Pulmonology

Robust multimodal mental workload classification: A cross-physiological condition machine learning approach.

BACKGROUND AND OBJECTIVE: Aircraft pilots can be faced with a high mental workload (MW) combined with moderate hypoxia and sleep restriction. We aimed to assess the cross-validation of a machine learning-based MW predictive model under hypoxia and/or sleep restriction. Secondly, we developed a robust predictive model using multimodal physiological parameters to improve the validity across differen...

Image Quality Assessment of Artificial Intelligence Iterative Reconstruction for Low-dose Bronchial Artery CTA in Preoperative Hemoptysis Patients.

OBJECTIVES: To investigate the feasibility and image quality of artificial intelligence iterative reconstruction (AIIR) for computed tomography angiography (CTA) of the bronchial artery (BA) with a reduced radiation dose and contrast agent dosage. MATERIALS AND METHODS: A total of 110 hemoptysis patients were prospectively enrolled for bronchial artery CTA (BA-CTA) and were randomly divided into 2...

Artificial intelligence in the diagnosis and prognosis of pediatric bacterial pneumonia: current advances and challenges.

PURPOSE OF REVIEW: The clinical presentation of pediatric bacterial pneumonia often overlaps with that of other respiratory conditions, posing conside...

Plasma proteomics biomarkers for predicting anti-tuberculosis drug-induced liver injury: A comprehensive assessment.

AIMS: This study aims to identify predictive plasma protein biomarkers for anti-tuberculosis drug-induced liver injury (ATB-DILI) and develop machine ...

Prediction hospital mortality for critical illness lung cancer patients with pneumonia.

Machine Learning Predictive Models for Prognosis in Patients undergoing Endovascular Abdominal Aortic Aneurysm Repair.

BACKGROUND: An increasing number of patients with abdominal aortic aneurysms (AAAs) are opting for endovascular aneurysm repair (EVAR), and predicting...

Next-generation lung-cancer-on-a-chip: toward personalized therapy, AI, and CRISPR-driven models.

Lung-cancer-on-a-chip (LCOC) technologies have advanced rapidly, yet most models evaluate mechanical strain, patient-derived tumors, multi-organ inter...

Old Criteria, New Intelligence: The Evolution of ECG in Pulmonary Hypertension Diagnosis.

BACKGROUND: Pulmonary hypertension (PH) carries a significant mortality risk, highlighting the need for improved early detection strategies. This revi...

Harnessing artificial intelligence for the assessment of liver fibrosis and steatosis via multiparametric ultrasound.

Artificial intelligence (AI) is revolutionizing medical imaging, particularly in chronic liver diseases assessment. AI technologies, including machine...

Single-inspiratory quantitative CT nomogram for enhanced PRISm and COPD differentiation: a cross-sectional study with interpretable diagnostic boundaries.

BACKGROUND: Differentiating preserved ratio impaired spirometry (PRISm) from chronic obstructive pulmonary disease (COPD) is challenging. Traditional ...

Development and deployment of an interpretable stacking ensemble model for predicting in-hospital mortality in ICU patients with chronic kidney disease and sepsis.

OBJECTIVE: To develop an interpretable stacking ensemble model for predicting in-hospital mortality in intensive care unit (ICU) patients with CKD and...

Rapid, non-invasive diagnosis of tuberculosis using desorption separation ionization mass spectrometry (DSI-MS) and urinary carbonyl metabolite fingerprints.

Tuberculosis (TB) remains a leading global infectious disease that demands rapid, non-invasive diagnostic solutions. Here, we present a rapid urine-ba...

Ridge-structured PANI with interfacial stress-concentrating: reversible protonation driven high sensitivity over wide pressures.

In research on pressure-sensitive electronic skin (e-skin) for motion and physiological signal monitoring, balancing sensitivity with a broad pressure...

Prediction of the Preclinical Stage of Coal Workers' Pneumoconiosis on Nonimaging Data Integrating Prior Knowledge and Machine Learning.

OBJECTIVE: This study aims to establish machine learning models using nonimaging data from health examinations of coal workers, which can screen the p...

Communicable diseases platform (CDP): Real-Time clinical analytics for infections.

OBJECTIVE: Communicable diseases, especially seasonal respiratory illnesses, contribute significantly to paediatric hospital presentations and admissi...

CTSB-positive tumor-associated macrophages shape prognosis and therapeutic response in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma (LUAD) remains a major clinical challenge in assessment of clinical outcomes and therapeutic response. Although tumor-...

Machine learning-guided identification of metastasis-associated miRNAs and their integration into a PFI-based cox risk score and nomogram.

BACKGROUND: Metastasis drives mortality in breast invasive carcinoma. We sought miRNA biomarkers that (i) discriminate metastatic potential, (ii) stra...

Automated diagnosis of usual interstitial pneumonia on chest CT via the mean curvature of isophotes.

PURPOSE: To test whether the mean curvature of isophotes (MCI), a geometric image transformation, can be used to improve automatic detection on chest ...

Diagnostic Accuracy of Artificial Intelligence in Detecting Pleural Effusion on Ultrasound Imaging: A Systematic Review and Meta-Analysis.

Artificial intelligence (AI) interpretation of ultrasound (US) images is promising, yet its accuracy in diagnosing pleural effusions remains unclear. ...

Interpretable machine learning-based prediction of liver metastasis risk in elderly patients with small cell lung Cancer: A study based on the SEER database and external validation in a Chinese cohort.

PURPOSE: Small cell lung cancer (SCLC) is a highly aggressive malignancy with a high incidence of liver metastases, particularly among elderly patient...