ORIGINAL RESEARCH|ARTICLES IN PRESS

Artificial Intelligence improves novices’ bronchoscopy performance – a randomized controlled trial in a simulated setting

Kristoffer Mazanti Cold, Sujun Xie, Anne Orholm Nielsen, et al

Chest Open Access Published:August 22, 2023

DOI:https://doi.org/10.1016/j.chest.2023.08.015

Abstract

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 developed to help guide trainees toward more effective training.

Research Question

Does feedback from an AI-based automatic bronchial segment identification system improve novice bronchoscopists’ end-of-training performance?

Study Design and Methods

The study was conducted as a randomized controlled trial in a standardized simulated setting. Novices without former bronchoscopy experience practiced on a mannequin. The feedback group (FG, n = 10) received feedback from the AI, and the control group (CG, n = 10) trained according to written instructions. Each participant decided when to end training and proceed to performing a full bronchoscopy without any aids.

Results

The FG performed significantly better on all three outcome measures (median difference, P-value): Diagnostic Completeness (3.5 segments, P<.001), Structured Progress (13.5 correct progressions, P<.001), and Procedure Time (-214 seconds, P=.002).

Interpretation

Training guided by this novel AI makes novices perform more complete, more systematic, and faster bronchoscopies. Future studies should examine its use in a clinical setting and its effects on more advanced learners.