Trial reportBMC medical education2026
Artificial intelligence-based image recognition in bronchoscopy: software development and randomized controlled trial for training evaluation in intensive care residents.
Trial report in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundFlexible bronchoscopy is an essential tool for airway management and both diagnostic and therapeutic interventions, particularly in critical care. Accurate identification of tracheobronchial structures is crucial but challenging for less experienced clinicians, often leading to prolonged procedures and increased complication risks. Simulation-based training using virtual reality or manikins has shown promise, and recent studies suggest that artificial intelligence (AI)-based training outperforms self-directed learning. Limited data exist comparing AI-based bronchoscopy training to expert-led instruction. This study aimed to develop and evaluate a custom-made AI-based software for identifying key tracheobronchial structures and assessing its effectiveness as a training tool for anesthesia and intensive care residents.
methodsAn AI-based software using YOLOv8 artificial neural networks was developed to recognize key tracheobronchial structures from bronchoscopy videos of a high-fidelity manikin. In a randomized trial, 22 second-year anesthesia residents with limited bronchoscopy experience were assigned to either AI-based unsupervised training (n=11) or traditional human-led training (n=11). Bronchoscopy skills were assessed using the modified Bronchoscopy Skill and Task Assessment Tool (BSTAT) before and after training.
resultsThe AI model demonstrated high accuracy, with an average precision-recall AUC of 0.98 and a mean average precision of 0.98. Both groups of residents showed significant improvement in their BSTAT scores (from 30±4 to 53±2, p<0.001) and reduced procedural time (from 217±44 to 101±23 seconds, p<0.001). No significant differences were observed between the AI-based and expert-led training groups.
conclusionWe developed an AI-based software capable of real-time guidance during flexible bronchoscopy. The AI-based training demonstrated comparable efficacy to expert-led instruction, suggesting its potential as a viable tool for unsupervised medical training in flexible bronchoscopy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.