Artificial intelligence improves bronchoscopy performance – a randomized cross-over trial

Abstract

RationaleFlexible 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.Methods101 participants were included from six different continents at the European Respiratory Society annual conference in Milan, 9–13th 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 cross-over randomization controlled for learning by testing. All procedures were automatically rated according to the outcome measures: Inspected segments, Structured Progressions, and Procedure Time.ResultsAI 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=.012 and +6.6 progressions p<0.001), Intermediates (+5.7 segments, p=.006 and +5.1 progressions p<0.001, and Experienced (+4.3 segments, p=.006 and +3.8 progressions p<0.016).ConclusionsAI-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.