留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

人工智能在胰胆内镜中的应用现状及研究进展

赖莘秀 王祥

赖莘秀, 王祥. 人工智能在胰胆内镜中的应用现状及研究进展[J]. 协和医学杂志, 2024, 15(2): 387-393. doi: 10.12290/xhyxzz.2023-0524
引用本文: 赖莘秀, 王祥. 人工智能在胰胆内镜中的应用现状及研究进展[J]. 协和医学杂志, 2024, 15(2): 387-393. doi: 10.12290/xhyxzz.2023-0524
LAI Xinxiu, WANG Xiang. Application Status and Research Advances of Artificial Intelligence in Pancreatobiliary Endoscopy[J]. Medical Journal of Peking Union Medical College Hospital, 2024, 15(2): 387-393. doi: 10.12290/xhyxzz.2023-0524
Citation: LAI Xinxiu, WANG Xiang. Application Status and Research Advances of Artificial Intelligence in Pancreatobiliary Endoscopy[J]. Medical Journal of Peking Union Medical College Hospital, 2024, 15(2): 387-393. doi: 10.12290/xhyxzz.2023-0524

人工智能在胰胆内镜中的应用现状及研究进展

doi: 10.12290/xhyxzz.2023-0524
基金项目: 

甘肃省科技计划项目 21YF5FA124

详细信息
    通讯作者:

    王祥, E-mail: wangxiang@lzu.edu.cn

  • 中图分类号: R575.7; TP18

Application Status and Research Advances of Artificial Intelligence in Pancreatobiliary Endoscopy

Funds: 

