Pulmonology

An interpretable radiomics-machine learning model for early risk stratification of invasive fungal infections in community-acquired pneumonia: a dual-center study.

To develop and validate an interpretable HRCT radiomics-based machine learning model for early risk stratification and identification of invasive fungal infection (IFI) in patients with community-acquired pneumonia (CAP). A total of 570 CAP patients who underwent HRCT from July 2022 to August 2024 in Center 1 and Center 2 were recruited. A VB-net pneumonia automatic segmentation algorithm was empl...

Decoding Spectral Perturbations from a SERS Artificial Nose in Serum for Early Lung Cancer Detection.

Serum biomarkers for early cancer detection often suffer from limited sensitivity and specificity due to the biochemical complexity of blood. Here, we report a surface-enhanced Raman scattering (SERS) artificial nose that integrates a set of chemically distinct molecular receptors to generate multidimensional spectral responses to human serum. These receptors exhibit large Raman cross sections and...

Predicting major adverse cardiovascular and cerebrovascular events in chronic heart failure: a machine learning study.

BACKGROUND: Heart failure (HF) is a clinical syndrome characterized by impaired cardiac diastolic and systolic function due to structural or functiona...

Nationwide 30-Day Surgical Outcomes in Korea: An Interim Analysis of the Korean Quality Improvement Platform in Surgery.

BACKGROUND: National surgical registries such as the American College of Surgeons NSQIP and Japan's National Clinical Database have shown that structu...

A weakly supervised deep learning-based recurrence prediction and risk stratification of lung adenocarcinoma from pathology whole-slide images.

BACKGROUND: Accurate prediction of postoperative recurrence in lung adenocarcinoma (LUAD) is essential for guiding clinical decision-making and improv...

Zero-Shot Self-Supervised Learning of Single Breath-Hold Magnetic Resonance Cholangiopancreatography (MRCP) Reconstruction.

PURPOSE: To investigate the feasibility of zero-shot self-supervised learning reconstruction for reducing breath-hold times in magnetic resonance chol...

Exploring the use of AI-generated counterfactual chest X-rays to enhance diagnostic learning in medical education.

Accurate interpretation of chest X-rays is a critical clinical skill, yet radiology training in medical education remains limited and often fails to p...

Integrative transcriptomic and bioinformatic analyses predict candidate EMT-related genes in sepsis-associated acute lung injury.

Sepsis-associated acute lung injury (sepsis-ALI) is a complex pathological condition; its underlying mechanisms remain mostly obscure. Thus, in this s...

Plasma metabolomics enhances risk prediction for lung cancer: a population-based validation study.

Metabolic biomarkers offer promising opportunities for lung cancer risk assessment and early detection. This study aimed to identify metabolites with ...

Ophiopogon japonicus alleviates pulmonary fibrosis through modulation of HIF1A/TUBB3 axis-associated M2 macrophage polarization and arginine metabolic alterations.

ETHNOPHARMACOLOGICAL RELEVANCE: Ophiopogon japonicus (Thunb.) Ker Gawl. is a traditional Chinese medicinal herb commonly used to "nourish yin and mois...

RRBP1 promotes osimertinib resistance by activating fatty acid oxidation-dependent metabolic and immune reprogramming in EGFR-Mutant lung adenocarcinoma.

Osimertinib is a third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor that improves clinical outcomes in patients with E...

Multifunctional Bioengineered Scaffolds and Adjunct Therapeutic Strategies for Diabetic Foot Ulcers.

Diabetic foot ulcers (DFUs) are chronic, non-healing wounds that affect up to 34% of diabetic patients. DFUs are complicated by infection in nearly 60...

Novel method for risk stratification of radiation-induced breast fibrosis: subgroup hypothesis verified by machine learning.

Breast fibrosis (BF) after radiotherapy remains one of the most dreaded late toxicities in breast cancer care, yet multiple additive predictors strugg...

A multidimensional risk prediction framework for malignant intestinal obstruction based on machine learning: computational model development and clinical validation.

Malignant intestinal obstruction (MIO) is a severe complication of advanced cancer. Traditional static assessment models struggle to capture its dynam...

The current state of demographic subgroup reporting for commercially available AI for radiology: a scoping review.

OBJECTIVE: Though subgroup performance reporting helps ensure the safety of artificial intelligence (AI) products, the extent of this reporting remain...

Discriminating HFrEF vs HFpEF from chest radiographs: Mitigating demographic performance gaps via augmentation and multimodal fusion.

Deep learning models applied to chest radiography have shown promise for cardiac phenotyping, yet concerns remain regarding demographic performance di...

Machine learning-based prediction of bronchiolitis in children under two years: a multicenter study within a single metropolitan area.

BACKGROUND: Bronchiolitis is a leading cause of hospitalization in infants, yet early and accurate risk prediction remains a clinical challenge. Tradi...

A methodological framework for integrating generalisable deep learning and radiomics fusion model for early lung cancer detection across multi-centre imaging datasets.

Early detection of lung cancer remains one of the most effective strategies for improving survival; however, diagnostic performance is limited by vari...

Large language models for optimizing clinical trial recruitment in ICUs: application to ventilator-induced diaphragm dysfunction.

BACKGROUND: Ventilator-induced diaphragm dysfunction (VIDD) is a frequent and under-recognized consequence of prolonged mechanical ventilation in inte...

Computational tools for personalizing treatment of acute respiratory failure, from machine learning to digital twins: a narrative review.

Patient-specific computational tools hold great promise for the development of more personalized treatment strategies for acute respiratory failure. S...