Development of deep learning-based narrow-band imaging endocytoscopic classification for predicting colorectal lesions from a retrospective study

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.