Pulmonology

Accuracy of artificial intelligence-based segmentation in maxillofacial structures: a systematic review.

OBJECTIVE: The aim of this review was to evaluate the accuracy of artificial intelligence (AI) in the segmentation of teeth, jawbone (maxilla, mandible with temporomandibular joint), and mandibular (inferior alveolar) canal in CBCT and CT scans.

Machine learning models compared with current clinical indices to predict the outcome of high flow nasal cannula therapy in acute hypoxemic respiratory failure.

BACKGROUND: Early identification of patients with acute hypoxemic respiratory failure (AHRF) who are at risk of failing high-flow nasal cannula (HFNC) therapy could facilitate closer monitoring, and timely adjustment/escalation of treatment. We aimed to establish whether machine learning (ML) models could predict HFNC outcome, early in the course of treatment, with greater accuracy than currently ...

Deep reinforcement learning for multi-targets propofol dosing.

The administration of propofol for sedation or general anesthesia presents challenges due to the complex relationship between patient factors and real...

AI integrations with lung cancer screening: Considerations in developing AI in a public health setting.

Lung cancer screening implementation has led to expanded imaging of the chest in older, tobacco-exposed populations. Growing numbers of screening case...

Development and Validation of an Electronic Health Record-Based, Pediatric Acute Respiratory Distress Syndrome Subphenotype Classifier Model.

OBJECTIVE: To determine if hyperinflammatory and hypoinflammatory pediatric acute respiratory distress syndrome (PARDS) subphenotypes defined using se...

An extensive review on infectious disease diagnosis using machine learning techniques and next generation sequencing: State-of-the-art and perspectives.

UNLABELLED: Infectious diseases, including tuberculosis (TB), HIV/AIDS, and emerging pathogens like COVID-19 pose severe global health challenges due ...

Deep learning-driven pulmonary artery and vein segmentation reveals demography-associated vasculature anatomical differences.

Pulmonary artery-vein segmentation is critical for disease diagnosis and surgical planning. Traditional methods rely on Computed Tomography Pulmonary ...

Automatic Segmentation of Bone Graft in Maxillary Sinus via Distance Constrained Network Guided by Prior Anatomical Knowledge.

Maxillary Sinus Lifting is a crucial surgical procedure for addressing insufficient alveolar bone mass andsevere resorption in dental implant therapy....

OMS-CNN: Optimized Multi-Scale CNN for Lung Nodule Detection Based on Faster R-CNN.

The global increase in lung cancer cases, often marked by pulmonary nodules, underscores the critical importance of timely detection to mitigate cance...

Deep mutational learning for the selection of therapeutic antibodies resistant to the evolution of Omicron variants of SARS-CoV-2.

Most antibodies for treating COVID-19 rely on binding the receptor-binding domain (RBD) of SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2...

Artificial intelligence-based deep learning algorithms for ground-glass opacity nodule detection: A review.

Ground-glass opacities (GGOs) are hazy opacities on chest computed tomography (CT) scans that can indicate various lung diseases, including early COVI...

Preoperative Maximum Standardized Uptake Value Emphasized in Explainable Machine Learning Model for Predicting the Risk of Recurrence in Resected Non-Small Cell Lung Cancer.

PURPOSE: To comprehensively analyze the association between preoperative maximum standardized uptake value (SUV) on 18F-fluorodeoxyglucose positron em...

Impact of [F]FDG PET/CT Radiomics and Artificial Intelligence in Clinical Decision Making in Lung Cancer: Its Current Role.

Lung cancer remains one of the most prevalent cancers globally and the leading cause of cancer-related deaths, accounting for nearly one-fifth of all ...

Comparing Artificial Intelligence and Traditional Regression Models in Lung Cancer Risk Prediction Using A Systematic Review and Meta-Analysis.

PURPOSE: Accurately identifying individuals who are at high risk of lung cancer is critical to optimize lung cancer screening with low-dose CT (LDCT)....

Assessments of lung nodules by an artificial intelligence chatbot using longitudinal CT images.

Large language models have shown efficacy across multiple medical tasks. However, their value in the assessment of longitudinal follow-up computed tom...

Logic-based machine learning predicts how escitalopram attenuates cardiomyocyte hypertrophy.

Cardiomyocyte hypertrophy is a key clinical predictor of heart failure. High-throughput and AI-driven screens have the potential to identify drugs and...

Edge Computing System for Automatic Detection of Chronic Respiratory Diseases Using Audio Analysis.

Chronic respiratory diseases affect people worldwide, but conventional diagnostic methods may not be accessible in remote locations far from populatio...

Automated classification of chest X-rays: a deep learning approach with attention mechanisms.

BACKGROUND: Pulmonary diseases such as COVID-19 and pneumonia, are life-threatening conditions, that require prompt and accurate diagnosis for effecti...

Prediction of river dissolved oxygen (DO) based on multi-source data and various machine learning coupling models.

Too low a concentration of dissolved oxygen (DO) in a river can disrupt the ecological balance, while too high a concentration may lead to eutrophicat...

Construction of a risk screening and visualization system for pulmonary nodule in physical examination population based on feature self-recognition machine learning model.

OBJECTIVE: To assess the effectiveness of a feature self-recognition machine learning model in screening for pulmonary nodule risk in a physical exami...