Abstract Bronchoscopy and intubation play crucial roles in respiratory disease diagnosis and treatment, yet the automation of their initial insertion phase remains limited. Advanced image analysis presents a viable solution to this challenge. However, insufficient comprehensive, publicly available datasets for training such models have hindered progress. We present a novel Upper Airway Anatomical Landmark (UAAL) Dataset, which annotates multiple anatomical landmark classes visualized through a bronchoscope, including the nose, nostril, channel, glottis, glottic aperture, vocal fold, and trachea, encompassing the entire upper respiratory tract from the nasal cavity to the trachea. It includes 3,814 clinical images from 82 patients with 10,330 annotations (4,910 instance segmentation masks and 5,420 bounding boxes) across 8 classes and 2,746 supplementary phantom images with 4,526 annotations (2,795 instance segmentation masks and 1,551 bounding boxes) across 9 classes. With its key contributions of diverse anatomical coverage, clinical data, supplementary phantom data, and public accessibility, this dataset will contribute to bronchoscopy and intubation automation systems, facilitating their transition from laboratory to clinical applications.
Tang et al. (Wed,) studied this question.
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