Pulmonology

Interpretable deep learning model of circulating genomics for quantitative survival prediction in advanced non-small cell lung cancer.

PURPOSE: Accurate quantitative survival prediction in advanced non-small cell lung cancer (NSCLC) remains an unmet clinical need. While liquid biopsy is widely used, single circulating tumor DNA (ctDNA) shows limited predictive power. We developed an interpretable deep-learning model to quantitatively predict outcomes. METHODS/PATIENTS: We integrated data from 1373 advanced NSCLC patients profiled...

Few-Shot Pulmonary Vessel Segmentation based on Tubular-Aware Prompt-Tuning.

Segmentation of the pulmonary vessel from computed tomography (CT) images plays a crucial role in the diagnosis and treatment of various lung diseases. Although deep learning-based approaches have shown remarkable progress in recent years, their performance is often hindered by the lack of high-quality annotated datasets, in which the complex anatomy and morphology of pulmonary vessels make manual...

A cascaded CNN-LSTM framework for quantifying respiratory motion from surface electromyographic signals.

OBJECTIVE: Surface electromyographic (sEMG) signals of the diaphragm provide a valuable physiological signal for real-time respiratory monitoring, par...

Intraoperative biopsy imaging of lung cancer risk.

BACKGROUND: Before surgical resection of lung tumor, intraoperative biopsy is needed for cancer diagnosis, while current techniques that guide biopsy ...

CT-based radiomics for predicting PD-L1 expression status in non-small cell lung cancer using a hybrid machine learning model.

PURPOSE: Programmed cell death ligand-1 (PD-L1) is a key prognostic and predictive biomarker for immunotherapy in non-small cell lung cancer (NSCLC). ...

The effect of artificial intelligence-assisted pulmonary rehabilitation on exercise capacity: A systematic review and meta-analysis.

INTRODUCTION: Artificial intelligence (AI) technologies are increasingly being integrated into pulmonary rehabilitation (PR) to improve individualizat...

[Radiological diagnosis of interstitial lung disease].

Diffuse interstitial lung diseases (ILDs) represent a complex and heterogeneous group of pulmonary disorders, requiring a structured, rigorous, and in...

Factors influencing long-term outcomes in lung transplantation: effective monitoring and comprehensive intervention.

PURPOSE OF REVIEW: Achieving long-term survival after lung transplantation remains a major challenge. Outcome determinants have expanded beyond pathop...

Synergistic impact of Gram-negative bacterial co-infection on invasive pulmonary aspergillosis in non-neutropenic patients: a machine learning-based risk stratification approach.

BACKGROUND: Invasive pulmonary aspergillosis (IPA) is increasingly recognized in non-neutropenic patients, where coexisting bacterial infections, part...

Auto-LSN: fully automated liver surface nodularity quantification in CT based on deep learning for the evaluation of advanced chronic liver disease.

OBJECTIVES: Liver surface nodularity (LSN) is a recognized non-invasive biomarker of cirrhosis. This study introduces auto-LSN, an artificial intellig...

Dual elastography ultrasound for classifying metabolic dysfunction-associated steatotic liver disease: a cross-sectional study within a prospective cohort.

BACKGROUND: The binary diagnostic approach does not reflect the entire spectrum of metabolic dysfunction associated steatotic liver disease (MASLD. We...

Evaluation of deliverable dose-mimicking automated volumetric arc radiation therapy planning for stage III non-small cell lung cancer patients: comparison with a commercial DVH-predicted automated planning system.

This study aimed to evaluate the clinical validity of a dose-mimicking automated planning for volumetric-modulated arc therapy (VMAT) in patients with...

Improving the management and outcomes of preschool wheeze: protocol of a prospective multicentre cohort study.

INTRODUCTION: Preschool wheeze and asthma are associated with substantial morbidity and impaired future lung function. Yet, wheeze is unreliably repor...

Semi-supervised multi-class pneumonia classification using a CNN-cascade forest framework.

Medical imaging diagnosis of pneumonia must be accurate at distinguishing between the various disease subtypes but must be resistant to sparse annotat...

Evaluation of multimodal large language models for pneumothorax assessment in real-world clinical scenarios.

Explainable machine learning-based 28-day mortality prediction model for elderly patients with acute kidney injury.

Elderly patients with acute kidney injury (AKI) face a significantly increased mortality risk. Recent advances in machine learning technology have mad...

Large-scale proteomic profiling identifies distinct inflammatory phenotypes in acute respiratory distress syndrome: a multicentre, prospective cohort study.

BACKGROUND: Host responses during acute respiratory distress syndrome are highly heterogeneous, contributing to inconsistent therapeutic outcomes. Pro...

A hybrid feature extraction framework combining PCA and mutual information for gene expression based lung cancer classification.

Lung cancer remains a leading cause of cancer-related mortality worldwide, with early and accurate diagnosis posing a critical challenge for improving...

Evaluation of oxygenation indices incorporating SpO₂ and PEEP for assessing ARDS severity: Evidence from the MIMIC-IV and eICU collaborative research database v2.0 databases.

BACKGROUND: The in-hospital mortality of acute respiratory distress syndrome can reach 35-45%, with patients requiring a more convenient and accurate ...

The Role of Complex Digital Interventions to Improve Pneumonia Care.

Pneumonia clinical decision support (CDS) has evolved from simple paper-based guidelines to complex electronic systems powered by artificial intellige...