@article{discovery10135290,
            note = {This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/},
          volume = {27},
           title = {Optical diagnosis of colorectal polyps using convolutional neural networks},
           pages = {5908--5918},
         journal = {World Journal of Gastroenterology},
            year = {2021},
           month = {September},
          number = {35},
        keywords = {Artificial intelligence, Deep learning, Convolutional neural networks, Computer aided diagnosis, Optical diagnosis, Colorectal polyps},
          author = {Kader, R and Hadjinicolaou, AV and Georgiades, F and Stoyanov, D and Lovat, LB},
             url = {http://dx.doi.org/10.3748/wjg.v27.i35.5908},
        abstract = {Colonoscopy remains the gold standard investigation for colorectal cancer screening as it offers the opportunity to both detect and resect pre-malignant and neoplastic polyps. Although technologies for image-enhanced endoscopy are widely available, optical diagnosis has not been incorporated into routine clinical practice, mainly due to significant inter-operator variability. In recent years, there has been a growing number of studies demonstrating the potential of convolutional neural networks (CNN) to enhance optical diagnosis of polyps. Data suggest that the use of CNNs might mitigate the inter-operator variability amongst endoscopists, potentially enabling a "resect and discard"or"leave in"strategy to be adopted in real-time. This would have significant financial benefits for healthcare systems, avoid unnecessary polypectomies of non-neoplastic polyps and improve the efficiency of colonoscopy. Here, we review advances in CNN for the optical diagnosis of colorectal polyps, current limitations and future directions.}
}