Science and Technology Project of Gansu Province 21YF5FA124

More Information
  • 摘要: 传统胰胆内镜手术因捕获图像信息耗时长、效率低以及医师水平存在差异而在临床应用中受到限制,而人工智能为胰胆内镜手术提供了高效、准确的图像自动识别方法,可辅助临床医师实现快速精准的预测并指导临床决策。同时,人工智能在内镜质量控制、教学培训等方面亦有较大发展潜力。本文就人工智能在胰胆内镜手术中的应用现状及研究进展进行综述,以期为改进目前的临床诊疗模式、实现胰胆疾病的精准医疗提供新思路。
    作者贡献:赖莘秀负责文献查阅与论文撰写;王祥负责论文指导与论文修订。
    利益冲突:所有作者均声明不存在利益冲突
  • [1] Choi R Y, Coyner A S, Kalpathy-Cramer J, et al. Introduction to machine learning, neural networks, and deep learning[J]. Transl Vis Sci Technol, 2020, 9(2): 14.
    [2] Chan H P, Samala R K, Hadjiiski L M, et al. Deep learning in medical image analysis[J]. Adv Exp Med Biol, 2020, 1213: 3-21.
    [3] Koleth G, Emmanue J, Spadaccini M, et al. Artificial intelligence in gastroenterology: Where are we heading?[J]. Endosc Int Open, 2022, 10(11): E1474-E1480. doi:  10.1055/a-1907-6569
    [4] LE BERRE C, SANDBORN W J, ARIDHI S, et al. Application of artificial intelligence to gastroenterology and hepatology[J]. Gastroenterology, 2020, 158(1): 76-94.e2. doi:  10.1053/j.gastro.2019.08.058
    [5] Jovanovic P, Salkic N N, Zerem E. Artificial neural network predicts the need for therapeutic ERCP in patients with suspected choledocholithiasis[J]. Gastrointest Endosc, 2014, 80(2): 260-268. doi:  10.1016/j.gie.2014.01.023
    [6] Dalai C, Azizian J, Trieu H, et al. Machine learning models compared to existing criteria for noninvasive prediction of endoscopic retrograde cholangiopancreatography-confirmed choledocholithiasis[J]. Liver Res, 2021, 5(4): 224-231. doi:  10.1016/j.livres.2021.10.001
    [7] Kim T, Kim J, Choi H S, et al. Artificial intelligence-assisted analysis of endoscopic retrograde cholangiopancreatography image for identifying ampulla and difficulty of selective cannulation[J]. Sci Rep, 2021, 11(1): 8381. doi:  10.1038/s41598-021-87737-3
    [8] Huang L, Lu X Y, Huang X, et al. Intelligent difficulty scoring and assistance system for endoscopic extraction of common bile duct stones based on deep learning: multicenter study[J]. Endoscopy, 2021, 53(5): 491-498. doi:  10.1055/a-1244-5698
    [9] Bang J Y, Hough M, Hawes R H, et al. Use of artificial intelligence to reduce radiation exposure at fluoroscopy-guided endoscopic procedures[J]. Am J Gastroenterol, 2020, 115(4): 555-561. doi:  10.14309/ajg.0000000000000565
    [10] Bittner J G 4th, Mellinger J D, Imam T, et al. Face and construct validity of a computer-based virtual reality simulator for ERCP[J]. Gastrointest Endosc, 2010, 71(2): 357-364. doi:  10.1016/j.gie.2009.08.033
    [11] Kochar B, Akshintala V S, Afghani E, et al. Incidence, severity, and mortality of post-ERCP pancreatitis: a systematic review by using randomized, controlled trials[J]. Gastrointest Endosc, 2015, 81(1): 143-149.e9. doi:  10.1016/j.gie.2014.06.045
    [12] Saito H, Fujimoto A, Oomoto K, et al. Current approaches and questions yet to be resolved for the prophylaxis of post-endoscopic retrograde cholangiopancreatography pancreatitis[J]. World J Gastrointest Endosc, 2022, 14(11): 657-666. doi:  10.4253/wjge.v14.i11.657
    [13] Archibugi L, Ciarfaglia G, Cárdenas-Jaén K, et al. Machine learning for the prediction of post-ERCP pancreatitis risk: a proof-of-concept study[J]. Dig Liver Dis, 2023, 55(3): 387-393. doi:  10.1016/j.dld.2022.10.005
    [14] Zhang X, Yue P, Zhang J D, et al. A novel machine learning model and a public online prediction platform for prediction of post-ERCP-cholecystitis (PEC)[J]. EClinicalMedicine, 2022, 48: 101431. doi:  10.1016/j.eclinm.2022.101431
    [15] Smith Z L, Mullady D K, Lang G D, et al. A randomized controlled trial evaluating general endotracheal anesthesia versus monitored anesthesia care and the incidence of sedation-related adverse events during ERCP in high-risk patients[J]. Gastrointest Endosc, 2019, 89(4): 855-862. doi:  10.1016/j.gie.2018.09.001
