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深度学习驱动的胃肠道内镜和病理影像中的结直肠病变检测

Deep learning driven colorectal lesion detection in gastrointestinal endoscopic and pathological imaging.

作者信息

Cai Yu-Wen, Dong Fang-Fen, Shi Yu-Heng, Lu Li-Yuan, Chen Chen, Lin Ping, Xue Yu-Shan, Chen Jian-Hua, Chen Su-Yu, Luo Xiong-Biao

机构信息

Department of Clinical Medicine, Fujian Medical University, Fuzhou 350004, Fujian Province, China.

Department of Medical Technology and Engineering, Fujian Medical University, Fuzhou 350004, Fujian Province, China.

出版信息

World J Clin Cases. 2021 Nov 6;9(31):9376-9385. doi: 10.12998/wjcc.v9.i31.9376.

DOI:10.12998/wjcc.v9.i31.9376
PMID:34877273
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8610875/
Abstract

Colorectal cancer has the second highest incidence of malignant tumors and is the fourth leading cause of cancer deaths in China. Early diagnosis and treatment of colorectal cancer will lead to an improvement in the 5-year survival rate, which will reduce medical costs. The current diagnostic methods for early colorectal cancer include excreta, blood, endoscopy, and computer-aided endoscopy. In this paper, research on image analysis and prediction of colorectal cancer lesions based on deep learning is reviewed with the goal of providing a reference for the early diagnosis of colorectal cancer lesions by combining computer technology, 3D modeling, 5G remote technology, endoscopic robot technology, and surgical navigation technology. The findings will supplement the research and provide insights to improve the cure rate and reduce the mortality of colorectal cancer.

摘要

结直肠癌是恶性肿瘤中发病率第二高的癌症，也是中国癌症死亡的第四大主要原因。结直肠癌的早期诊断和治疗将提高5年生存率，并降低医疗成本。目前早期结直肠癌的诊断方法包括粪便、血液、内镜检查和计算机辅助内镜检查。本文综述了基于深度学习的结直肠癌病变图像分析与预测研究，旨在通过结合计算机技术、3D建模、5G远程技术、内镜机器人技术和手术导航技术，为结直肠癌病变的早期诊断提供参考。这些研究结果将补充相关研究，并为提高结直肠癌的治愈率和降低死亡率提供见解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea48/8610875/da36351627df/WJCC-9-9376-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea48/8610875/1e73c65c9a39/WJCC-9-9376-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea48/8610875/b1e0e83d860e/WJCC-9-9376-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea48/8610875/9264919ce6c2/WJCC-9-9376-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea48/8610875/da36351627df/WJCC-9-9376-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea48/8610875/1e73c65c9a39/WJCC-9-9376-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea48/8610875/b1e0e83d860e/WJCC-9-9376-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea48/8610875/9264919ce6c2/WJCC-9-9376-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ea48/8610875/da36351627df/WJCC-9-9376-g004.jpg

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Endoscopy. 2021 Aug;53(8):832-836. doi: 10.1055/a-1266-1066. Epub 2020 Dec 16.
2
The prognostic role of tissue TLR2 and TLR4 in colorectal cancer.组织 TLR2 和 TLR4 在结直肠癌中的预后作用。
Virchows Arch. 2020 Nov;477(5):705-715. doi: 10.1007/s00428-020-02833-5. Epub 2020 May 19.
3
Optical Biopsy: Automated Classification of Airway Endoscopic Findings Using a Convolutional Neural Network.
J Clin Med. 2023 Oct 24;12(21):6721. doi: 10.3390/jcm12216721.
4
Introducing AI to the molecular tumor board: one direction toward the establishment of precision medicine using large-scale cancer clinical and biological information.将人工智能引入分子肿瘤专家委员会：利用大规模癌症临床和生物学信息建立精准医学的一个方向。
Exp Hematol Oncol. 2022 Oct 31;11(1):82. doi: 10.1186/s40164-022-00333-7.
光学活检：使用卷积神经网络对气道内镜检查结果进行自动分类。
Laryngoscope. 2022 Feb;132 Suppl 4:S1-S8. doi: 10.1002/lary.28708. Epub 2020 Apr 28.
4
A CNN-Assisted Enhanced Audio Signal Processing for Speech Emotion Recognition.基于 CNN 的增强型音频信号处理在语音情感识别中的应用。
Sensors (Basel). 2019 Dec 28;20(1):183. doi: 10.3390/s20010183.
5
5G and intelligence medicine-how the next generation of wireless technology will reconstruct healthcare?5G与智能医学——下一代无线技术将如何重塑医疗保健？
Precis Clin Med. 2019 Dec;2(4):205-208. doi: 10.1093/pcmedi/pbz020. Epub 2019 Oct 18.
6
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7
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8
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Lancet Oncol. 2019 Dec;20(12):1645-1654. doi: 10.1016/S1470-2045(19)30637-0. Epub 2019 Oct 4.
9
Artificial intelligence in healthcare.人工智能在医疗保健领域的应用。
Nat Biomed Eng. 2018 Oct;2(10):719-731. doi: 10.1038/s41551-018-0305-z. Epub 2018 Oct 10.
10
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Magn Reson Med. 2019 Aug;82(2):786-795. doi: 10.1002/mrm.27758. Epub 2019 Apr 8.