Pulmonology

Predictive value of machine learning for radiation pneumonitis and checkpoint inhibitor pneumonitis in lung cancer patients: a systematic review and meta-analysis.

Some studies have developed machine learning (ML) models for the prediction of pneumonitis following immunotherapy and radiotherapy for patients with lung cancer (LC). However, the prediction accuracy of these models remains a topic of debate. Thus, this study aims to summarize the advantages of ML methods in the early prediction of radiation pneumonitis (RP) and checkpoint inhibitor pneumonitis (...

Identification of potential pathogenic genes associated with the comorbidity of rheumatoid arthritis and renal fibrosis using bioinformatics and machine learning.

This study aimed to identify the potential pathogenic genes associated with the comorbidity of rheumatoid arthritis (RA) and renal fibrosis (RF). Transcriptomic data related to RA and RF were retrieved from the GEO database. Differential expression gene analysis (DEGs) and weighted gene co-expression network analysis (WGCNA) were carried out to identify the RA-RF-DEGs. Subsequently, functional enr...

Profiling short-term longitudinal severity progression and associated genes in COVID-19 patients using EHR and single-cell analysis.

Here we propose CovSF, a deep learning model designed to track and forecast short-term severity progression of COVID-19 patients using longitudinal cl...

Deep learning-based lung cancer classification of CT images.

Lung cancer remains a leading cause of cancer-related deaths worldwide, with accurate classification of lung nodules being critical for early diagnosi...

Enhanced pulmonary nodule detection with U-Net, YOLOv8, and swin transformer.

RATIONALE AND OBJECTIVES: Lung cancer remains the leading cause of cancer-related mortality worldwide, emphasizing the critical need for early pulmona...

Mapping the EORTC QLQ-C30 and QLQ-LC13 to the SF-6D utility index in patients with lung cancer using machine learning and traditional regression methods.

BACKGROUND: Preference-based measures of health-related quality of life (HRQoL), such as the Short Form Six-Dimension (SF-6D) is essential for health ...

Forecasting tuberculosis in Ethiopia using deep learning: progress toward sustainable development goal evidence from global burden of disease 1990-2021.

BACKGROUND: Tuberculosis (TB) is a preventable and treatable disease caused by Mycobacterium tuberculosis, which most often affects lungs and remains ...

2.5D deep learning radiomics and clinical data for predicting occult lymph node metastasis in lung adenocarcinoma.

BACKGROUND: Occult lymph node metastasis (OLNM) refers to lymph node involvement that remains undetectable by conventional imaging techniques, posing ...

Automatic recognition and differentiation of pulmonary contusion and bacterial pneumonia based on deep learning and radiomics.

BACKGROUND: In clinical work, there are difficulties in distinguishing pulmonary contusion(PC) from bacterial pneumonia(BP) on CT images by the naked ...

Utilization of artificial intelligence-based pulmonary nodule target reconstruction software in clinical practice education for standardized training residents.

Deep learning-based cough classification using application-recorded sounds: a transfer learning approach with VGGish.

BACKGROUND: Coughing sounds contain various bio-metric information with regards to respiratory diseases that can help in the assessment of respiratory...

Machine learning for detection of diffusion abnormalities-related respiratory changes among normal, overweight, and obese individuals based on BMI and pulmonary ventilation parameters: an observational study.

BACKGROUND: The integration of machine learning (ML) algorithms enables the detection of diffusion abnormalities-related respiratory changes in indivi...

A real-world data analysis of montelukast in FDA Adverse Event Reporting System (FAERS) database.

BACKGROUND: Montelukast(MTK) is a leukotriene receptor antagonist widely used clinically for treating asthma and rhinitis. However, many adverse event...

Multiomics and Machine Learning Identify Immunometabolic Biomarkers for Active Tuberculosis Diagnosis Against Nontuberculous Mycobacteria and Latent Tuberculosis Infection.

This study utilized multiomics combined with a comprehensive machine learning-based predictive modeling approach to identify, validate, and prioritize...

Thoracic staging of lung cancers by FDG-PET/CT: impact of artificial intelligence on the detection of associated pulmonary nodules.

This study focuses on automating the classification of certain thoracic lung cancer stages in 3D FDG-PET/CT images according to the 9th Edition of the...

Improving Robustness and Reliability in Medical Image Classification with Latent-Guided Diffusion and Nested-Ensembles.

Once deployed, medical image analysis methods are often faced with unexpected image corruptions and noise perturbations. These unknown covariate shift...

A Review of Medical Oxygen Concentrators for Respiratory Applications.

Medical oxygen concentrators are vital apparatuses that deliver supplemental oxygen to persons with hypoxemia. This narrative review provides a review...

Biologics for severe asthma: deciphering what is best for the patient.

INTRODUCTION: Choosing the right biologic for the right patient is challenging. It requires evaluating patient characteristics and disease manifestati...

Discovery of SARS-CoV-2 papain-like protease inhibitors through machine learning and molecular simulation approaches.

The papain-like protease (PLpro), a cysteine protease found in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), plays a crucial role in v...

A Wearable AI-Driven Mask with Humidity-Sensing Respiratory Microphone for Non-Vocal Communication.

Hoarseness and dysphonia caused by vocal cord conditions or laryngeal surgeries significantly hinder communication and quality of life. This study pre...