Pulmonology

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

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Integrating radiomic and 3D autoencoder-based features for Non-Small Cell Lung Cancer survival analysis.

BACKGROUND AND OBJECTIVES: The aim of this study is to develop a radiomic and deep learning-based si...

An explainable deep learning model to predict partial anomalous pulmonary venous connection for patients with atrial septal defect.

BACKGROUND: Patients with partial anomalous pulmonary venous connection (PAPVC) usually present asym...

Downscaling MODIS evapotranspiration into finer resolution using machine learning approach on a small scale, Ribb watershed, Ethiopia.

By monitoring evapotranspiration (ET), the exchange of water and energy between the soil, plants, an...

Development and external validation of an interpretable machine learning model for the prediction of intubation in the intensive care unit.

Given the limited capacity to accurately determine the necessity for intubation in intensive care un...

Interpretable machine learning uncovers epithelial transcriptional rewiring and a role for Gelsolin in COPD.

Transcriptomic analyses have advanced the understanding of complex disease pathophysiology including...

Development and Validation of Deep Learning-Based Infectivity Prediction in Pulmonary Tuberculosis Through Chest Radiography: Retrospective Study.

BACKGROUND: Pulmonary tuberculosis (PTB) poses a global health challenge owing to the time-intensive...

Predictive modeling of COVID-19 mortality risk in chronic kidney disease patients using multiple machine learning algorithms.

The coronavirus disease 2019 (COVID-19) has a significant impact on the global population, particula...

Optimizing pulmonary chest x-ray classification with stacked feature ensemble and swin transformer integration.

This research presents an integrated framework designed to automate the classification of pulmonary ...

Mitigating Diagnostic Errors in Lung Cancer Classification: A Multi-Eyes Principle to Uncertainty Quantification.

In radiology, particularly in lung cancer diagnosis, diagnostic errors and cognitive biases pose sub...

Non-small cell lung cancer detection through knowledge distillation approach with teaching assistant.

Non-small cell lung cancer (NSCLC) exhibits a comparatively slower rate of metastasis in contrast to...

Deep learning approaches for automated classification of neonatal lung ultrasound with assessment of human-to-AI interrater agreement.

Neonatal respiratory disorders pose significant challenges in clinical settings, often requiring rap...

Computer vision applications for the detection or analysis of tuberculosis using digitised human lung tissue images - a systematic review.

OBJECTIVE: To conduct a systematic review of the computer vision applications that detect, diagnose,...

Screening the Best Risk Model and Susceptibility SNPs for Chronic Obstructive Pulmonary Disease (COPD) Based on Machine Learning Algorithms.

BACKGROUND AND PURPOSE: Chronic obstructive pulmonary disease (COPD) is a common and progressive dis...

Artificial intelligence-aided colonoscopic differential diagnosis between Crohn's disease and gastrointestinal tuberculosis.

BACKGROUND AND AIM: Differentiating between Crohn's disease (CD) and gastrointestinal tuberculosis (...

Evaluating machine learning model bias and racial disparities in non-small cell lung cancer using SEER registry data.

BACKGROUND: Despite decades of pursuing health equity, racial and ethnic disparities persist in heal...

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