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This study aims to create an artificial intelligence model capable of accurately identifying bronchial segments during broncho-endoscopic navigation. To achieve this, we analyzed 126 videos from bronchoscopic procedures conducted on critically ill patients at a university hospital in Buenos Aires, Argentina. A dataset of consistently annotated videos, captured by bronchoscopists with varied expertise, was established. Inter-annotator agreement for image classification was evaluated using Cohen’s kappa coefficient. Images of multiple bronchial segments were used as input to train a convolutional neural network in order to obtain a classification model. This paper presents the annotation schema, labeling guidelines, the developed corpus, and some preliminary results.

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AI-Assisted Bronchoscopy in the Intensive Care Unit: Corpus Development and an Application to Anatomic Position Identification

  • 会议论文

  • 发表日期：

    2025年

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  • Luciano Tarsia,
  • Nicolas Mastropasqua,
  • Indalecio Carboni Bisso,
  • Marcos Las Heras,
  • Valeria Burgos,
  • Marcelo Risk,
  • María Florencia Courtois,
  • Ignacio Fernández Ceballos,
  • Carolina Lockhart,
  • Daniel Acevedo,
  • Viviana Cotik

摘要

This study aims to create an artificial intelligence model capable of accurately identifying bronchial segments during broncho-endoscopic navigation. To achieve this, we analyzed 126 videos from bronchoscopic procedures conducted on critically ill patients at a university hospital in Buenos Aires, Argentina. A dataset of consistently annotated videos, captured by bronchoscopists with varied expertise, was established. Inter-annotator agreement for image classification was evaluated using Cohen’s kappa coefficient. Images of multiple bronchial segments were used as input to train a convolutional neural network in order to obtain a classification model. This paper presents the annotation schema, labeling guidelines, the developed corpus, and some preliminary results.