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Background: Colorectal cancer (CRC) is the third most common cancer worldwide. Although colonoscopy screening has been proven as an effective strategy for preventing CRC unfortunately, even conventional colonoscopy by expert gastroenterologists can miss adenomas or pre-cancerous lesions in up to 25% of cases. This systematic review aimed to classify colorectal polyps (CRP) or CRC in endoscopic clinic settings using a new machine learning method, convolutional neural network (CNN). Methods: We will search PubMed/MEDLINE, Scopus, Web of Science, IEEE, Inspec, ProQuest, Google Scholar, Microsoft Academic Search, ScienceOpen, arXiv, and bioRxiv from 1 st January 2010 to the 31 th of July 2020.  Our search will not be restricted based on language or geographical area. The primary studies will be selected that have observational design (cross-sectional, case control or cohort); the study subjects will be adult patients (>= 18 years old) referred to colonoscopy clinics; and the results of their colonoscopy evaluation will be available in the form of images or videos. The extracted data will be combined using meta-analysis of prediction models. The primary data synthesis will be performed based on area under curve-receiver operating characteristic curve and/or accuracy measures. We will use Stata version 14.2 (Statacorp; College Station, TX) for primary and secondary data synthesis. Conclusion: The inferences of our secondary research will provide evidence to evaluate the prognostic role of CNN in discriminating CRP or CRC in colonoscopy settings.

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Classifying colorectal cancer or colorectal polyps in endoscopic setting using convolutional neural network: protocol for a systematic review and meta-analysis