Pulmonology

Identifying sex-specific sub-phenotypes of Alzheimer's disease progression using longitudinal electronic health records.

BACKGROUND: Alzheimer's Disease (AD) is a complex neurodegenerative disorder, with women comprising nearly two-thirds of individuals with AD. However, sex-specific heterogeneity in AD progression remains insufficiently understood. A data-driven approach is needed to characterise such heterogeneity from longitudinal electronic health records (EHRs). METHODS: We developed a deep learning-based frame...

Artificial intelligence-assisted detection and optical differentiation of colorectal lesions in Lynch syndrome surveillance (CADLY2): a multicentre, open-label, randomised controlled superiority trial.

BACKGROUND: Artificial intelligence (AI)-based computer-aided detection (CADe) systems improve adenoma detection in average-risk colorectal cancer screening. Meanwhile, evidence in Lynch syndrome surveillance is sparse and inconsistent. We assessed the effect of CADe on adenoma detection during Lynch syndrome surveillance. Computer-aided optical diagnosis (CADx) performance for optical differentia...

Multiparametric PET/CT Tensor Radiomics for Stability-Aware Machine Learning in Lung Cancer Survival Prediction.

To address the challenges of model instability and limited generalizability in radiomics-based lung cancer prognosis, we developed a robust multiparam...

AGFR-Net: anatomy-guided feature refinement for robust multi-label thoracic disease classification from chest X-rays.

Detecting thoracic diseases from chest X-ray images remains difficult because many abnormalities appear with subtle visual patterns and are often obsc...

Simulator development using natural language: clinician-led innovation through artificial intelligence.

BACKGROUND: Healthcare simulation training faces significant barriers due to the "clinician-developer gap," where educators lack programming expertise...

Chest Radiograph-based Artificial Intelligence for Osteoporosis: Accuracy and Associations With Fracture and Mortality.

OBJECTIVES: To evaluate an AI tool for opportunistic osteoporosis screening using chest radiographs (CXRs), focusing on its diagnostic accuracy agains...

Translating machine learning predictions into meaningful risk estimates to support clinical decisions: a post hoc analysis of chronic obstructive pulmonary disease adverse outcomes using unified auto clinical scores.

OBJECTIVE: The successful integration of Machine Learning (ML) models into clinical practice remains limited, as they often lack the standardized, qua...

Research progress on detection technologies for Mycoplasma pneumoniae.

Mycoplasma pneumoniae (M. pneumoniae) is a primary pathogen responsible for community-acquired pneumonia (CAP), particularly prevalent among children ...

Detection of bronchopulmonary dysplasia in infants and prediction of school-age lung function from tidal breathing data using recurrent neural networks.

Objective.To test whether machine learning (ML) models trained on tidal breathing flow time series can discriminate between individuals with and witho...

Clinical Study on the Prevention of High-Risk Pulmonary Nodule Progression With Yifei Sanjie Pill: Protocol for a Multicenter Randomized Controlled Trial.

BACKGROUND: High-risk subsolid pulmonary nodules, especially mixed ground-glass nodules, can represent precancerous or early-stage lung adenocarcinoma...

IgA-Enriched Phenotype Predicts Liver-Related Events in MASLD.

BACKGROUND & AIMS: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a complex, multifactorial condition. To achieve truly personali...

Pneumoconiosis screening and classification using deep learning models.

OBJECTIVES: To develop and evaluate different deep learning (DL) models for clinically relevant screening and classification of pneumoconiosis based o...

Cellular hallmarks and aging clock of the human lung parenchyma.

Aging affects lung function, predisposing older adults to respiratory diseases; however, the cellular and molecular mechanisms of lung aging are not f...

Data-efficient machine learning approach for predicting asthma attack risk.

Asthma caused around 436,000 deaths globally in 2021. Predicting asthma attacks can save lives, reduce costs, and strengthen healthcare systems. Howev...

An explainable multimodal 2.5D deep learning-radiomics model for predicting extranodal extension in lung adenocarcinoma using preoperative CT: a multicenter retrospective cohort study.

OBJECTIVE: This study aimed to develop the Radiomics-Assembled ENE system (RAIEm), a multimodal preoperative computed tomography (CT) radiomics model,...

Scientific evidence of commercial artificial intelligence products for pulmonary nodule assessment on CT scans: a systematic review.

OBJECTIVES: This systematic review evaluates the available evidence on the efficacy of commercially available AI-software applications for lung nodule...

Association of inflammation-related composite indices with lung cancer risk in US adults and all-cause mortality among lung cancer patients: evidence from the 1999-2018 NHANES.

BACKGROUND: Chronic inflammation is implicated in lung cancer pathogenesis. Inflammation-related haematologic indices are accessible biomarkers, but t...

Optimizing stroke mortality prediction: a comparative study of sampling, cost-sensitive learning, and threshold adjustment.

BACKGROUND: Stroke is a leading cause of mortality and disability worldwide, creating a critical need for accurate prediction tools to support early c...

Machine learning-optimized bioinspired N-doped carbon-wrapped trimetallic oxides for an efficient oxygen evolution reaction.

Despite the significant advancement in the development of a wide range of nitrogen-doped multi-metal oxide-based electrocatalysts for the oxygen evolu...

High-entropy Na layered oxide facilitating reversible oxygen capacity.

Achieving nonhysteretic and reversible (nHR) oxygen redox in sodium-ion batteries (SIBs) is critical for enhancing their energy density and practical ...