    [16] Hormati A, Aminnejad R, Saeidi M, et al. Prevalence of anesthetic and gastrointestinal complications of endoscopic retrograde cholangiopancreatography[J]. Anesth Pain Med, 2019, 9(4): e95796.
    [17] Kang H, Lee B, Jo J H, et al. Machine-learning model for the prediction of hypoxaemia during endoscopic retrograde cholangiopancreatography under monitored anaesthesia care[J]. Yonsei Med J, 2023, 64(1): 25-34. doi:  10.3349/ymj.2022.0381
    [18] Coté G A, Elmunzer B J, Forster E, et al. Development of an automated ERCP Quality Report Card using structured data fields[J]. Tech Innov Gastrointest Endosc, 2021, 23(2): 129-138. doi:  10.1016/j.tige.2021.01.005
    [19] Imler T D, Sherman S, Imperiale T F, et al. Provider-specific quality measurement for ERCP using natural language processing[J]. Gastrointest Endosc, 2018, 87(1): 164-173.e2. doi:  10.1016/j.gie.2017.04.030
    [20] Norton I D, Zheng Y, Wiersema M S, et al. Neural network analysis of EUS images to differentiate between pancreatic malignancy and pancreatitis[J]. Gastrointest Endosc, 2001, 54(5): 625-629. doi:  10.1067/mge.2001.118644
    [21] Zhu M L, Xu C, Yu J G, et al. Differentiation of pancreatic cancer and chronic pancreatitis using computer-aided diagnosis of endoscopic ultrasound (EUS) images: a diagnostic test[J]. PLoS One, 2013, 8(5): e63820. doi:  10.1371/journal.pone.0063820
    [22] Sǎftoiu A, Vilmann P, Dietrich C F, et al. Quantitative contrast-enhanced harmonic EUS in differential diagnosis of focal pancreatic masses (with videos)[J]. Gastrointest Endosc, 2015, 82(1): 59-69. doi:  10.1016/j.gie.2014.11.040
    [23] Zhang M M, Yang H, Jin Z D, et al. Differential diagnosis of pancreatic cancer from normal tissue with digital imaging processing and pattern recognition based on a support vector machine of EUS images[J]. Gastrointest Endosc, 2010, 72(5): 978-985. doi:  10.1016/j.gie.2010.06.042
    [24] Ozkan M, Cakiroglu M, Kocaman O, et al. Age-based computer-aided diagnosis approach for pancreatic cancer on endoscopic ultrasound images[J]. Endosc Ultrasound, 2016, 5(2): 101-107. doi:  10.4103/2303-9027.180473
    [25] Marya N B, Powers P D, Chari S T, et al. Utilisation of artificial intelligence for the development of an EUS-convolutional neural network model trained to enhance the diagnosis of autoimmune pancreatitis[J]. Gut, 2021, 70(7): 1335-1344. doi:  10.1136/gutjnl-2020-322821
    [26] Goyal H, Sherazi S A A, Gupta S, et al. Application of artificial intelligence in diagnosis of pancreatic malignancies by endoscopic ultrasound: a systemic review[J]. Therap Adv Gastroenterol, 2022, 15: 17562848221093873.
    [27] Tonozuka R, Itoi T, Nagata N, et al. Deep learning analysis for the detection of pancreatic cancer on endosonographic images: a pilot study[J]. J Hepatobiliary Pancreat Sci, 2021, 28(1): 95-104. doi:  10.1002/jhbp.825
    [28] Kuwahara T, Hara K, Mizuno N, et al. Usefulness of deep learning analysis for the diagnosis of malignancy in intraduc-tal papillary mucinous neoplasms of the pancreas[J]. Clin Transl Gastroenterol, 2019, 10(5): 1-8.
    [29] Udriştoiu A L, Cazacu I M, Gruionu L G, et al. Real-time computer-aided diagnosis of focal pancreatic masses from endoscopic ultrasound imaging based on a hybrid convolutional and long short-term memory neural network model[J]. PLoS One, 2021, 16(6): e0251701. doi:  10.1371/journal.pone.0251701
    [30] Jang S I, Kim Y J, Kim E J, et al. Diagnostic performance of endoscopic ultrasound-artificial intelligence using deep learning analysis of gallbladder polypoid lesions[J]. J Gastroenterol Hepatol, 2021, 36(12): 3548-3555. doi:  10.1111/jgh.15673
    [31] Spadaccini M, Koleth G, Emmanuel J, et al. Enhanced endoscopic ultrasound imaging for pancreatic lesions: The road to artificial intelligence[J]. World J Gastroenterol, 2022, 28(29): 3814-3824. doi:  10.3748/wjg.v28.i29.3814
