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Author Spotlight: Learning Systematic Bronchoscopy in a Simulation-Base Setting

Author Spotlight: Learning Systematic Bronchoscopy in a Simulation-Base Setting

Published on: June 23, 2023

Simulación de entrenamiento basada en inteligencia artificial para mejorar el rendimiento en Broncoscopia Clínica: un

Kristoffer M Cold¹, Arman Arshad², Christian Kildegaard^2,3

¹Copenhagen Academy for Medical Education and Simulation, Rigshospitalet, Copenhagen, Denmark kristoffer.mazanti.cold.01@regionh.dk.

|December 31, 2025

Abstracto

Objectives:

Does a simulation-based training intervention with an artificial intelligence (AI) navigation system improve their clinical bronchoscopy performance? And can the AIs outcome measures be used to evaluate clinical performance?

Design:

Pre-postintervention study.

Setting:

Odense University Hospital of Southern Denmark, pulmonary endoscopy suite.

Participants:

Nine bronchoscopists (4 experienced, >500 bronchoscopies and 5 intermediates, 10-500 bronchoscopies).

Primary Outcome Measures:

Diagnostic completeness (DC), structured progress (SP), procedure time (PT) and procedure efficiency (DC/PT).

Results:

The primary outcome measures showed no statistically significant difference between the pre- and postintervention bronchoscopies DC: 53% versus 59%, p=0.16, SP: 29% versus 32%, p=0.35 and PT: 219 s versus 181 s, p=0.22. The experienced outperformed the intermediates regarding DC: 73% versus 43%, p<0.001, SP: 47% versus 13%, p<0.001 and procedure efficiency: 533 s/full inspection versus 274 s/full inspection, p<0.001 but not on PT: 189 s versus 208 s, p=0.53).

Conclusions:

DC, SP and PT showed no statistically significant difference after a simulation-based training intervention. DC, SP and procedure efficiency differentiated between experienced and intermediate bronchoscopists and can be used to evaluate clinical bronchoscopy performance.

Palabras clave:

Inteligencia Artificial Broncoscopia Cirugía Torácica

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