Artificial intelligence improves bronchoscopy performance: a randomised crossover trial.

Rationale: Flexible bronchoscopy is an operator-dependent procedure. An automatic bronchial identification system based on artificial intelligence (AI) could help bronchoscopists to perform more complete and structured procedures through automatic guidance.

Methods: 101 participants were included from six different continents at the European Respiratory Society annual conference in Milan, 9-13 September 2023. Participants were split into three groups based on experience: novices (0 bronchoscopies), intermediates (1-249 bronchoscopies) and experienced (≥250 bronchoscopies). The participants performed two bronchoscopies on a realistic physical phantom, one with AI (AmbuBronchoSimulatorTrainingGUIDEv.0.0.1, Prototype version, Ambu) and one Standard procedure. The F1-group received AI guidance for their first procedure, the F2-group for their second. A crossover randomisation controlled for learning by testing. All procedures were automatically rated according to the outcome measures: inspected segments, structured progressions and procedure time.

Results: AI guidance caused the participants to inspect more segments (mean difference, paired t-test: +6.0 segments, p<0.001), perform more structured progressions (+5.2 progressions, p<0.001) and spend more time on the procedure (+72 s, p<0.001) compared to their standard procedures. The effects of AI guidance on inspected segments and structured progression were highest for novices but significant for all experience groups: novices (+8.2 segments, p=0.012 and +6.6 progressions, p<0.001), intermediates (+5.7 segments, p=0.006 and +5.1 progressions, p<0.001) and experienced (+4.3 segments, p=0.006 and +3.8 progressions, p<0.016).

Conclusions: AI guidance helped bronchoscopists of all experience levels to inspect more segments in a more structured order. Clinical implementation of AI guidance could help ensure and document more complete bronchoscopy procedures in the future.