Application of deep learning-based convolutional neural networks in gastrointestinal disease endoscopic examination Article Swipe
Yangyang Wang
,
Bin Liu
,
Jianwei Wang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3748/wjg.v31.i36.111137
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3748/wjg.v31.i36.111137
Gastrointestinal (GI) diseases, including gastric and colorectal cancers, significantly impact global health, necessitating accurate and efficient diagnostic methods. Endoscopic examination is the primary diagnostic tool; however, its accuracy is limited by operator dependency and interobserver variability. Advancements in deep learning, particularly convolutional neural networks (CNNs), show great potential for enhancing GI disease detection and classification. This review explores the application of CNNs in endoscopic imaging, focusing on polyp and tumor detection, disease classification, endoscopic ultrasound, and capsule endoscopy analysis. We discuss the performance of CNN models with traditional diagnostic methods, highlighting their advantages in accuracy and real-time decision support. Despite promising results, challenges remain, including data availability, model interpretability, and clinical integration. Future directions include improving model generalization, enhancing explainability, and conducting large-scale clinical trials. With continued advancements, CNN-powered artificial intelligence systems could revolutionize GI endoscopy by enhancing early disease detection, reducing diagnostic errors, and improving patient outcomes.
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Metadata
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https://doi.org/10.3748/wjg.v31.i36.111137Digital Object Identifier
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Application of deep learning-based convolutional neural networks in gastrointestinal disease endoscopic examinationWork title
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articleOpenAlex work type
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enPrimary language
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2025Year of publication
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2025-09-18Full publication date if available
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Yangyang Wang, Bin Liu, Jianwei WangList of authors in order
- Landing page
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https://doi.org/10.3748/wjg.v31.i36.111137Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.ncbi.nlm.nih.gov/pmc/articles/12476647Direct OA link when available
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0Total citation count in OpenAlex
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165Number of works referenced by this work
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