切换至 "中华医学电子期刊资源库"

中华胃肠内镜电子杂志 ›› 2026, Vol. 13 ›› Issue (02) : 125 -129. doi: 10.3877/cma.j.issn.2095-7157.2026.02.009

综述

人工智能辅助结肠镜识别遗漏息肉研究进展
张津1, 熊英2,3,4,()   
  1. 1067000 承德,承德医学院研究生学院
    2071000 保定,保定市第一中心医院消化内科
    3071000 保定,保定市第一中心医院内镜诊疗中心
    4071000 保定,保定市胃肠动力相关疾病诊断重点实验室
  • 收稿日期:2025-10-14 出版日期:2026-05-15
  • 通信作者: 熊英
  • 基金资助:
    河北省卫生健康委员会科研基金项目(20240539)

Progress in artificial intelligence for colonoscopy to reduce overlooked polyps

Jin Zhang1, Ying Xiong2,3,4,()   

  1. 1Chengde Medical University, Chengde 067000, China
    2Digestive Department, No.1 Central Hospital of Baoding, Baoding 071000, China
    3Endoscopy Center, No.1 Central hospital of Baoding, Baoding 071000, China
    4Baoding City Key Laboratory of Diagnosis and Treatment for Gastrointestinal Dynamics Related Diseases, Baoding 071000, China
  • Received:2025-10-14 Published:2026-05-15
  • Corresponding author: Ying Xiong
引用本文:

张津, 熊英. 人工智能辅助结肠镜识别遗漏息肉研究进展[J/OL]. 中华胃肠内镜电子杂志, 2026, 13(02): 125-129.

Jin Zhang, Ying Xiong. Progress in artificial intelligence for colonoscopy to reduce overlooked polyps[J/OL]. Chinese Journal of Gastrointestinal Endoscopy(Electronic Edition), 2026, 13(02): 125-129.

随着人工智能的发展,现已应用于医学领域。人工智能在结肠镜检查方面应用越来越普遍,受到人们广泛关注。结直肠癌发生率和死亡率呈逐年上升趋势。由于结直肠癌发展周期和演变,使得人们有充分的机会预防结直肠癌的发生发展。结肠镜检查发现并切除结直肠息肉是预防结直肠癌的重要方法。然而,在检查过程中存在着遗漏结直肠病变的现象。大多数间期结直肠癌源于初次结肠镜检查被遗漏的息肉,有效地控制漏诊率将直接降低间期结直肠癌的发生与发展。人工智能辅助结肠镜检查在识别遗漏息肉方面发挥显著优势,不仅在检查过程中直接影响降低息肉漏诊率,还能在患者肠道准备阶段和对低年资医师培养方面发挥至关重要的作用。

With the development of artificial intelligence (AI), its applications in medicine have expanded significantly.In particular, AI-assisted colonoscopy is becoming more and more common and has received widespread attention.Colorectal cancer (CRC) incidence and mortality continue to rise annually; however, given the prolonged developmental cycle of CRC, there is a critical opportunity for early intervention.Colonoscopy with polyp detection and removal remains a cornerstone of CRC prevention.Nonetheless, missed colorectal lesions during colonoscopy remain a persistent concern.Since the majority of interval colorectal cancers arise from polyps overlooked at the index colonoscopy, effectively reducing the miss rate will directly curtail the incidence and progression of interval CRC.AI-assisted colonoscopy demonstrates substantial advantages in identifying missed polyps, not only by directly decreasing polyp miss rates during procedures but also by enhancing bowel preparation quality and aiding in the training of novice endoscopists.

