ArticleFrontiers in oncology2022
The investigation of construction and clinical application of image recognition technology assisted bronchoscopy diagnostic model of lung cancer.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 9 citations in OpenAlex.
- Recent advances in artificial intelligence across interventional pulmonology: a narrative review.Journal of thoracic disease · 2026Review
- Artificial Intelligence in Pulmonary Endoscopy: Current Evidence, Limitations, and Future Directions.Journal of imaging · 2026Review
- Multi-adapter SAM-inspired bronchoscopic image segmentation for lung cancer diagnosis.Frontiers in oncology · 2026Article
- An accurate prediction for respiratory diseases using deep learning on bronchoscopy diagnosis images.Journal of advanced research · 2025Article
- Review
- BM-BronchoLC - A rich bronchoscopy dataset for anatomical landmarks and lung cancer lesion recognition.Scientific data · 2024Article
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Authors and funding
5 authors at 3 institutions in 1 country.
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Abstract
Background: The incidence and mortality of lung cancer ranks first in China. Bronchoscopy is one of the most common diagnostic methods for lung cancer. In recent years, image recognition technology(IRT) has been more and more widely studied and applied in the medical field. We developed a diagnostic model of lung cancer under bronchoscopy based on deep learning method and tried to classify pathological types. Methods: A total of 2238 lesion images were collected retrospectively from 666 cases of lung cancer diagnosed by pathology in the bronchoscopy center of the Third Xiangya Hospital from Oct.01 2017 to Dec.31 2020 and 152 benign cases from Jun.01 2015 to Dec.31 2020. The benign and malignant images were divided into training, verification and test set according to 7:1:2 respectively. The model was trained and tested based on deep learning method. We also tried to classify different pathological types of lung cancer using the model. Furthermore, 9 clinicians with different experience were invited to diagnose the same test images and the results were compared with the model. Results: The diagnostic model took a total of 30s to diagnose 467 test images. The overall accuracy, sensitivity, specificity and area under curve (AUC) of the model to differentiate benign and malignant lesions were 0.951, 0.978, 0.833 and 0.940, which were equivalent to the judgment results of 2 doctors in the senior group and higher than those of other doctors. In the classification of squamous cell carcinoma (SCC) and adenocarcinoma (AC), the overall accuracy was 0.745, including 0.790 for SCC, 0.667 for AC and AUC was 0.728. Conclusion: The performance of our diagnostic model to distinguish benign and malignant lesions in bronchoscopy is roughly the same as that of experienced clinicians and the efficiency is much higher than manually. Our study verifies the possibility of applying IRT in diagnosis of lung cancer during white light bronchoscopy.
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