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Background <p>Flexible 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.</p> Methods <p>An 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 (<i>n</i>=11) or traditional human-led training (<i>n</i>=11). Bronchoscopy skills were assessed using the modified Bronchoscopy Skill and Task Assessment Tool (BSTAT) before and after training. </p> Results <p>The 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, <i>p</i>&lt;0.001) and reduced procedural time (from 217±44 to 101±23 seconds, <i>p</i>&lt;0.001). No significant differences were observed between the AI-based and expert-led training groups. </p> Conclusion <p>We 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.</p>

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Artificial intelligence-based image recognition in bronchoscopy: software development and randomized controlled trial for training evaluation in intensive care residents

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月24日

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  • Beatrice Brunoni,
  • Francesco Zadek,
  • Federica Pampurini,
  • Marco Vettorello,
  • Francesco Baccoli,
  • Federico Cabitza,
  • Roberto Fumagalli,
  • Thomas Langer

摘要

Background

Flexible 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.

Methods

An 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.

Results

The 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.

Conclusion

We 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.