Pulmonology

Automatic mandibular third molar and mandibular canal relationship determination based on deep learning models for preoperative risk reduction.

OBJECTIVES: This study explores the application of deep learning models for classifying the spatial relationship between mandibular third molars and the mandibular canal using cone-beam computed tomography images. Accurate classification of this relationship is essential for preoperative planning, as improper assessment can lead to complications such as inferior alveolar nerve injury during extrac...

Smart nanomedicines powered by artificial intelligence: a breakthrough in lung cancer diagnosis and treatment.

Lung cancer remains one of the leading causes of cancer-related mortality worldwide, primarily due to challenges in early detection, suboptimal therapeutic efficacy, and severe adverse effects associated with conventional treatments. The convergence of nanotechnology and artificial intelligence (AI) offers transformative potential in precision oncology, enabling innovative solutions for lung cance...

Enhanced diagnosis of multi-drug-resistant microbes using group association modeling and machine learning.

New solutions are needed to detect genotype-phenotype associations involved in microbial drug resistance. Herein, we describe a Group Association Mode...

Post-Anesthesia Care Unit (PACU) readiness predictions using machine learning: a comparative study of algorithms.

INTRODUCTION: Accurate and timely discharge from the Post-Anesthesia Care Unit (PACU) is essential to prevent postoperative complications and optimize...

Optimizing imaging modalities for sarcoma subtypes in radiation therapy: State of the art.

The choice of imaging modalities is essential in sarcoma management, as different techniques provide complementary information depending on tumor subt...

Early-stage lung cancer detection via thin-section low-dose CT reconstruction combined with AI in non-high risk populations: a large-scale real-world retrospective cohort study.

BACKGROUND: Current lung cancer screening guidelines recommend annual low-dose computed tomography (LDCT) for high-risk individuals. However, the effe...

Enhanced tuberculosis detection using Vision Transformers and explainable AI with a Grad-CAM approach on chest X-rays.

Tuberculosis (TB), caused by Mycobacterium tuberculosis, remains a leading global health challenge, especially in low-resource settings. Accurate diag...

Machine Learning-Based VO Estimation Using a Wearable Multiwavelength Photoplethysmography Device.

The rate of oxygen consumption, which is measured as the volume of oxygen consumed per mass per minute (VO) mL/kg/min, is a critical metric for evalua...

Precision Oncology in Non-small Cell Lung Cancer: A Comparative Study of Contextualized ChatGPT Models.

OBJECTIVES: The growing adoption of Large Language Models (LLMs) in medicine has raised important questions about their potential utility for clinical...

A tumor-infiltrating B lymphocytes -related index based on machine-learning predicts prognosis and immunotherapy response in lung adenocarcinoma.

INTRODUCTION: Tumor-infiltrating B lymphocytes (TILBs) play a pivotal role in shaping the immune microenvironment of tumors (TIME) and in the progress...

Deciphering the molecular fingerprint of haemoglobin in lung cancer: A new strategy for early diagnosis using two-trace two-dimensional correlation near infrared spectroscopy (2T2D-NIRS) and machine learning techniques.

Lung cancer remains one of the deadliest malignancies worldwide, highlighting the need for highly sensitive and minimally invasive early diagnostic me...

AI Efficiency in Dentistry: Comparing Artificial Intelligence Systems with Human Practitioners in Assessing Several Periodontal Parameters.

Artificial intelligence (AI) is increasingly used in healthcare, including dental and periodontal diagnostics, due to its ability to analyze complex d...

Explainable SHAP-XGBoost models for pressure injuries among patients requiring with mechanical ventilation in intensive care unit.

pressure injuries are significant concern for ICU patients on mechanical ventilation. Early prediction is crucial for enhancing patient outcomes and r...

Vowel segmentation impact on machine learning classification for chronic obstructive pulmonary disease.

Vowel-based voice analysis is gaining attention as a potential non-invasive tool for COPD classification, offering insights into phonatory function. T...

Machine learning-based model for predicting all-cause mortality in severe pneumonia.

BACKGROUND: Severe pneumonia has a poor prognosis and high mortality. Current severity scores such as Acute Physiology and Chronic Health Evaluation (...

Ontology-driven identification of inconsistencies in clinical data: A case study in lung cancer phenotyping.

OBJECTIVE: To illustrate the use of an ontology in evaluating data quality in the medical field, focusing on phenotyping lung cancers.

Rapid, non-invasive breath analysis for enhancing detection of silicosis using mass spectrometry and interpretable machine learning.

Occupational lung diseases, such as silicosis, are a significant global health concern, especially with increasing exposure to engineered stone dust. ...

Development and validation of a machine learning-based nomogram for predicting prognosis in lung cancer patients with malignant pleural effusion.

Malignant pleural effusion (MPE) is a common complication in patients with advanced lung cancer, significantly impacting their survival rates and qual...

Machine learning allows robust classification of lung neoplasm tissue using an electronic biopsy through minimally-invasive electrical impedance spectroscopy.

New bronchoscopy techniques like radial probe endobronchial ultrasound have been developed for real-time sampling characterization, but their use is s...

Artificial Intelligence-Guided Bronchoscopy is Superior to Human Expert Instruction for the Performance of Critical-Care Physicians: A Randomized Controlled Trial.

OBJECTIVES: Bronchoscopy in the mechanically ventilated patient is an important skill for critical-care physicians. However, training opportunity is h...