表1 国内外AI辅助结肠镜系统降低息肉/腺瘤漏诊率的临床证据汇总
系统名称 AI类型 研究设计 主要结局指标 局限性/备注
GI Genius (Medtronic) CADe 多中心、串联结肠镜研究[14] AMR: 15.5% vs. 32.4% 显著降低总体AMR达18.7%,尤其在<5 mm息肉中效果明显;假阴性率*(6.8%)明显降低。
MAGENTIQ-COLO (Magentiq Eye) CADe+CADx 国际多中心RCT[17] AMR:19.0% vs. 36.0% 有效降低AMR,尤其是<5 mm的腺瘤,对检测凹陷型、侧向发育型肿瘤更有优势。
CAD EYE (Fujifilm) CADe+CADx 单中心RCT[22] 直肠乙状结肠AMR: 11.9% vs. 26.0% 在特定肠段(乙状结肠及直肠)显著降低AMR。但该研究为单中心、样本量有限。
串联结肠镜检查、随机对照[23] AMR:17.4% vs. 30.3% 该研究样本量较少(94例),仍需更大的数据支持这一结论。
YOLO(YOLO v3) CADe 多中心RCT[24] PMR: 13.8% vs. 36.7%;AMR: 14.2% vs. 40.6% 证实了该系统在临床应用,包括降低结直肠病变漏诊率中的有效性,且与医师经验、疲劳度无关;该系统版本较多,其余版本的效果仍需进一步研究。
EndoScreener CADe 前瞻性、串联结肠镜研究[30] PMR: 12.98% vs. 45.9%;AMR: 13.89% vs. 40% 显著降低各肠段息肉及腺瘤漏诊率,对平坦、边界模糊、<5 mm的息肉更为敏感。
ENDOANGEL CADe+CADx 回顾性研究[32] AMR :11.9% vs. 24.3% 集成CADe/CADx,兼具盲区监测功能;成本效益方面更具有经济性[34]
SKOUT (Iterative Scopes) CADe RCT [15] ADR:54.2% vs. 40.6% 一项大型RCT显示ADR、SSL检出率未显著改善,可能与研究者均为高年资医师有关,对漏诊率的影响仍需多中心、大样本进一步研究。
Endo-AID (Olympus) CADe RCT [19,20] ADR:55.1% vs. 43.8% 尤其是BBPS为6~7分时,能更好地发挥作用。研究多集中于提升ADR,尤其是微小平坦型腺瘤,直接与结直肠息肉漏诊率相关证据有待进一步报道。
[1]
Sung HFerlay JSiegel RL,et al.Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries[J].CA Cancer J Clin202171(3):209-249.
[2]
Makar JAbdelmalak JCon D,et al.Use of artificial intelligence improves colonoscopy performance in adenoma detection: a systematic review and meta-analysis[J].Gastrointest Endosc2025101(1):68-81.e8.
[3]
Zhao SWang SPan P,et al.Magnitude,Risk Factors, and Factors Associated With Adenoma Miss Rate of Tandem Colonoscopy:A Systematic Review and Meta-analysis[J].Gastroenterology2019156(6): 1661-1674.e11.
[4]
Anderson RBurr NEValori R.Causes of post-colonoscopy colorectal cancers based on world endoscopy organization system of analysis[J]. Gastroenterology2020158(5):1287-1299.e2.
[5]
Makar JAbdelmalak JCon D,et al.Use of artificial intelligence improves colonoscopy performance in adenoma detection: a systematic review and meta-analysis[J].Gastrointest Endosc2025101(1):68-81.e8.
[6]
于晓欢,路璐,郑权,等.结肠镜新技术对提高结直肠腺瘤性息肉检出率的影响[J/OL].中华胃肠内镜电子杂志2022, 9(4): 219-224.
[7]
Bisschops REast JEHassan C,et al.Advanced imaging for detection and differentiation of colorectal neoplasia:European Society of Gastrointestinal Endoscopy (ESGE) Guidelin-Update 2019[J].Endoscopy, 201951(12):1155-1179.
[8]
Vinsard DGMori YMisawa M,et al.Quality assurance of computer- aided detection and diagnosis in colonoscopy[J].Gastrointest Endosc, 201990(1):55-63.
[9]
Repici ASpadaccini MAntonelli G,et al.Artificial intelligence and colonoscopy experience: lessons from two randomized trials[J].Gut2022, 71(4):757-765.
[10]
Barua IVinsard DGJodal HC,et al.Artificial intelligence for polyp detection during colonoscopy:a systematic review and meta-analysis[J]. Endoscopy202153(3):277-284.
[11]
Baumer SStreicher KAlqahtani SA,et al.Accuracy of polyp characterization by artificial intelligence and endoscopists:a prospective, non-randomized study in a tertiary endoscopy center[J].Endosc Int Open, 202311(9):E818-E828.
[12]
Ahsan MAnderson ZJarbath M,et al.The Impact of Computer-aided Detection Technology in Adenoma Detection Rate Among Experienced Endoscopists in the Community Setting[J].J Community Hosp Intern Med Perspect202414(5):42-48.
[13]
Shaukat ALichtenstein DRSomers SC,et al.Computer-aided detection improves adenomas per colonoscopy for screening and surveillance colonoscopy:a randomized trial[J].Gastroenterology2022, 163(3):732-741.
[14]
Wallace MBSharma PBhandari P,et al.Impact of artificial intelligence on miss rate of colorectal neoplasia[J].Gastroenterology, 2022163(1):295-304.e5.
[15]
