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

OBJECTIVES: Bronchoscopy in the mechanically ventilated patient is an important skill for critical-care physicians. However, training opportunity is heterogenous and limited by infrequent caseload or inadequate instructor feedback for satisfactory competencies. A new artificial intelligence (AI) navigational system using augmented reality – the Ambu Broncho Simulator – can guide bronchoscopy training. Is training with the AI system comparable to bedside, expert tutor instruction in improving bronchoscopy performance? DESIGN: A nonblinded, parallel group randomized controlled trial was conducted. SETTING: The study was conducted in a simulated setting at an academic university hospital. SUBJECTS: Critical-care physicians were invited to take part in the study. INTERVENTIONS: Forty participants received 30 minutes of bronchoscopy training, either guided by AI only (artificial intelligence group [AIG]) or by expert tutor feedback (expert tutor group [ETG]). All participants performed a final full navigation bronchoscopy performance test and completed a cognitive load questionnaire, the NASA Task Load Index . MEASUREMENTS AND MAIN RESULTS: Mean intersegmental time (MIT = PT/DC), diagnostic completeness (DC), procedure time (PT), structured progress (SP), and number of segments revisited (SR) were measured. The primary outcome measure assessed was MIT, a measure of bronchoscopic performance efficiency. The secondary outcome measures were DC, PT, SP, and SR. Nineteen participants were randomized to the AIG and 21 participants to the ETG. MIT, PT, and SR were significantly better in the AIG compared to the ETG (median difference, p): MIT (–7.9 s, 0.027), PT (–77 s, 0.022), SR (–7 segments, 0.019); all showing moderate effect sizes (0.35, 0.36, and 0.37, respectively) as per Cohen’s classification. There was no significant difference between the groups for all other final test measures. CONCLUSIONS: Training using an AI system resulted in faster and more efficient bronchoscopy performance by critical-care physicians when compared to expert human tutor instruction. This could change the future of bronchoscopy training in critical care and warrants validation in patients through clinical studies.

Endoscopists' competence can vary widely, as shown in the variation in the adenoma detection rate (ADR). Computer-aided quality assessment (CAQ) can automatically assess performance during individual procedures. In this review we identified and described different CAQ systems for colonoscopy. A systematic review of the literature was done using MEDLINE, EMBASE, and Scopus based on 3 blocks of terms according to the inclusion criteria: colonoscopy, competence assessment, and automatic evaluation. Articles were systematically reviewed by 2 reviewers, first by abstract and then in full text. The methodological quality was assessed using the Medical Education Research Study Quality Instrument (MERSQI). Of 12,575 identified studies, 6831 remained after removal of duplicates and 6806 did not pass the eligibility criteria and were excluded, leaving 25 studies, of which 13 studies were included in the final analysis. Five categories of CAQ systems were identified: withdrawal speedometer (7 studies), endoscope movement analysis (3 studies), effective withdrawal time (1 study), fold examination quality (1 study), and visual gaze pattern (1 study). The withdrawal speedometer was the only CAQ system that tested its feedback by examining changes in ADR. Three studies observed an improvement in ADR, and 2 studies did not. The methodological quality of the studies was high (mean MERSQI, 15.2 points; maximum, 18 points). Thirteen studies developed or tested CAQ systems, most frequently by correlating it to the ADR. Only 5 studies tested feedback by implementing the CAQ system. A meta-analysis was impossible because of the heterogeneous study designs, and more studies are warranted.

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.

Background: Artificial intelligence (AI) systems have been implemented to improve the diagnostic yield and operators’ skills within endoscopy. Similar AI systems are now emerging in bronchoscopy. Our objective was to identify and describe AI systems in bronchoscopy.Methods: A systematic review was performed using MEDLINE, Embase and Scopus databases, focusing on two terms: bronchoscopy and AI. All studies had to evaluate their AI against human ratings. The methodological quality of each study was assessed using the Medical Education Research Study Quality Instrument (MERSQI).Results: 1196 studies were identified, with 20 passing the eligibility criteria. The studies could be divided into three categories: nine studies in airway anatomy and navigation, seven studies in computer-aided detection and classification of nodules in endobronchial ultrasound, and four studies in rapid on-site evaluation. 16 were assessment studies, with 12 showing equal performance and four showing superior performance of AI compared with human ratings. Four studies within airway anatomy implemented their AI, all favouring AI guidance to no AI guidance. The methodological quality of the studies was moderate (mean MERSQI 12.9 points, out of a maximum 18 points).Interpretation: 20 studies developed AI systems, with only four examining the implementation of their AI. The four studies were all within airway navigation and favoured AI to no AI in a simulated setting. Future implementation studies are warranted to test for the clinical effect of AI systems within bronchoscopy.

Background: Bronchoscopy is a challenging technical procedure, and assessment of competence currently relies on expert raters. Human rating is time consuming and prone to rater bias. The aim of this study was to evaluate if a bronchial segment identification system based on artificial intelligence (AI) could automatically, instantly, and objectively assess competencies in flexible bronchoscopy in a valid way. Methods: Participants were recruited at the Clinical Skills Zone of the European Respiratory Society Annual Conference in Milan, 9^th–13^th September 2023. The participants performed one full diagnostic bronchoscopy in a simulated setting and were rated immediately by the AI according to its four outcome measures: diagnostic completeness (DC), structured progress (SP), procedure time (PT), and mean intersegmental time (MIT). The procedures were video-recorded and rated after the conference by two blinded, expert raters using a previously validated assessment tool with nine items regarding anatomy and dexterity. Results: Fifty-two participants from six different continents were included. All four outcome measures of the AI correlated significantly with the experts’ anatomy-ratings (Pearson’s correlation coefficient, P value): DC (r=0.47, P<0.001), SP (r=0.57, P<0.001), PT (r=−0.32, P=0.02), and MIT (r=−0.55, P<0.001) and also with the experts’ dexterity-ratings: DC (r=0.38, P=0.006), SP (r=0.53, P<0.001), PT (r=−0.34, P=0.014), and MIT (r=−0.47, P<0.001). Conclusions: The study provides initial validity evidence for AI-based immediate and automatic assessment of anatomical and navigational competencies in flexible bronchoscopy. SP provided stronger correlations with human experts’ ratings than the traditional DC.