Pulmonology

[A two-stage model for predicting postoperative pulmonary infection in esophageal cancer patients].

Postoperative pulmonary infection (PPI) after esophageal cancer surgery occurs frequently and severely impairs patients' prognosis. Most existing prediction models cannot realize staged classification of risk factors, which limits targeted risk identification and intervention. Based on machine learning algorithms, this study integrates preoperative baseline characteristics and perioperative indica...

[Research on electrical impedance tomography for pediatric pneumonia based on an improved radial basis function neural network].

In the diagnosis of pediatric pneumonia, the weak conductivity contrast of lung tissues leads to limited image resolution in electrical impedance tomography (EIT). To improve the quality of image reconstruction, this study proposes a radial basis function neural network optimized by the crested porcupine optimizer and adaptive moment estimation (CPOA-RBFNN). By integrating the global search capabi...

Delta radiomics for predicting early radiation-induced lung injury after thoracic radiotherapy: a retrospective paired-CT study.

PURPOSE: Early radiation-induced lung injury remains a clinically relevant complication after thoracic radiotherapy. We compared pretreatment, posttre...

Decoding silica nanoparticle toxicity: integrating machine learning, feature importance, rule extraction, and adsorption-uptake processes.

The complexity of nanoparticle toxicity necessitates applying machine learning to large toxicological datasets to identify predictive features and tox...

Benzo(a)pyrene aggregating lung inflammation via inducing IL1R1 expression in asthma: Insights from network toxicology, single-cell transcriptomics, and Mendelian randomization.

OBJECTIVE: Exposure to benzo(a)pyrene (BaP) negatively affects lung inflammation in patients with asthma. However, there is a lack of systematic resea...

Generative Artificial Intelligence and Prompt Engineering in Asthma-Related Settings.

Generative artificial intelligence (AI) is rapidly emerging as a valuable tool in medicine, with increasing use in asthma and allergy practice. Large ...

Pioneering detonation pressure in energetic materials.

Accurate prediction of detonation performance is a critical challenge in the design and screening of energetic materials due to the complex and nonlin...

Q-Bone system: an intelligent quantitative system for alveolar bone loss to assist the diagnosis of periodontitis - model development and validation.

OBJECTIVES: To develop and validate the Q-Bone system, an intelligent quantitative system for anatomically driven assessment of alveolar bone loss and...

Impact of machine learning on spare parts availability for critical medical devices: a supervised machine learning perspective.

PURPOSE: This paper seeks to improve the reliability and quality of operation of the critical medical equipment methods through the combination of fai...

Early prognostication for ICU patients with combined respiratory and circulatory failure: an interpretable machine learning approach.

Accurate prognostication in the intensive care unit (ICU) is essential for delivering personalized and ethically sound care, yet it remains a challeng...

A conceptual agentic AI architecture for MASLD-associated significant fibrosis in primary care.

Metabolic dysfunction-associated steatotic liver disease (MASLD) is highly prevalent yet often underdiagnosed or undertreated in primary care due to a...

Budget impact of implementing AI-enabled chest X-ray based incidental pulmonary nodule detection for early lung cancer diagnosis: models from Colombia, Costa Rica, Mexico, Thailand and Vietnam.

OBJECTIVE: To assess the budget impact of incidental pulmonary nodule (IPN) detection using an artificial intelligence-software for chest X-ray (CXR) ...

Bridging the rural logistics penalty: quantifying structural costs and designing policy mechanisms for home TCM delivery to older adults with chronic respiratory diseases.

BACKGROUND: Equitable access to prescribed therapies remains challenging for older adults with chronic respiratory diseases (CRDs) in rural China. Liq...

From clusters to clinical rules: unsupervised machine learning identifies four newborn hearing phenotypes with bedside risk stratification.

OBJECTIVE: To identify risk patterns among high-risk newborns associated with hearing screening failure using unsupervised machine learning. STUDY DES...

Development and validation of machine-learning diagnostic models for identifying frailty in older adults with tuberculosis: a multicentre observational study protocol.

BACKGROUND: The Frailty in Tuberculosis (FIT) study aims to assess frailty in older adults with tuberculosis (TB) using machine learning (ML) to devel...

Benchmarking of AI and Radiologists for Indeterminate Lung Nodule Malignancy Risk Estimation on Screening CT: The LUNA25 Challenge.

Purpose To compare the performance of an artificial intelligence (AI) system with that of radiologists for estimating malignancy risk of indeterminate...

Impact on Cost and Expert Time of Data-Efficient Deep Learning for Medical Image Segmentation.

Purpose To develop and systematically evaluate an iterative training approach, termed the expert-guided annotation loop, for efficient reference stand...

Impact of Exposure Parameters on Deep Learning Models in Chest Radiography and Implications for Deployment.

Purpose To investigate whether deep learning models trained on chest radiographs (CXRs) rely on radiographic exposure parameters as shortcut features ...

High-fidelity super-resolution CT radiomics for non-invasive EGFR mutation prediction in lung adenocarcinoma: a multi-center pooled analysis.

OBJECTIVE: To develop and validate a high-fidelity super-resolution (SR)-enhanced radiomics framework using a Residual Channel Attention Network (RCAN...

Right Ventricular Metrics as End Points in Clinical Trials: A Review.

IMPORTANCE: Clinical trials in cardiovascular medicine aim to deliver high-quality evidence with greater efficiency, including smaller sample sizes an...