Optical diagnosis of colorectal polyps using convolutional neural networks
| Autor: | Andreas V. Hadjinicolaou, Danail Stoyanov, Fanourios Georgiades, Laurence Lovat, Rawen Kader |
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| Přispěvatelé: | Georgiades, Fanourios [0000-0003-0440-2720], Lovat, Laurence B [0000-0003-4542-3915], Apollo - University of Cambridge Repository |
| Rok vydání: | 2021 |
| Předmět: |
Colorectal polyps
Artificial intelligence medicine.medical_specialty Computer science Colonic Polyps Colonoscopy Optical diagnosis Convolutional neural network medicine Humans Medical physics Early Detection of Cancer medicine.diagnostic_test business.industry Deep learning Gastroenterology Minireviews General Medicine Gold standard (test) Computer aided diagnosis Computer-aided diagnosis Colorectal cancer screening Convolutional neural networks Neural Networks Computer Colorectal Neoplasms business Healthcare system |
| Zdroj: | World Journal of Gastroenterology |
| ISSN: | 1007-9327 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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