2. Milivojevic V, Milosavljevic T. Burden of gastroduodenal diseases from the global perspective. Curr Treat Options Gastroenterol 2020;18:148–157.
3. Peery AF, Crockett SD, Murphy CC, et al. Burden and cost of gastrointestinal, liver, and pancreatic diseases in the United States: update 2021. Gastroenterology 2022;162:621–644.
4. Kim YE, Park H, Jo MW, et al. Trends and patterns of burden of disease and injuries in Korea using disability-adjusted life years. J Korean Med Sci 2019;34(Suppl 1):e75.
5. Jung HK, Jang B, Kim YH, et al. [Health care costs of digestive diseases in Korea]. Korean J Gastroenterol 2011;58:323–331Korean.
7. Peterse EFP, Meester RGS, de Jonge L, et al. Comparing the cost-effectiveness of innovative colorectal cancer screening tests. J Natl Cancer Inst 2021;113:154–161.
8. Krzeczewski B, Hassan C, Krzeczewska O, et al. Cost-effectiveness of colonoscopy in an organized screening program. Pol Arch Intern Med 2021;131:128–135.
9. Areia M, Mori Y, Correale L, et al. Cost-effectiveness of artificial intelligence for screening colonoscopy: a modelling study. Lancet Digit Health 2022;4:e436.
10. Ren Y, Zhao M, Zhou D, Xing Q, Gong F, Tang W. Cost-effectiveness analysis of colonoscopy and fecal immunochemical testing for colorectal cancer screening in China. Front Public Health 2022;10:952378.
11. Mori Y, East JE, Hassan C, et al. Benefits and challenges in implementation of artificial intelligence in colonoscopy: World Endoscopy Organization position statement. Dig Endosc 2023;35:422–429.
12. Lee KS, Son SH, Park SH, Kim ES. Automated detection of colorectal tumors based on artificial intelligence. BMC Med Inform Decis Mak 2021;21:33.
13. Lee KS, Ahn KH. Application of artificial intelligence in early diagnosis of spontaneous preterm labor and birth. Diagnostics (Basel) 2020;10:733.
14. Lee KS, Park H. Machine learning on thyroid disease: a review. Front Biosci (Landmark Ed) 2022;27:101.
15. Lee KS, Ham BJ. Machine learning on early diagnosis of depression. Psychiatry Investig 2022;19:597–605.
16. Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems; 2011 Dec 12-17; Siem Reap, Cambodia: Neural Information Processing Systems, 2011: 1097-1105.
17. Lee KS, Park KW. Social determinants of the association among cerebrovascular disease, hearing loss and cognitive impairment in a middle-aged or older population: recurrent neural network analysis of the Korean Longitudinal Study of Aging (2014-2016). Geriatr Gerontol Int 2019;19:711–716.
18. Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation. arXiv [Preprint] 2015;[cited 2023 Jul 1]. Available from:
https://doi.org/10.48550/arXiv.1505.04597.
19. Tan M, Le QV. EfficientNet: rethinking model scaling for Convolutional Neural Networks. arXiv [Preprint] 2019;[cited 2023 Jul 1]. Available from:
https://doi.org/10.48550/arXiv.1905.11946.
20. Tan M, Le QV. EfficientNetV2: smaller models and faster training. arXiv [Preprint] 2021;[cited 2023 Jul 1]. Availble from:
https://doi.org/10.48550/arXiv.2104.00298.
21. Lai LL, Blakely A, Invernizzi M, et al. Separation of color channels from conventional colonoscopy images improves deep neural network detection of polyps. J Biomed Opt 2021;26:015001.
22. Golhar M, Bobrow TL, Khoshknab MP, Jit S, Ngamruengphong S, Durr NJ. Improving colonoscopy lesion classification using semi-supervised deep learning. IEEE Access 2021;9:631–640.
23. Safarov S, Whangbo TK. A-DenseUNet: Adaptive densely connected UNet for polyp segmentation in colonoscopy images with atrous convolution. Sensors (Basel) 2021;21:1441.
24. Jha D, Ali S, Tomar NK, et al. Real-time polyp detection, localization and segmentation in colonoscopy using deep learning. IEEE Access 2021;9:40496–40510.
25. Wang Y, Feng Z, Song L, Liu X, Liu S. Multiclassification of endoscopic colonoscopy images based on deep transfer learning. Comput Math Methods Med 2021;2021:2485934.
26. Su H, Lin B, Huang X, Li J, Jiang K, Duan X. MBFFNet: multibranch feature fusion network for colonoscopy. Front Bioeng Biotechnol 2021;9:696251.
