Observational studyScientific reports2022
Deep learning-based diagnosis from endobronchial ultrasonography images of pulmonary lesions.
Observational study in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence in bronchoscopy: a systematic review.European respiratory review : an official journal of the European Respiratory Society · 2025Pooled it
- Artificial intelligence and digital health in bronchoscopy: advances in diagnostic accuracy, real-time navigation and procedural skills training.Breathe (Sheffield, England) · 2026Review
- Artificial Intelligence and Endobronchial Ultrasound for Lymph-Node Detection and Characterization: A Pioneer Multicenter Transatlantic Study.Journal of clinical medicine · 2026Article
- Recent advances in artificial intelligence across interventional pulmonology: a narrative review.Journal of thoracic disease · 2026Review
- Applications of Artificial Intelligence in Endobronchial Ultrasound for Lung Cancer Diagnosis and Staging: A Scoping Review.Current oncology (Toronto, Ont.) · 2026Article
- Artificial Intelligence in Pediatric Bronchoscopy: Current Evidence and Future Perspectives.Pediatric pulmonology · 2026Review
- Artificial Intelligence in Pulmonary Endoscopy: Current Evidence, Limitations, and Future Directions.Journal of imaging · 2026Review
- Self-Supervised Spatio-Temporal Network for Classifying Lung Tumor in EBUS Videos.Diagnostics (Basel, Switzerland) · 2025Article
- An accurate prediction for respiratory diseases using deep learning on bronchoscopy diagnosis images.Journal of advanced research · 2025Article
- Review
- Convergence of evolving artificial intelligence and machine learning techniques in precision oncology.NPJ digital medicine · 2025Article
- Tumor detection on bronchoscopic images by unsupervised learning.Scientific reports · 2025Article
- Deep learning with test-time augmentation for radial endobronchial ultrasound image differentiation: a multicentre verification study.BMJ open respiratory research · 2023Article
- Development and validation of the artificial intelligence (AI)-based diagnostic model for bronchial lumen identification.Translational lung cancer research · 2022Article
- Science progress distinguishing different types of airway stents under bronchoscopy by artificial intelligence.Science progressArticle
- Efficacy and safety of radial probe endobronchial ultrasound-transbronchial lung biopsy in peripheral pulmonary lesions without guide sheath and fluoroscopy.Therapeutic advances in respiratory diseaseArticle
- Artificial Intelligence in Interventional Pulmonology.Therapeutic advances in pulmonary and critical care medicineReview
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Endobronchial ultrasonography with a guide sheath (EBUS-GS) improves the accuracy of bronchoscopy. The possibility of differentiating benign from malignant lesions based on EBUS findings may be useful in making the correct diagnosis. The convolutional neural network (CNN) model investigated whether benign or malignant (lung cancer) lesions could be predicted based on EBUS findings. This was an observational, single-center cohort study. Using medical records, patients were divided into benign and malignant groups. We acquired EBUS data for 213 participants. A total of 2,421,360 images were extracted from the learning dataset. We trained and externally validated a CNN algorithm to predict benign or malignant lung lesions. Test was performed using 26,674 images. The dataset was interpreted by four bronchoscopists. The accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the CNN model for distinguishing benign and malignant lesions were 83.4%, 95.3%, 53.6%, 83.8%, and 82.0%, respectively. For the four bronchoscopists, the accuracy rate was 68.4%, sensitivity was 80%, specificity was 39.6%, PPV was 76.8%, and NPV was 44.2%. The developed EBUS-computer-aided diagnosis system is expected to read EBUS findings that are difficult for clinicians to judge with precision and help differentiate between benign lesions and lung cancers.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.