Pulmonology

Development of a machine learning model related to explore the association between heavy metal exposure and alveolar bone loss among US adults utilizing SHAP: a study based on NHANES 2015-2018.

BACKGROUND: Alveolar bone loss (ABL) is common in modern society. Heavy metal exposure is usually considered to be a risk factor for ABL. Some studies revealed a positive trend found between urinary heavy metals and periodontitis using multiple logistic regression and Bayesian kernel machine regression. Overfitting using kernel function, long calculation period, the definition of prior distributio...

Modeling the latent impacts of extreme floods on indoor mold spores in residential buildings: Application of machine learning algorithms.

Floods can severely impact the economy, environment and society. These impacts can be direct and indirect. Past research has focused more on the former impacts. Of the indirect impacts, those on mold growth in indoor environments that affect human respiratory health (e.g. asthma) have received limited attention. Models can be used to predict these impacts and support development of mitigation and ...

[Artificial intelligence in paediatric pneumology - opportunities and unanswered questions].

Artificial intelligence (AI) is already being used in most medical disciplines, including paediatric pneumology. This review describes current develop...

Development and Evaluation of a Deep Learning-Based Pulmonary Hypertension Screening Algorithm Using a Digital Stethoscope.

BACKGROUND: Despite the poor outcomes related to the presence of pulmonary hypertension, it often goes undiagnosed in part because of low suspicion an...

Prediction of the Risk of Adverse Clinical Outcomes with Machine Learning Techniques in Patients with Noncommunicable Diseases.

Decision-making in chronic diseases guided by clinical decision support systems that use models including multiple variables based on artificial intel...

Unlocking the link: predicting cardiovascular disease risk with a focus on airflow obstruction using machine learning.

BACKGROUND: Respiratory diseases and Cardiovascular Diseases (CVD) often coexist, with airflow obstruction (AO) severity closely linked to CVD inciden...

Leveraging deep-learning and unconventional data for real-time surveillance, forecasting, and early warning of respiratory pathogens outbreak.

BACKGROUND: Controlling re-emerging outbreaks such as COVID-19 is a critical concern to global health. Disease forecasting solutions are extremely ben...

Automated Euler number of the alveolar capillary network based on deep learning segmentation with verification by stereological methods.

Diseases like bronchopulmonary dysplasia (BPD) affect the development of the pulmonary vasculature, including the alveolar capillary network (ACN). Si...

Protocol for functional screening of CFTR-targeted genetic therapies in patient-derived organoids using DETECTOR deep-learning-based analysis.

Here, we present a protocol for the rapid functional screening of gene editing and addition strategies in patient-derived organoids using the deep-lea...

Exploring the assessment of post-cardiac valve surgery pulmonary complication risks through the integration of wearable continuous physiological and clinical data.

BACKGROUND: Postoperative pulmonary complications (PPCs) following cardiac valvular surgery are characterized by high morbidity, mortality, and econom...

Automated recognition and segmentation of lung cancer cytological images based on deep learning.

Compared with histological examination of lung cancer, cytology is less invasive and provides better preservation of complete morphology and detail. H...

A Machine Learning Model Score Predicts Advanced Fibrosis and Cirrhosis in Indeterminate FIB-4 Patients.

Tumor Cellularity Assessment Using Artificial Intelligence Trained on Immunohistochemistry-Restained Slides Improves Selection of Lung Adenocarcinoma Samples for Molecular Testing.

Tumor cellularity (TC) in lung adenocarcinoma slides submitted for molecular testing is important in identifying actionable mutations, but lack of bes...

Deep learning and machine learning in CT-based COPD diagnosis: Systematic review and meta-analysis.

BACKGROUND: With advancements in medical technology and science, chronic obstructive pulmonary disease (COPD), one of the world's three major chronic ...

The use of artificial intelligence to aid the diagnosis of lung cancer - A retrospective-cohort study.

INTRODUCTION: AI software in the form of deep learning-based automatic detection (DLAD) algorithms for chest X-ray (CXR) interpretation have shown suc...

Integrating machine learning models for optimizing ecosystem health assessments through prediction of nitrate-N concentrations in the lower stretch of Ganga River, India.

Nitrate, a highly reactive form of inorganic nitrogen, is commonly found in aquatic environments. Understanding the dynamics of nitrate-N concentratio...

Deep Learning-Assisted Computer-Aided Diagnosis System for Early Detection of Lung Cancer.

PURPOSE: The largest cause of cancer-related fatalities worldwide is lung cancer. The dimensions and positioning of the primary tumor, the presence of...

Detection of Breath Nitric Oxide at Ppb Level Based on Multiperiodic Spectral Reconstruction Neural Network.

As breath nitric oxide (NO) is a biomarker of respiratory inflammation, reliable techniques for the online detection of ppb-level NO in exhaled breath...

Self-critical strategy adjustment based artificial intelligence method in generating diagnostic reports of respiratory diseases.

. Humanity faces many health challenges, among which respiratory diseases are one of the leading causes of human death. Existing AI-driven pre-diagnos...

The TB27 Transcriptomic Model for Predicting Culture Conversion.

RATIONALE: Treatment monitoring of tuberculosis patients is complicated by a slow growth rate of . Recently, host RNA signatures have been used to mon...