Optical diagnosis of colorectal polyps using convolutional neural networks.
Hadjinicolaou, Andreas V
World J Gastroenterol
Baishideng Publishing Group Inc.
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Kader, R., Hadjinicolaou, A. V., Georgiades, F., Stoyanov, D., & Lovat, L. B. (2021). Optical diagnosis of colorectal polyps using convolutional neural networks.. World J Gastroenterol, 27 (35), 5908-5918. https://doi.org/10.3748/wjg.v27.i35.5908
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.
Artificial intelligence, Computer aided diagnosis, Colorectal Polyps, Optical Diagnosis, Deep Learning, Convolutional Neural Networks, Humans, Colorectal Neoplasms, Colonic Polyps, Colonoscopy, Early Detection of Cancer, Neural Networks, Computer
External DOI: https://doi.org/10.3748/wjg.v27.i35.5908
This record's URL: https://www.repository.cam.ac.uk/handle/1810/330705
Attribution-NonCommercial 4.0 International
Licence URL: https://creativecommons.org/licenses/by-nc/4.0/