Pulmonology

Detection and analysis of COVID-19 in medical images using deep learning techniques.

The main purpose of this work is to investigate and compare several deep learning enhanced techniques applied to X-ray and CT-scan medical images for the detection of COVID-19. In this paper, we used four powerful pre-trained CNN models, VGG16, DenseNet121, ResNet50,and ResNet152, for the COVID-19 CT-scan binary classification task. The proposed Fast.AI ResNet framework was designed to find out th...

Analysis of COVID-19 severity from the perspective of coagulation index using evolutionary machine learning with enhanced brain storm optimization.

Coronavirus 2019 (COVID-19) is an extreme acute respiratory syndrome. Early diagnosis and accurate assessment of COVID-19 are not available, resulting in ineffective therapeutic therapy. This study designs an effective intelligence framework to early recognition and discrimination of COVID-19 severity from the perspective of coagulation indexes. The framework is proposed by integrating an enhanced...

A Combined Hot and Hypoxic Environment during Maximal Cycling Sprints Reduced Muscle Oxygen Saturation: A Pilot Study.

The present study investigated the effects of a combined hot and hypoxic environment on muscle oxygenation during repeated 15-s maximal cycling sprint...

Budget constrained machine learning for early prediction of adverse outcomes for COVID-19 patients.

The combination of machine learning (ML) and electronic health records (EHR) data may be able to improve outcomes of hospitalized COVID-19 patients th...

Interpretability-Driven Sample Selection Using Self Supervised Learning for Disease Classification and Segmentation.

In supervised learning for medical image analysis, sample selection methodologies are fundamental to attain optimum system performance promptly and wi...

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. Radiolog...

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 admitte...

Diagnostic test accuracy of artificial intelligence analysis of cross-sectional imaging in pulmonary hypertension: a systematic literature review.

OBJECTIVES: To undertake the first systematic review examining the performance of artificial intelligence (AI) applied to cross-sectional imaging for ...

Determining Top Fully Connected Layer's Hidden Neuron Count for Transfer Learning, Using Knowledge Distillation: a Case Study on Chest X-Ray Classification of Pneumonia and COVID-19.

Deep convolutional neural network (CNN)-assisted classification of images is one of the most discussed topics in recent years. Continuously innovation...

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...

Deep Learning and Risk Assessment in Acute Pulmonary Embolism.

MTU-COVNet: A hybrid methodology for diagnosing the COVID-19 pneumonia with optimized features from multi-net.

PURPOSE: The aim of this study was to establish and evaluate a fully automatic deep learning system for the diagnosis of COVID-19 using thoracic compu...

AI for the collective analysis of a massive number of genome sequences: various examples from the small genome of pandemic SARS-CoV-2 to the human genome.

In genetics and related fields, huge amounts of data, such as genome sequences, are accumulating, and the use of artificial intelligence (AI) suitable...

Detection of COVID-19 in Chest X-ray Images: A Big Data Enabled Deep Learning Approach.

Coronavirus disease (COVID-19) spreads from one person to another rapidly. A recently discovered coronavirus causes it. COVID-19 has proven to be chal...

Using different machine learning models to classify patients into mild and severe cases of COVID-19 based on multivariate blood testing.

COVID-19 is a serious respiratory disease. The ever-increasing number of cases is causing heavier loads on the health service system. Using 38 blood t...

A comparative analysis of eleven neural networks architectures for small datasets of lung images of COVID-19 patients toward improved clinical decisions.

The 2019 novel severe acute respiratory syndrome coronavirus 2-SARS-CoV2, commonly known as COVID-19, is a highly infectious disease that has endanger...

Performance of a 3D convolutional neural network in the detection of hypoperfusion at CT pulmonary angiography in patients with chronic pulmonary embolism: a feasibility study.

BACKGROUND: Chronic pulmonary embolism (CPE) is a life-threatening disease easily misdiagnosed on computed tomography. We investigated a three-dimensi...

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-...

CheXED: Comparison of a Deep Learning Model to a Clinical Decision Support System for Pneumonia in the Emergency Department.

PURPOSE: Patients with pneumonia often present to the emergency department (ED) and require prompt diagnosis and treatment. Clinical decision support ...

Efficient and targeted COVID-19 border testing via reinforcement learning.

Throughout the coronavirus disease 2019 (COVID-19) pandemic, countries have relied on a variety of ad hoc border control protocols to allow for non-es...