27. Sziová B, Nagy S, Fazekas Z. Application of structural entropy and spatial filling factor in colonoscopy image classification. Entropy (Basel) 2021;23:936.
28. Ma R, Wang R, Zhang Y, et al. RNNSLAM: reconstructing the 3D colon to visualize missing regions during a colonoscopy. Med Image Anal 2021;72:102100.
29. Li K, Fathan MI, Patel K, et al. Colonoscopy polyp detection and classification: dataset creation and comparative evaluations. PLoS One 2021;16:e0255809.
30. Yeung M, Sala E, Schönlieb CB, Rundo L. Focus U-Net: a novel dual attention-gated CNN for polyp segmentation during colonoscopy. Comput Biol Med 2021;137:104815.
31. Syed S, Angel AJ, Syeda HB, et al. The h-ANN Model: comprehensive colonoscopy concept compilation using combined contextual embeddings. Biomed Eng Syst Technol Int Jt Conf BIOSTEC Revis Sel Pap 2022;5:189–200.
32. Wang YP, Jheng YC, Sung KY, et al. Use of U-Net convolutional neural networks for automated segmentation of fecal material for objective evaluation of bowel preparation quality in colonoscopy. Diagnostics (Basel) 2022;12:613.
33. Nogueira-Rodríguez A, Reboiro-Jato M, Glez-Peña D, López-Fernández H. Performance of convolutional neural networks for polyp localization on public colonoscopy image datasets. Diagnostics (Basel) 2022;12:898.
34. Wang L, Chen L, Wang X, et al. Development of a convolutional neural network-based colonoscopy image assessment model for differentiating Crohn’s disease and ulcerative colitis. Front Med (Lausanne) 2022;9:789862.
35. Chen S, Urban G, Baldi P. Weakly supervised polyp segmentation in colonoscopy images using deep neural networks. J Imaging 2022;8:121.
36. Sharma P, Balabantaray BK, Bora K, Mallik S, Kasugai K, Zhao Z. An ensemble-based deep convolutional neural network for computer-aided polyps identification from colonoscopy. Front Genet 2022;13:844391.
37. Byeon SJ, Park J, Cho YA, Cho BJ. Automated histological classification for digital pathology images of colonoscopy specimen via deep learning. Sci Rep 2022;12:12804.
38. Souaidi M, El Ansari M. Multi-scale hybrid network for polyp detection in wireless capsule endoscopy and colonoscopy images. Diagnostics (Basel) 2022;12:2030.
39. Yu T, Lin N, Zhang X, et al. An end-to-end tracking method for polyp detectors in colonoscopy videos. Artif Intell Med 2022;131:102363.
40. Mathew S, Nadeem S, Kaufman A. CLTS-GAN: color-lighting-texture-specular reflection augmentation for colonoscopy. Med Image Comput Comput Assist Interv 2022;2022:519–529.
41. Ramzan M, Raza M, Sharif MI, Kadry S. Gastrointestinal tract polyp anomaly segmentation on colonoscopy images using graft-U-Net. J Pers Med 2022;12:1459.
42. Cui R, Yang R, Liu F, Cai C. N-Net: Lesion region segmentations using the generalized hybrid dilated convolutions for polyps in colonoscopy images. Front Bioeng Biotechnol 2022;10:963590.
43. Yamada M, Shino R, Kondo H, et al. Robust automated prediction of the revised Vienna Classification in colonoscopy using deep learning: development and initial external validation. J Gastroenterol 2022;57:879–889.
44. González-Bueno Puyal J, Brandao P, Ahmad OF, et al. Polyp detection on video colonoscopy using a hybrid 2D/3D CNN. Med Image Anal 2022;82:102625.
45. Raymann J, Rajalakshmi R. GAR-Net: guided attention residual network for polyp segmentation from colonoscopy video frames. Diagnostics (Basel) 2022;13:123.
46. Tang CP, Chang HY, Wang WC, Hu WX. A novel computer-aided detection/diagnosis system for detection and classification of polyps in colonoscopy. Diagnostics (Basel) 2023;13:170.
47. Lewis J, Cha YJ, Kim J. Dual encoder-decoder-based deep polyp segmentation network for colonoscopy images. Sci Rep 2023;13:1183.
48. Li Y. Deep reinforcement learning: an overview. arXiv [Preprint] 2017;[cited 2023 Jul 1]. Available from:
https://doi.org/10.48550/arXiv.1701.07274.
49. Silver D, Huang A, Maddison CJ, et al. Mastering the game of Go with deep neural networks and tree search. Nature 2016;529:484–489.
留言 (0)