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Related Concept Videos

Endoscopic Studies I: Bronchoscopy and Thoracoscopy01:30

Endoscopic Studies I: Bronchoscopy and Thoracoscopy

Endoscopy is a non-surgical medical technique used to examine a person's internal organs and vessels. This lesson will focus on two types of endoscopic studies: bronchoscopy and thoracoscopy.
Bronchoscopy
Description
Bronchoscopy is a procedure that involves direct visualization of the larynx, trachea, and bronchi for diagnostic and therapeutic purposes. A flexible fiber optic or rigid bronchoscope is used to carry out the procedure. The fiber-optic bronchoscope is more frequently used due to...

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Related Experiment Video

Updated: May 28, 2026

Systematic Bronchoscopy: the Four Landmarks Approach
04:47

Systematic Bronchoscopy: the Four Landmarks Approach

Published on: June 23, 2023

Predicting Diagnostic Success and Procedural Efficiency in Robotic Bronchoscopy Using Machine Learning.

Juliana Guarize1, Claudia Bardoni2, Cristina Diotti2

  • 1Division of Interventional Pulmonology, IEO, European Institute of Oncology IRCCS, 20141 Milan, Italy.

Diseases (Basel, Switzerland)
|May 26, 2026
PubMed
Summary
This summary is machine-generated.

Lesion diameter significantly impacts robotic-assisted bronchoscopy duration and diagnostic yield. This finding highlights the importance of lesion size in planning these procedures for optimal outcomes.

Keywords:
ION Endoluminal Systembronchial signdiagnostic yieldgradient boostinglung cancermachine learningrobotic bronchoscopy

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Robotic-assisted Bronchoscopy Combined with Multimodal Imaging for Targeted Lung Cryobiopsies
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Last Updated: May 28, 2026

Systematic Bronchoscopy: the Four Landmarks Approach
04:47

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Published on: June 23, 2023

Robotic-assisted Bronchoscopy Combined with Multimodal Imaging for Targeted Lung Cryobiopsies
04:10

Robotic-assisted Bronchoscopy Combined with Multimodal Imaging for Targeted Lung Cryobiopsies

Published on: July 19, 2024

Area of Science:

  • Pulmonology
  • Medical Robotics
  • Machine Learning in Medicine

Background:

  • Robotic-assisted bronchoscopy using the ION™ Endoluminal System enables precise navigation to peripheral pulmonary lesions.
  • Procedural efficiency and diagnostic success are affected by patient and lesion characteristics.
  • Understanding these factors is crucial for optimizing robotic bronchoscopy outcomes.

Purpose of the Study:

  • To analyze the influence of lesion diameter, radiological appearance, and bronchial signs on procedural duration and diagnostic yield.
  • To compare conventional regression with gradient boosting machine learning models for predictive insights.
  • To identify key predictors for procedural time and diagnostic accuracy in robotic bronchoscopy.

Main Methods:

  • A retrospective cohort study of 189 procedures using the ION™ Endoluminal System.
  • Analysis of procedural duration and diagnostic yield as primary outcomes.
  • Application of multivariable regression and gradient boosting for predictive modeling and feature importance extraction.

Main Results:

  • Lesion diameter was the primary predictor of procedural time, accounting for 89.2% importance in gradient boosting models.
  • The median lesion diameter was 12.3 mm, with a diagnostic yield of 87.3%.
  • Gradient boosting models showed lesion diameter (85.8%) and bronchial signs (14.2%) as key predictors for diagnostic classification (ROC-AUC = 0.68).

Conclusions:

  • Lesion diameter is a critical factor influencing both procedural efficiency and diagnostic performance in robotic-assisted bronchoscopy.
  • The findings underscore the need for larger datasets to validate these predictors and ensure generalizability.
  • Further research is warranted to refine predictive models for robotic bronchoscopy.