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Upper Airway Anatomical Landmark Dataset for Automated Bronchoscopy and Intubation.

Ruoyi Hao1, Yang Zhang2, Zhiqing Tang1

  • 1Department of Electronic Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China.

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|December 3, 2025
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A new dataset of upper airway anatomical landmarks aids in automating bronchoscopy and intubation. This resource supports the development of advanced image analysis for respiratory disease diagnosis and treatment.

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Respiratory Medicine

Background:

  • Bronchoscopy and intubation are vital for respiratory care but lack automation in their insertion phase.
  • Advanced image analysis holds potential for automation, yet requires comprehensive datasets.
  • Existing datasets are insufficient for training robust automated systems.

Purpose of the Study:

  • To introduce a novel, comprehensive dataset for training automated bronchoscopy and intubation systems.
  • To provide annotated images covering the entire upper respiratory tract for improved model development.
  • To facilitate the clinical application of automated respiratory procedures.

Main Methods:

  • Development of the Upper Airway Anatomical Landmark (UAAL) Dataset.
  • Annotation of clinical images (3,814) and phantom images (2,746) with anatomical landmarks.
  • Inclusion of instance segmentation masks and bounding boxes for multiple landmark classes.

Main Results:

  • The UAAL Dataset contains 10,330 annotations across 8 classes from clinical images and 4,526 annotations across 9 classes from phantom images.
  • The dataset covers diverse anatomical structures from the nasal cavity to the trachea.
  • Public accessibility of the dataset is a key feature.

Conclusions:

  • The UAAL Dataset is a significant contribution to the field of medical image analysis for respiratory procedures.
  • It will accelerate the development and clinical implementation of automated bronchoscopy and intubation systems.
  • This resource addresses the critical need for comprehensive training data in medical AI.