ArticleNature communications2025
Development of deep learning-based narrow-band imaging endocytoscopic classification for predicting colorectal lesions from a retrospective study.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence-based computer-aided detection systems for adenomas during colonoscopy: a systematic review and Bayesian network meta-analysis.Frontiers in artificial intelligence · 2026Pooled it
- Human-machine collaboration in colorectal cancer screening: a narrative review of artificial intelligence-assisted colonoscopy.Frontiers in medicine · 2026Review
- AI-enabled multi-omics integration in colorectal cancer: from molecular stratification to clinical translation.Frontiers in cell and developmental biology · 2026Review
- Development of deep learning-based narrow-band imaging endocytoscopic classification for predicting colorectal lesions from a retrospective study.Nature communications · 2025Article
Corrections and comments
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Authors and funding
16 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data-driven approaches have advanced colorectal lesion diagnosis in digestive endoscopy, yet their application in endocytoscopy (EC)-a high-magnification imaging technique-remains limited, with most studies relying on conventional machine learning methods like support vector machines. Inspired by the success of large-scale language models that leverage progressive pre-training, we develop a computer-aided diagnosis (CAD) model using narrow-band imaging endocytoscopy (EC-NBI) to classify colorectal lesions (non-neoplastic lesions, adenomas, and invasive cancers). Here, we show that our model, trained through a multi-stage pre-training strategy combined with supervised deep clustering, outperforms state-of-the-art supervised methods in a multi-center retrospective cohort. Notably, it surpasses endoscopists' diagnostic accuracy in human-machine competitions and enhances their performance when used as an assistive tool. This EC-NBI CAD model significantly improves the accuracy and consistency of diagnosing colorectal lesions, laying a foundation for future early cancer screening, particularly for distinguishing superficial and deep submucosal invasive cancers, pending further expansive multi-center data.
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Registered trials
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.