Pulmonology

[Machine learning-based method for interpreting the guidelines of the diagnosis and treatment of COVID-19].

The outbreak of pneumonia caused by novel coronavirus (COVID-19) at the end of 2019 was a major public health emergency in human history. In a short period of time, Chinese medical workers have experienced the gradual understanding, evidence accumulation and clinical practice of the unknown virus. So far, National Health Commission of the People's Republic of China has issued seven trial versions ...

Repurposing factories with robotics in the face of COVID-19.

Can collaborative robots ramp up the production of medical ventilators?

Lung Nodule Detection from Feature Engineering to Deep Learning in Thoracic CT Images: a Comprehensive Review.

This paper presents a systematic review of the literature focused on the lung nodule detection in chest computed tomography (CT) images. Manual detect...

Diagnostic classification of solitary pulmonary nodules using support vector machine model based on 2-[18F]fluoro-2-deoxy-D-glucose PET/computed tomography texture features.

PURPOSE: This study aimed to evaluate the diagnostic value of a support vector machine (SVM) model built with texture features based on standard 2-[F]...

Artificial intelligence and the future of global health.

Concurrent advances in information technology infrastructure and mobile computing power in many low and middle-income countries (LMICs) have raised ho...

Comparative outcomes of robot-assisted minimally invasive versus open esophagectomy in patients with esophageal squamous cell carcinoma: a propensity score-weighted analysis.

Robots are increasingly used in minimally invasive surgery. We evaluated the clinical benefits of robot-assisted minimally invasive esophagectomy (RAM...

Deep Learning Localization of Pneumonia: 2019 Coronavirus (COVID-19) Outbreak.

An Automated Algorithm Incorporating Poincaré Analysis Can Quantify the Severity of Opioid-Induced Ataxic Breathing.

BACKGROUND: Opioid-induced respiratory depression (OIRD) is traditionally recognized by assessment of respiratory rate, arterial oxygen saturation, en...

Machine Learning/Deep Neuronal Network: Routine Application in Chest Computed Tomography and Workflow Considerations.

The constantly increasing number of computed tomography (CT) examinations poses major challenges for radiologists. In this article, the additional ben...

The Relative Importance of Clinical and Socio-demographic Variables in Prognostic Prediction in Non-Small Cell Lung Cancer: A Variable Importance Approach.

BACKGROUND: Prognostic modeling in health care has been predominantly statistical, despite a rapid growth of literature on machine-learning approaches...

Artificial Intelligence-based Fully Automated Per Lobe Segmentation and Emphysema-quantification Based on Chest Computed Tomography Compared With Global Initiative for Chronic Obstructive Lung Disease Severity of Smokers.

OBJECTIVES: The objective of this study was to evaluate an artificial intelligence (AI)-based prototype algorithm for the fully automated per lobe seg...

Machine Learning and Deep Neural Network Applications in the Thorax: Pulmonary Embolism, Chronic Thromboembolic Pulmonary Hypertension, Aorta, and Chronic Obstructive Pulmonary Disease.

The radiologic community is rapidly integrating a revolution that has not fully entered daily practice. It necessitates a close collaboration between ...

Classifying non-small cell lung cancer types and transcriptomic subtypes using convolutional neural networks.

OBJECTIVE: Non-small cell lung cancer is a leading cause of cancer death worldwide, and histopathological evaluation plays the primary role in its dia...

Prediction of Pulmonary to Systemic Flow Ratio in Patients With Congenital Heart Disease Using Deep Learning-Based Analysis of Chest Radiographs.

IMPORTANCE: Chest radiography is a useful noninvasive modality to evaluate pulmonary blood flow status in patients with congenital heart disease. Howe...

[Prediction of gene mutation in lung cancer based on deep learning and histomorphology analysis].

Lung cancer is a most common malignant tumor of the lung and is the cancer with the highest morbidity and mortality worldwide. For patients with advan...

A Deep Learning Approach to Antibiotic Discovery.

Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we traine...

[Application of Radiomics in Classification and Prediction of Benign and Malignant Lung Tumors].

Aiming at the lack of quantitative evaluation methods in clinical diagnosis of lung cancer, a classification and prediction model of lung cancer based...

Adverse drug event rates in pediatric pulmonary hypertension: a comparison of real-world data sources.

OBJECTIVE: Real-world data (RWD) are increasingly used for pharmacoepidemiology and regulatory innovation. Our objective was to compare adverse drug e...

Expert artificial intelligence-based natural language processing characterises childhood asthma.

INTRODUCTION: The lack of effective, consistent, reproducible and efficient asthma ascertainment methods results in inconsistent asthma cohorts and st...

AI-Driven COVID-19 Tools to Interpret, Quantify Lung Images.

Qualitative interpretation is a good thing when it comes to reading lung images in the fight against coronavirus 2019 disease (COVID-19), but quantita...