    [32] Ishikawa T, Hayakawa M, Suzuki H, et al. Development of a novel evaluation method for endoscopic ultrasound-guided fine-needle biopsy in pancreatic diseases using artificial intelligence[J]. Diagnostics (Basel), 2022, 12(2): 434. doi:  10.3390/diagnostics12020434
    [33] Zhang J, Zhu L R, Yao L W, et al. Deep learning-based pancreas segmentation and station recognition system in EUS: development and validation of a useful training tool (with video)[J]. Gastrointest Endosc, 2020, 92(4): 874-885.e3. doi:  10.1016/j.gie.2020.04.071
    [34] Yao L W, Zhang J, Liu J, et al. A deep learning-based system for bile duct annotation and station recognition in linear endoscopic ultrasound[J]. EBioMedicine, 2021, 65: 103238. doi:  10.1016/j.ebiom.2021.103238
    [35] Iwasa Y, Iwashita T, Takeuchi Y, et al. Automatic segmentation of pancreatic tumors using deep learning on a video image of contrast-enhanced endoscopic ultrasound[J]. J Clin Med, 2021, 10(16): 3589. doi:  10.3390/jcm10163589
    [36] Seo K, Lim J H, Seo J, et al. Semantic segmentation of pancreatic cancer in endoscopic ultrasound images using deep learning approach[J]. Cancers (Basel), 2022, 14(20): 5111. doi:  10.3390/cancers14205111
    [37] Stassen P M C, De Jonge P J F, Webster G J M, et al. Clinical practice patterns in indirect peroral cholangiopancreatoscopy: outcome of a European survey[J]. Endosc Int Open, 2021, 9(11): E1704-E1711. doi:  10.1055/a-1535-1458
    [38] Gerges C, Beyna T, Tang R S Y, et al. Digital single-operator peroral cholangioscopy-guided biopsy sampling versus ERCP-guided brushing for indeterminate biliary strictures: a prospective, randomized, multicenter trial (with video)[J]. Gastrointest Endosc, 2020, 91(5): 1105-1113. doi:  10.1016/j.gie.2019.11.025
    [39] Jang S, Stevens T, Kou L, et al. Efficacy of digital single-operator cholangioscopy and factors affecting its accuracy in the evaluation of indeterminate biliary stricture[J]. Gastrointest Endosc, 2020, 91(2): 385-393.e1. doi:  10.1016/j.gie.2019.09.015
    [40] Marya N B, Powers P D, Petersen B T, et al. Identification of patients with malignant biliary strictures using a cholangioscopy-based deep learning artificial intelligence (with video)[J]. Gastrointest Endosc, 2023, 97(2): 268-278.e1. doi:  10.1016/j.gie.2022.08.021
    [41] Ribeiro T, Saraiva M M, Afonso J, et al. Automatic identification of papillary projections in indeterminate biliary strictures using digital single-operator cholangioscopy[J]. Clin Transl Gastroenterol, 2021, 12(11): e00418. doi:  10.14309/ctg.0000000000000418
    [42] Robles-Medranda C, Oleas R, Sánchez-Carriel M, et al. Vascularity can distinguish neoplastic from non-neoplastic bile duct lesions during digital single-operator cholangioscopy[J]. Gastrointest Endosc, 2021, 93(4): 935-941. doi:  10.1016/j.gie.2020.07.025
    [43] Pereira P, Mascarenhas M, Ribeiro T, et al. Automatic detection of tumor vessels in indeterminate biliary strictures in digital single-operator cholangioscopy[J]. Endosc Int Open, 2022, 10(3): E262-E268. doi:  10.1055/a-1723-3369
    [44] Saraiva M M, Ribeiro T, Ferreira J P S, et al. Artificial intelligence for automatic diagnosis of biliary stricture malignancy status in single-operator cholangioscopy: a pilot study[J]. Gastrointest Endosc, 2022, 95(2): 339-348. doi:  10.1016/j.gie.2021.08.027
  • 加载中
计量
  • 文章访问数:  75
  • HTML全文浏览量:  15
  • PDF下载量:  7
  • 被引次数: 0
出版历程
  • 收稿日期:  2023-11-06
  • 录用日期:  2023-11-20
  • 网络出版日期:  2023-11-23
  • 刊出日期:  2024-03-30

目录

    /

    返回文章
    返回

    【温馨提醒】近日,《协和医学杂志》编辑部接到作者反映,有多名不法人员冒充期刊编辑发送见刊通知,鼓动作者添加微信,从而骗取版面费的行为。特提醒您,本刊与作者联系的方式均为邮件通知或电话,稿件进度通知邮箱为:mjpumch@126.com,编辑部电话为:010-69154261,请提高警惕,谨防上当受骗!如有任何疑问,请致电编辑部核实。谢谢!