ArticleCancer medicine2021
Effectiveness of convolutional neural networks in the interpretation of pulmonary cytologic images in endobronchial ultrasound procedures.
Article in Cancer medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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16 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- Artificial intelligence in bronchoscopy: a systematic review.European respiratory review : an official journal of the European Respiratory Society · 2025Pooled it
- Few-Shot Learning for Cytological Classification of Primary Lung Cancer and Pulmonary Metastases During Endobronchial Ultrasound.Diagnostics (Basel, Switzerland) · 2026Article
- Recent advances in artificial intelligence across interventional pulmonology: a narrative review.Journal of thoracic disease · 2026Review
- Large Language Models Utility for Rapid On-Site Evaluation in Interventional Pulmonology.Diagnostics (Basel, Switzerland) · 2026Article
- Applications of Artificial Intelligence in Endobronchial Ultrasound for Lung Cancer Diagnosis and Staging: A Scoping Review.Current oncology (Toronto, Ont.) · 2026Article
- Human and Deep Learning Predictions of Peripheral Lung Cancer Using a 1.3 mm Video Endoscopic Probe.Respirology (Carlton, Vic.) · 2025Article
- Review
- Challenges and Opportunities in Cytopathology Artificial Intelligence.Bioengineering (Basel, Switzerland) · 2025Review
- Automatic lung cancer subtyping using rapid on-site evaluation slides and serum biological markers.Respiratory research · 2024Article
- Review
- AI/ML advances in non-small cell lung cancer biomarker discovery.Frontiers in oncology · 2023Review
- Deep convolutional neural network for rib fracture recognition on chest radiographs.Frontiers in medicine · 2023Article
- Effectiveness of convolutional neural networks in the interpretation of pulmonary cytologic images in endobronchial ultrasound procedures.Cancer medicine · 2021Article
- Cytopathology 2.0: How Artificial Intelligence Is Redefining the Future of Cytopathology.Journal of cytologyReview
- Science progress distinguishing different types of airway stents under bronchoscopy by artificial intelligence.Science progressArticle
- Artificial Intelligence in Interventional Pulmonology.Therapeutic advances in pulmonary and critical care medicineReview
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRapid on-site cytologic evaluation (ROSE) helps to improve the diagnostic accuracy in endobronchial ultrasound (EBUS) procedures. However, cytologists are seldom available to perform ROSE in many institutions. Recent studies have investigated the application of deep learning in cytologic image analysis. As such, the present study analyzed lung cytologic images obtained by EBUS procedures, and employed deep-learning methods to distinguish between benign and malignant cells and to semantically segment malignant cells.
methodsNinety-seven patients who underwent 104 EBUS procedures were enrolled. Four hundred and ninety-nine lung cytologic images obtained via ROSE, including 425 malignant and 74 benign, and most malignant were lung adenocarcinoma (64.3%). All the images were used to train a residual network model with 101 layers (ResNet101), with suitable hyperparameters selected to classify benign and malignant lung cytologic images. An HRNet model was also employed to mark the area of malignant cells. Automatic patch-cropping was adopted to facilitate dataset preparation.
resultsMalignant cells were successfully classified by ResNet101 with 98.8% classification accuracy, 98.8% sensitivity, and 98.8% specificity in patch-based classification; 95.5% classification accuracy in image-based classification; and 92.9% classification accuracy in patient-based classification. Malignant cell area was successfully marked by HRNet with a mean intersection over union of 89.2%. The automatic cropping method enabled the system to complete diagnosis within 1 s.
conclusionsThis is the first study to combine lung cytologic image deep-learning classification with semantic segmentation. The model was optimized for high accuracy and the automatic cropping facilitates the clinical application of our model. The success in both lung cytologic images classification and semantic segmentation on our dataset shows a promising result for clinical application in the future.
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