Reliability of Rapid On-Site Evaluation Achieved by Remote Sharing Systems (E-ROSE) and AI Algorithms (AI-ROSE) Compared With the Gold Standard in the Diagnosis of Lung Cancer.

Background And Objective: In recent decades, artificial intelligence has seen significant development in various fields of medicine, including interventional pulmonology. The study aims to evaluate the diagnostic performance of innovative approaches to detect lung cancer on biopsy sample images (Rapid On-Site Evaluation, ROSE) compared to the diagnostic gold standard.

Methods: We conducted a multicentric study, comparing remote anatomopathological evaluation (E-ROSE) and machine learning algorithms (AI-ROSE) reliability in diagnosing lung cancer, evaluating 277 biopsy sample images, 25 of which were doubtful; to compare them with the definitive histological examination performed by the pathologist.

Exploring phenotypes to improve long-term mortality risk stratification in obstructive sleep apnea through a machine learning approach: an observational cohort study.

Background: Obstructive sleep apnea (OSA) is a heterogeneous sleep disorder for which the identification of phenotypes might help for risk stratification for long-term mortality. Thus, the aim of the study was to identify distinct phenotypes of OSA and to study the association of phenotypes features with long-term mortality by using machine learning.

Methods: This retrospective study included patients diagnosed with OSA who completed a 15-year follow-up and were adherent to continuous positive airway pressure (CPAP) therapy.

Electronic Nose Analysis of Exhaled Breath Volatile Organic Compound Profiles during Normoxia, Hypoxia, and Hyperoxia.

This study investigates volatile organic compound (VOC) profiles in the exhaled breath of normal subjects under different oxygenation conditions-normoxia (FiO2 21%), hypoxia (FiO2 11%), and hyperoxia (FiO2 35%)-using an electronic nose (e-nose). We aim to identify significant differences in VOC profiles among the three conditions utilizing principal component analysis (PCA) and canonical discriminant analysis (CDA). Our results indicate distinct VOC patterns corresponding to each oxygenation state, demonstrating the potential of e-nose technology in detecting physiological changes in breath composition (cross-validated accuracy values: FiO2 21% vs.

Mortality in obstructive sleep apnea syndrome (OSAS) and overlap syndrome (OS): The role of nocturnal hypoxemia and CPAP compliance.

Background: Obstructive sleep apnea syndrome (OSAS) and chronic obstructive pulmonary disease (COPD) are two chronic diseases that afflict many individuals worldwide with negative effects on health that may overlap in Overlap Syndrome (OS). The aim of our study was to investigate the differences in mortality between OSAS alone and OS and the risk factors involved.

Methods: The study was conducted on patients with OSAS or OS diagnosis that completed 15-year follow-up between 2005 and 2023.