.

Serological diagnosis of active tuberculosis (TB) is enhanced by detection of multiple antibodies due to variable immune responses among patients. Cli...

Towards Intraoperative Quantification of Atrial Fibrosis Using Light-Scattering Spectroscopy and Convolutional Neural Networks.

Light-scattering spectroscopy (LSS) is an established optical approach for characterization of biological tissues. Here, we investigated the capabilit...

Selection, Visualization, and Interpretation of Deep Features in Lung Adenocarcinoma and Squamous Cell Carcinoma.

Although deep learning networks applied to digital images have shown impressive results for many pathology-related tasks, their black-box approach and...

In utero exposure to electronic-cigarette aerosols decreases lung fibrillar collagen content, increases Newtonian resistance and induces sex-specific molecular signatures in neonatal mice.

Approximately 7% of pregnant women in the United States use electronic-cigarette (e-cig) devices during pregnancy. There is, however, no scientific ev...

Applying interpretable deep learning models to identify chronic cough patients using EHR data.

BACKGROUND AND OBJECTIVE: Chronic cough (CC) affects approximately 10% of adults. Many disease states are associated with chronic cough, such as asthm...

Intravenous Iron Replacement Improves Exercise Tolerance in COPD: A Single-Blind Randomized Trial.

INTRODUCTION: Iron deficiency affects exercise capacity because of the critical role iron plays in the optimal functioning of skeletal muscle metaboli...