Pulmonology

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

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Development and application of a deep learning-based comprehensive early diagnostic model for chronic obstructive pulmonary disease.

BACKGROUND: Chronic obstructive pulmonary disease (COPD) is a frequently diagnosed yet treatable con...

Prediction of tumor origin in cancers of unknown primary origin with cytology-based deep learning.

Cancer of unknown primary (CUP) site poses diagnostic challenges due to its elusive nature. Many cas...

A serial image analysis architecture with positron emission tomography using machine learning combined for the detection of lung cancer.

INTRODUCTION AND OBJECTIVES: Lung cancer is the second type of cancer with the second highest incide...

Apnoea detection using ECG signal based on machine learning classifiers and its performances.

Sleep apnoea is a common disorder affecting sleep quality by obstructing the respiratory airway. Thi...

A machine learning algorithm for detecting abnormal patterns in continuous capnography and pulse oximetry monitoring.

Continuous capnography monitors patient ventilation but can be susceptible to artifact, resulting in...

Completion of Pembrolizumab in Advanced Non-Small Cell Lung Cancer-Real World Outcomes After Two Years of Therapy (COPILOT).

BACKGROUND: Seminal trials with first-line pembrolizumab for metastatic non-small cell lung cancer (...

Role of oxygen reserve index monitoring in patients undergoing robot-assisted radical prostatectomy: a retrospective study.

PURPOSE: Robot-assisted radical prostatectomy (RARP) is a common surgical procedure for the treatmen...

Predictive modeling of deep vein thrombosis risk in hospitalized patients: A Q-learning enhanced feature selection model.

Deep vein thrombosis (DVT) represents a critical health concern due to its potential to lead to pulm...

An adversarial learning approach to generate pressure support ventilation waveforms for asynchrony detection.

BACKGROUND AND OBJECTIVE: Mechanical ventilation is a life-saving treatment for critically-ill patie...

Identification of shared potential diagnostic markers in asthma and depression through bioinformatics analysis and machine learning.

BACKGROUND: There is mounting evidence that asthma might exacerbate depression. We sought to examine...

The application of different machine learning models based on PET/CT images and EGFR in predicting brain metastasis of adenocarcinoma of the lung.

OBJECTIVE: To explore the value of six machine learning models based on PET/CT radiomics combined wi...

Research on the chemical oxygen demand spectral inversion model in water based on IPLS-GAN-SVM hybrid algorithm.

Spectral collinearity and limited spectral datasets are the problems influencing Chemical Oxygen Dem...

Towards quantifying biomarkers for respiratory distress in preterm infants: Machine learning on mid infrared spectroscopy of lipid mixtures.

Neonatal respiratory distress syndrome (nRDS) is a challenging condition to diagnose which can lead ...

Advancing predictive markers in lung adenocarcinoma: A machine learning-based immunotherapy prognostic prediction signature.

The prognosis of lung adenocarcinoma (LUAD) is generally poor. Immunotherapy has emerged as a promis...

Anatomically aware dual-hop learning for pulmonary embolism detection in CT pulmonary angiograms.

Pulmonary Embolisms (PE) represent a leading cause of cardiovascular death. While medical imaging, t...

Diagnostic performance of a deep-learning model using F-FDG PET/CT for evaluating recurrence after radiation therapy in patients with lung cancer.

OBJECTIVE: We developed a deep learning model for distinguishing radiation therapy (RT)-related chan...

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