Pulmonology

Commentary: Artificial intelligence for pulmonary nodules: Machines to diagnosis cancer.

An annotation-free whole-slide training approach to pathological classification of lung cancer types using deep learning.

Deep learning for digital pathology is hindered by the extremely high spatial resolution of whole-sl...

Deep Learning for Detection of Elevated Pulmonary Artery Wedge Pressure Using Standard Chest X-Ray.

BACKGROUND: To accurately diagnose and control heart failure (HF), it is important to carry out a si...

Early risk assessment for COVID-19 patients from emergency department data using machine learning.

Since its emergence in late 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) h...

Correlation between lung infection severity and clinical laboratory indicators in patients with COVID-19: a cross-sectional study based on machine learning.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has caused a global pandemic that has raised worldwi...

Predicting benign, preinvasive, and invasive lung nodules on computed tomography scans using machine learning.

OBJECTIVE: The study objective was to investigate if machine learning algorithms can predict whether...

Missed Incidental Pulmonary Embolism: Harnessing Artificial Intelligence to Assess Prevalence and Improve Quality Improvement Opportunities.

PURPOSE: Incidental pulmonary embolism (IPE) can be found on body CT. The aim of this study was to e...

Suspected Acute Pulmonary Embolism: Gestalt, Scoring Systems, and Artificial Intelligence.

Pulmonary embolism (PE) remains a diagnostic challenge in 2021. As the pathology is potentially fata...

From predictions to prescriptions: A data-driven response to COVID-19.

The COVID-19 pandemic has created unprecedented challenges worldwide. Strained healthcare providers ...

Machine Learning-Derived Fractal Features of Shape and Texture of the Left Atrium and Pulmonary Veins From Cardiac Computed Tomography Scans Are Associated With Risk of Recurrence of Atrial Fibrillation Postablation.

[Figure: see text].

GroupRegNet: a groupwise one-shot deep learning-based 4D image registration method.

Accurate deformable four-dimensional (4D) (three-dimensional in space and time) medical images regis...

Using machine learning to investigate the relationship between domains of functioning and functional mobility in older adults.

Previous studies have shown that functional mobility, along with other physical functions, decreases...

Feasibility of machine learning methods for predicting hospital emergency room visits for respiratory diseases.

The prediction of hospital emergency room visits (ERV) for respiratory diseases after the outbreak o...

A Machine Learning Prediction Model of Respiratory Failure Within 48 Hours of Patient Admission for COVID-19: Model Development and Validation.

BACKGROUND: Predicting early respiratory failure due to COVID-19 can help triage patients to higher ...

Deep learning-based differentiation of invasive adenocarcinomas from preinvasive or minimally invasive lesions among pulmonary subsolid nodules.

OBJECTIVES: To evaluate a deep learning-based model using model-generated segmentation masks to diff...

Genomic sequence analysis of lung infections using artificial intelligence technique.

Attributable to the modernization of Artificial Intelligence (AI) procedures in healthcare services,...

Unsupervised Deep Anomaly Detection in Chest Radiographs.

The purposes of this study are to propose an unsupervised anomaly detection method based on a deep n...

A multipurpose machine learning approach to predict COVID-19 negative prognosis in São Paulo, Brazil.

The new coronavirus disease (COVID-19) is a challenge for clinical decision-making and the effective...

A Staging Auxiliary Diagnosis Model for Nonsmall Cell Lung Cancer Based on the Intelligent Medical System.

At present, human health is threatened by many diseases, and lung cancer is one of the most dangerou...

Lung Cancer and Granuloma Identification Using a Deep Learning Model to Extract 3-Dimensional Radiomics Features in CT Imaging.

BACKGROUND: We aimed to evaluate a deep learning (DL) model combining perinodular and intranodular r...

Toward data-efficient learning: A benchmark for COVID-19 CT lung and infection segmentation.

PURPOSE: Accurate segmentation of lung and infection in COVID-19 computed tomography (CT) scans play...