Pulmonology

A benchmark of deep learning approaches to predict lung cancer risk using national lung screening trial cohort.

Deep learning (DL) methods have demonstrated remarkable effectiveness in assisting with lung cancer risk prediction tasks using computed tomography (CT) scans. However, the lack of comprehensive comparison and validation of state-of-the-art (SOTA) models in practical settings limits their clinical application. This study aims to review and analyze current SOTA deep learning models for lung cancer ...

Deep learning-based image domain reconstruction enhances image quality and pulmonary nodule detection in ultralow-dose CT with adaptive statistical iterative reconstruction-V.

OBJECTIVES: To evaluate the image quality and lung nodule detectability of ultralow-dose CT (ULDCT) with adaptive statistical iterative reconstruction-V (ASiR-V) post-processed using a deep learning image reconstruction (DLIR)-based image domain compared to low-dose CT (LDCT) and ULDCT without DLIR.

Integrating CNN and Bi-LSTM for protein succinylation sites prediction based on Natural Language Processing technique.

Protein succinylation, a post-translational modification wherein a succinyl group (-CO-CH₂-CH₂-CO-) attaches to lysine residues, plays a critical regu...

SMR-guided molecular subtyping and machine learning model reveals novel prognostic biomarkers and therapeutic targets in non-small cell lung adenocarcinoma.

Non-small cell lung adenocarcinoma (LUAD) is a markedly heterogeneous disease, with its underlying molecular mechanisms and prognosis prediction prese...

AI based medical imagery diagnosis for COVID-19 disease examination and remedy.

COVID-19, caused by the SARS-CoV-2 coronavirus, has spread to more than 200 countries, affecting millions, costing billions, and claiming nearly 2 mil...

Prediction of mortality risk in patients with severe community-acquired pneumonia in the intensive care unit using machine learning.

The aim of this study was to develop and validate a machine learning-based mortality risk prediction model for patients with severe community-acquired...

Cardiac MR image reconstruction using cascaded hybrid dual domain deep learning framework.

Recovering diagnostic-quality cardiac MR images from highly under-sampled data is a current research focus, particularly in addressing cardiac and res...

A comprehensive retrospect on the current perspectives and future prospects of pneumoconiosis.

Pneumoconiosis is a widespread occupational pulmonary disease caused by inhalation and retention of dust particles in the lungs, is characterized by c...

Multi-site, multi-vendor development and validation of a deep learning model for liver stiffness prediction using abdominal biparametric MRI.

BACKGROUND: Chronic liver disease (CLD) is a substantial cause of morbidity and mortality worldwide. Liver stiffness, as measured by MR elastography (...

Machine Learning Approach for Sepsis Risk Assessment in Ischemic Stroke Patients.

BackgroundIschemic stroke is a critical neurological condition, with infection representing a significant aspect of its clinical management. Sepsis, a...

Machine learning models provide modest accuracy in predicting clinical impact of porcine reproductive and respiratory syndrome type 2 in Canadian sow herds.

OBJECTIVE: To determine the predictive potential of the open reading frame 5 nucleotide sequence of porcine reproductive and respiratory syndrome (PRR...

Cost-effectiveness of a machine learning risk prediction model (LungFlag) in the selection of high-risk individuals for non-small cell lung cancer screening in Spain.

OBJECTIVE: The LungFlag risk prediction model uses individualized clinical variables to identify individuals at high-risk of non-small cell lung cance...

Phenotypic antibiotic resistance prediction using antibiotic resistance genes and machine learning models in Mannheimia haemolytica.

Mannheimia haemolytica is one of the most common causative agents of bovine respiratory disease (BRD); however, antibiotic resistance in this species ...

A machine learning model and identification of immune infiltration for chronic obstructive pulmonary disease based on disulfidptosis-related genes.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a chronic and progressive lung disease. Disulfidptosis-related genes (DRGs) may be involve...

Machine learning-derived asthma and allergy trajectories in children: a systematic review and meta-analysis.

INTRODUCTION: Numerous studies have characterised trajectories of asthma and allergy in children using machine learning, but with different techniques...

Enhanced swin transformer based tuberculosis classification with segmentation using chest X-ray.

BACKGROUND:: Tuberculosis disease is the disease that causes significant morbidity and mortality worldwide. Thus, early detection of the disease is cr...

VoxelMorph-Based Deep Learning Motion Correction for Ultrasound Localization Microscopy of Spinal Cord.

Accurate assessment of spinal cord vasculature is important for the urgent diagnosis of injury and subsequent treatment. Ultrasound localization micro...

Multimodal data deep learning method for predicting symptomatic pneumonitis caused by lung cancer radiotherapy combined with immunotherapy.

OBJECTIVES: The pairing of immunotherapy and radiotherapy in the treatment of locally advanced nonsmall cell lung cancer (NSCLC) has shown promise. By...

Integration of single-cell transcriptomics and bulk transcriptomics to explore prognostic and immunotherapeutic characteristics of nucleotide metabolism in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) is a highly aggressive tumor with one of the highest morbidity and mortality rates in the world. Nucleotide met...

Comprehensive bioinformatics analysis identifies metabolic and immune-related diagnostic biomarkers shared between diabetes and COPD using multi-omics and machine learning.

BACKGROUND: Diabetes and chronic obstructive pulmonary disease (COPD) are prominent global health challenges, each imposing significant burdens on aff...