AIMC Topic: Bronchoscopy

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The Impact of Deep Learning on Determining the Necessity of Bronchoscopy in Pediatric Foreign Body Aspiration: Can Negative Bronchoscopy Rates Be Reduced?

Journal of pediatric surgery Oct 19, 2024
INTRODUCTION: This study aimed to evaluate the role of deep learning methods in diagnosing foreign body aspiration (FBA) to reduce the frequency of negative bronchoscopy and minimize potential complications.
Bronchoscopy Foreign Bodies Respiratory Aspiration Sensitivity and Specificity Male Infant Deep Learning Child, Preschool Child Humans Retrospective Studies Female
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Mastery Learning Guided by Artificial Intelligence Is Superior to Directed Self-Regulated Learning in Flexible Bronchoscopy Training: An RCT.

Respiration; international review of thoracic diseases Oct 17, 2024
INTRODUCTION: Simulation-based training has proven effective for learning flexible bronchoscopy. However, no studies have tested the efficacy of training toward established proficiency criteria, i.e., mastery learning (ML). We wish to test the effect...
Humans Artificial Intelligence Learning Clinical Competence Bronchoscopy Simulation Training Female Adult Male
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Endobronchial Ultrasound-Based Support Vector Machine Model for Differentiating between Benign and Malignant Mediastinal and Hilar Lymph Nodes.

Respiration; international review of thoracic diseases Jul 22, 2024
INTRODUCTION: The aim of the study was to establish an ultrasonographic radiomics machine learning model based on endobronchial ultrasound (EBUS) to assist in diagnosing benign and malignant mediastinal and hilar lymph nodes (LNs).
Endosonography Middle Aged Mediastinum Bronchoscopy ROC Curve Diagnosis, Differential Lung Neoplasms Aged Humans Lymph Nodes Adult Retrospective Studies Male Support Vector Machine Female Lymphatic Metastasis
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Using Machine Learning for Endoscopic Detection of Low-Grade Subglottic Stenosis: A Proof of Principle.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery Jul 17, 2024
The current study trains, tests, and evaluates a deep learning algorithm to detect subglottic stenosis (SGS) on endoscopy. A retrospective review of patients undergoing microlaryngoscopy-bronchoscopy was performed. A pretrained image classifier (Resn...
Machine Learning Algorithms Retrospective Studies Humans Deep Learning Bronchoscopy Proof of Concept Study Female Male Middle Aged Laryngoscopy Laryngostenosis
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TransEBUS: The interpretation of endobronchial ultrasound image using hybrid transformer for differentiating malignant and benign mediastinal lesions.

Journal of the Formosan Medical Association = Taiwan yi zhi May 3, 2024
The purpose of this study is to establish a deep learning automatic assistance diagnosis system for benign and malignant classification of mediastinal lesions in endobronchial ultrasound (EBUS) images. EBUS images are in the form of video and contain...
Mediastinum Mediastinal Neoplasms Deep Learning Bronchoscopy Diagnosis, Differential Area Under Curve Endosonography Image Interpretation, Computer-Assisted Humans
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AI co-pilot bronchoscope robot.

Nature communications Jan 4, 2024
The unequal distribution of medical resources and scarcity of experienced practitioners confine access to bronchoscopy primarily to well-equipped hospitals in developed regions, contributing to the unavailability of bronchoscopic services in underdev...
Bronchoscopes Humans Adult Artificial Intelligence Bronchoscopy Robotics
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Performance of Biopsy Tools in Procurement of Lung Tissue in Robot-Assisted Peripheral Navigation: A Comparison.

Respiration; international review of thoracic diseases Nov 30, 2023
INTRODUCTION: Robot-assisted navigation bronchoscopy (RANB) has been gaining traction as a new technology for minimally invasive biopsies of peripheral pulmonary lesions (PPLs). Cryobiopsy is an established method of procuring satisfactory lung tissu...
Cryosurgery Humans Robotics Lung Retrospective Studies Biopsy Lung Neoplasms Bronchoscopy
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Artificial Intelligence Improves Novices' Bronchoscopy Performance: A Randomized Controlled Trial in a Simulated Setting.

Chest Aug 23, 2023
BACKGROUND: Navigating through the bronchial tree and visualizing all bronchial segments is the initial step toward learning flexible bronchoscopy. A novel bronchial segment identification system based on artificial intelligence (AI) has been develop...
Humans Artificial Intelligence Learning Clinical Competence Bronchoscopy Bronchi
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Systematic Bronchoscopy: the Four Landmarks Approach.

Journal of visualized experiments : JoVE Jun 23, 2023
Flexible bronchoscopy is a technically difficult procedure and has been identified as the most important procedure that should be integrated into a simulation-based training program for pulmonologists. However, more specific guidelines that govern br...
Clinical Competence Bronchoscopy Bronchi Artificial Intelligence Computer Simulation Humans
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Enhancing Nodule Biopsy Through Technology Integration.

Innovations (Philadelphia, Pa.) Feb 18, 2023
Technology in navigating to peripheral pulmonary nodules has improved in recent years. The recent integration of a robotic platform using shape-sensing technology and mobile cone-beam computed tomography imaging technology has enhanced confidence in ...
Humans Robotics Biopsy Cone-Beam Computed Tomography Lung Neoplasms Solitary Pulmonary Nodule Bronchoscopy
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