Pulmonology

Integrated Cells and Collagen Fibers Spatial Image Analysis.

Modern technologies designed for tissue structure visualization like brightfield microscopy, fluorescent microscopy, mass cytometry imaging (MCI) and mass spectrometry imaging (MSI) provide large amounts of quantitative and spatial information about cells and tissue structures like vessels, bronchioles etc. Many published reports have demonstrated that the structural features of cells and extracel...

Deep learning predicts epidermal growth factor receptor mutation subtypes in lung adenocarcinoma.

PURPOSE: This study aimed to explore the predictive ability of deep learning (DL) for the common epidermal growth factor receptor (EGFR) mutation subtypes in patients with lung adenocarcinoma.

The utility of color normalization for AI-based diagnosis of hematoxylin and eosin-stained pathology images.

The color variation of hematoxylin and eosin (H&E)-stained tissues has presented a challenge for applications of artificial intelligence (AI) in digit...

Automated interpretation and analysis of bronchoalveolar lavage fluid.

BACKGROUND: The cytological analysis of bronchoalveolar lavage fluid (BALF) plays an essential role in the differential diagnosis of respiratory disea...

COVID-19 Screening in Chest X-Ray Images Using Lung Region Priors.

Early screening of COVID-19 is essential for pandemic control, and thus to relieve stress on the health care system. Lung segmentation from chest X-ra...

A Novel COVID-19 Diagnosis Support System Using the Stacking Approach and Transfer Learning Technique on Chest X-Ray Images.

COVID-19 is an infectious disease-causing flu-like respiratory problem with various symptoms such as cough or fever, which in severe cases can cause p...

A deep learning approach using effective preprocessing techniques to detect COVID-19 from chest CT-scan and X-ray images.

Coronavirus disease-19 (COVID-19) is a severe respiratory viral disease first reported in late 2019 that has spread worldwide. Although some wealthy c...

Machine learning versus traditional methods for the development of risk stratification scores: a case study using original Canadian Syncope Risk Score data.

Artificial Intelligence and machine learning (ML) methods are promising for risk-stratification, but the added benefit over traditional statistical me...

Identifying clinical phenotypes in extremely low birth weight infants-an unsupervised machine learning approach.

There is increasing evidence that patient heterogeneity significantly hinders advancement in clinical trials and individualized care. This study aimed...

First molecular modelling report on tri-substituted pyrazolines as phosphodiesterase 5 (PDE5) inhibitors through classical and machine learning based multi-QSAR analysis.

Phosphodiesterase 5 (PDE5) falls under a broad category of metallohydrolase enzymes responsible for the catalysis of the phosphodiesterase bond, and t...

Identification of Benign and Malignant Lung Nodules in CT Images Based on Ensemble Learning Method.

BACKGROUND AND OBJECTIVE: Under the background of urgent need for computer-aided technology to provide physicians with objective decision support, aim...

Recurrent Wheeze Exacerbations Following Acute Bronchiolitis-A Machine Learning Approach.

Acute bronchiolitis is one of the most common respiratory infections in infancy. Although most infants with bronchiolitis do not get hospitalized, in...

A Machine-Learning-Based System for Prediction of Cardiovascular and Chronic Respiratory Diseases.

Cardiovascular and chronic respiratory diseases are global threats to public health and cause approximately 19 million deaths worldwide annually. This...

QQ-NET - using deep learning to solve quantitative susceptibility mapping and quantitative blood oxygen level dependent magnitude (QSM+qBOLD or QQ) based oxygen extraction fraction (OEF) mapping.

PURPOSE: To improve accuracy and speed of quantitative susceptibility mapping plus quantitative blood oxygen level-dependent magnitude (QSM+qBOLD or Q...

Deep learning identified pathological abnormalities predictive of graft loss in kidney transplant biopsies.

Interstitial fibrosis, tubular atrophy, and inflammation are major contributors to kidney allograft failure. Here we sought an objective, quantitative...

A deep learning- and CT image-based prognostic model for the prediction of survival in non-small cell lung cancer.

OBJECTIVE: To assist clinicians in arranging personalized treatment, planning follow-up programs and extending survival times for non-small cell lung ...

Predictive value of a novel Asian lung cancer screening nomogram based on artificial intelligence and epidemiological characteristics.

BACKGROUND: To develop and validate a risk prediction nomogram based on a deep learning convolutional neural networks (CNN) model and epidemiological ...

Role of Deep Learning in Predicting Aging-Related Diseases: A Scoping Review.

Aging refers to progressive physiological changes in a cell, an organ, or the whole body of an individual, over time. Aging-related diseases are highl...

Machine learning in the prediction of medical inpatient length of stay.

Length of stay (LOS) estimates are important for patients, doctors and hospital administrators. However, making accurate estimates of LOS can be diffi...

SquiggleNet: real-time, direct classification of nanopore signals.

We present SquiggleNet, the first deep-learning model that can classify nanopore reads directly from their electrical signals. SquiggleNet operates fa...