Bach Mai hospital, Hanoi, 10000, Vietnam.
Hanoi Medical University, Hanoi, 10000, Vietnam.
Sci Data. 2024 Mar 28;11(1):321. doi: 10.1038/s41597-024-03145-y.
Flexible bronchoscopy has revolutionized respiratory disease diagnosis. It offers direct visualization and detection of airway abnormalities, including lung cancer lesions. Accurate identification of airway lesions during flexible bronchoscopy plays an important role in the lung cancer diagnosis. The application of artificial intelligence (AI) aims to support physicians in recognizing anatomical landmarks and lung cancer lesions within bronchoscopic imagery. This work described the development of BM-BronchoLC, a rich bronchoscopy dataset encompassing 106 lung cancer and 102 non-lung cancer patients. The dataset incorporates detailed localization and categorical annotations for both anatomical landmarks and lesions, meticulously conducted by senior doctors at Bach Mai Hospital, Vietnam. To assess the dataset's quality, we evaluate two prevalent AI backbone models, namely UNet++ and ESFPNet, on the image segmentation and classification tasks with single-task and multi-task learning paradigms. We present BM-BronchoLC as a reference dataset in developing AI models to assist diagnostic accuracy for anatomical landmarks and lung cancer lesions in bronchoscopy data.
柔性支气管镜检查已经彻底改变了呼吸系统疾病的诊断方式。它提供了对气道异常的直接可视化和检测，包括肺癌病变。在柔性支气管镜检查中准确识别气道病变对于肺癌的诊断起着重要作用。人工智能（AI）的应用旨在帮助医生识别支气管镜图像中的解剖学标志和肺癌病变。这项工作描述了 BM-BronchoLC 的开发，这是一个包含 106 例肺癌和 102 例非肺癌患者的丰富支气管镜数据集。该数据集包含了详细的解剖学标志和病变的定位和分类注释，由越南 Bach Mai 医院的资深医生精心制作。为了评估数据集的质量，我们在图像分割和分类任务上评估了两种流行的 AI 骨干模型，即 UNet++和 ESFPNet，使用单任务和多任务学习范式。我们将 BM-BronchoLC 作为一个参考数据集，用于开发人工智能模型，以提高支气管镜数据中解剖学标志和肺癌病变的诊断准确性。
