Pulmonology

Latest AI and machine learning research in pulmonology for healthcare professionals.

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Deep Learning to Detect Pulmonary Hypertension from the Chest X-Ray Images of Patients with Systemic Sclerosis.

Pulmonary hypertension (PH) is a serious prognostic complication in patients with systemic sclerosis...

Prediction of acute respiratory infections using machine learning techniques in Amhara Region, Ethiopia.

Many studies have shown that infectious diseases are responsible for the majority of deaths in child...

A comprehensive study on tuberculosis prediction models: Integrating machine learning into epidemiological analysis.

Tuberculosis (TB), the second leading infectious killer globally, claimed the lives of 1.3 million i...

Cultivating diagnostic clarity: The importance of reporting artificial intelligence confidence levels in radiologic diagnoses.

Accurate image interpretation is essential in the field of radiology to the healthcare team in order...

Lymph Node Metastasis Prediction From In Situ Lung Squamous Cell Carcinoma Histopathology Images Using Deep Learning.

Lung squamous cell carcinoma (LUSC), a subtype of non-small cell lung cancer, represents a significa...

Transfer learning in spirometry: CNN models for automated flow-volume curve quality control in paediatric populations.

PROBLEM: Current spirometers face challenges in evaluating acceptability criteria, often requiring m...

SPE-YOLO: A deep learning model focusing on small pulmonary embolism detection.

OBJECTIVES: By developing the deep learning model SPE-YOLO, the detection of small pulmonary embolis...

Transformer-based deep learning model for the diagnosis of suspected lung cancer in primary care based on electronic health record data.

BACKGROUND: Due to its late stage of diagnosis lung cancer is the commonest cause of death from canc...

Advantages of Metabolomics-Based Multivariate Machine Learning to Predict Disease Severity: Example of COVID.

The COVID-19 outbreak caused saturations of hospitals, highlighting the importance of early patient ...

Predicting paediatric asthma exacerbations with machine learning: a systematic review with meta-analysis.

BACKGROUND: Asthma exacerbations in children pose a significant burden on healthcare systems and fam...

Data-driven explainable machine learning for personalized risk classification of myasthenic crisis.

OBJECTIVE: Myasthenic crisis (MC) is a critical progression of Myasthenia gravis (MG), requiring int...

Diagnostic performance of an artificial intelligence model for the detection of pneumothorax at chest X-ray.

PURPOSE: Pneumothorax (PTX) is a common clinical urgency, its diagnosis is usually performed on ches...

Identification of TXN and F5 as novel diagnostic gene biomarkers of the severe asthma based on bioinformatics and machine learning analysis.

Asthma poses a major threat to human health. The aim of this study was to identify genetic markers o...

Enhancing multiclass COVID-19 prediction with ESN-MDFS: Extreme smart network using mean dropout feature selection technique.

Deep learning and artificial intelligence offer promising tools for improving the accuracy and effic...

Explainable machine learning model for predicting the risk of significant liver fibrosis in patients with diabetic retinopathy.

BACKGROUND: Diabetic retinopathy (DR), a prevalent complication in patients with type 2 diabetes, ha...

Image-based deep learning in diagnosing mycoplasma pneumonia on pediatric chest X-rays.

BACKGROUND: Correctly diagnosing and accurately distinguishing mycoplasma pneumonia in children has ...

A novel benign and malignant classification model for lung nodules based on multi-scale interleaved fusion integrated network.

One of the precursors of lung cancer is the presence of lung nodules, and accurate identification of...

Analyzing immune cell infiltrates in skeletal muscle of infantile-onset Pompe disease using bioinformatics and machine learning.

Pompe disease, a severe lysosomal storage disorder, is marked by heart problems, muscle weakness, an...

Statistical inference and neural network training based on stochastic difference model for air pollution and associated disease transmission.

A polluted air environment can potentially provoke infections of diverse respiratory diseases. The d...

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