ArticleFrontiers in oncology2022
Real-Time Artificial Intelligence-Based Histologic Classifications of Colorectal Polyps Using Narrow-Band Imaging.
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 4 papers.
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4 citing papers in PubMed.
- Article
- Machine Learning-Based Prediction of Histopathological Classification in Colorectal Polyps.The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2025Article
- Classification of colon polyps with malignant potential using statistical analysis of features extracted from ex vivo optical coherence tomography images.Biomedical engineering online · 2025Article
- Improving the endoscopic recognition of early colorectal carcinoma using artificial intelligence: current evidence and future directions.Endoscopy international open · 2024Review
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10 authors.
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Abstract
Background and Aims: With the development of artificial intelligence (AI), we have become capable of applying real-time computer-aided detection (CAD) in clinical practice. Our aim is to develop an AI-based CAD-N and optimize its diagnostic performance with narrow-band imaging (NBI) images. Methods: We developed the CAD-N model with ResNeSt using NBI images for real-time assessment of the histopathology of colorectal polyps (type 1, hyperplastic or inflammatory polyps; type 2, adenomatous polyps, intramucosal cancer, or superficial submucosal invasive cancer; type 3, deep submucosal invasive cancer; and type 4, normal mucosa). We also collected 116 consecutive polyp videos to validate the accuracy of the CAD-N. Results: A total of 10,573 images (7,032 images from 650 polyps and 3,541 normal mucous membrane images) from 478 patients were finally chosen for analysis. The sensitivity, specificity, PPV, NPV, and accuracy for each type of the CAD-N in the test set were 89.86%, 97.88%, 93.13%, 96.79%, and 95.93% for type 1; 93.91%, 95.49%, 91.80%, 96.69%, and 94.94% for type 2; 90.21%, 99.29%, 90.21%, 99.29%, and 98.68% for type 3; and 94.86%, 97.28%, 94.73%, 97.35%, and 96.45% for type 4, respectively. The overall accuracy was 93%. We also built models for polyps ≤5 mm, and the sensitivity, specificity, PPV, NPV, and accuracy for them were 96.81%, 94.08%, 95%, 95.97%, and 95.59%, respectively. Video validation results showed that the sensitivity, specificity, and accuracy of the CAD-N were 84.62%, 86.27%, and 85.34%, respectively. Conclusions: We have developed real-time AI-based histologic classifications of colorectal polyps using NBI images with good accuracy, which may help in clinical management and documentation of optical histology results.
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