Shaukat AColucci DErisson L,et al.Improvement in adenoma detection using a novel artificial intelligence-aided polyp detection device[J].Endosc Int Open20219(2):E263-E270.
[16]
Maas MHJRath TSpada C,et al.A computer-aided detection system in the everyday setting of diagnostic, screening,and surveillance colonoscopy:an international,randomized trial[J].Endoscopy202456(11): 843-850.
[17]
Maas MHJNeumann HShirin H,et al.A computer-aided polyp detection system in screening and surveillance colonoscopy:an international, multicentre, randomised, tandem trial[J].Lancet Digit Health20246(3):e157-e165.
[18]
Lau LHSHo JCLLai JCT,et al.Effect of real-time computer-aided polyp detection system (ENDO-AID) on adenoma detection in endoscopists-in-training:a randomized trial[J].Clin Gastroenterol Hepatol, 202422(3):630-641.e4.
[19]
Gimeno-García AZNegrin DHHernández A,et al.Usefulness of a novel computer-aided detection system for colorectal neoplasia:a randomized controlled trial[J].Gastrointest Endosc202397(3):528-536. e1.
[20]
Schauer CChieng MWang M,et al.Artificial intelligence improves adenoma detection rate during colonoscopy[J].NZ Med J2022135 (1561):22-30.
[21]
De Lange GProuvost VRahmi G,et al.Artificial intelligence for characterization of colorectal polyps:Prospective multicenter study[J]. Endosc Int Open202412(3):E413-E418.
[22]
Nakashima HKitazawa NFukuyama C,et al.Clinical evaluation of computer-aided colorectal neoplasia detection using a novel endoscopic artificial intelligence:a single-center randomized controlled trial[J]. Digestion2023104(3):193-201.
[23]
Hiratsuka YHisabe TOhtsu K,et al.Evaluation of artificial intelligence: computer-aided detection of colorectal polyps[J].J Anus Rectum Colon20259(1):79-87.
[24]
Kamba STamai NSaitoh I,et al.Reducing adenoma miss rate of colonoscopy assisted by artificial intelligence:a multicenter randomized controlled trial[J].J Gastroenterol202156(8):746-757.
[25]
Zhou GXiao XTu M,et al.Computer aided detection for laterally spreading tumors and sessile serrated adenomas during colonoscopy[J]. PLoS One202015(4):e0231880.
[26]
Wang PBerzin TMGlissen Brown JR,et al.Real time automatic detection system increases colonoscopic polyp and adenoma detection rates:a prospective randomised controlled study[J].Gut201968(10):1813 -1819.
[27]
Wang PLiu XBerzin TM,et al.Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial):a double-blind randomised study[J]. Lancet Gastroenterol Hepatol20205(4):343-351.
[28]
Liu PWang PGlissen Brown JR,et al.The single-monitor trial: an embedded CADe system increased adenoma detection during colonoscopy:a prospective randomized study[J].Ther Adv Gastroenterol, 202013:1756284820979165.
[29]
Wang PLiu XGKang M,et al.Artificial intelligence empowers the second-observer strategy for colonoscopy:a randomized clinical trial[J]. Gastroenterol Rep (Oxf)202311:goac081.
[30]
Wang PLiu PGlissen Brown JR,et al.Lower adenoma miss rate of computer-aided detection-assisted colonoscopy vs. routine white-light colonoscopy in a prospective tandem study[J].Gastroenterology2020, 159(4):1252-1261.
[31]
Glissen Brown JRMansour NMWang P,et al.Deep learning computer-aided polyp detection reduces adenoma miss rate:a united states multi-center randomized tandem colonoscopy study (CADeT-CS trial)[J]. Clin Gastroenterol Hepatol202220(7):1499-1507.
[32]
Wang YHe C.ENDOANGEL improves detection of missed colorectal adenomas in second colonoscopy:a retrospective study[J]. Medicine2024103(28):e38938.
[33]
李琼霞,李秀梅,叶颖剑,等. "内镜精灵"辅助结肠镜在结肠息肉检查中的应用[J].中华全科医学202523(3):417-420.
[34]
李佳,吴练练,杜代如,等.消化内镜人工智能辅助诊疗设备的成本效益分析[J].中华消化内镜杂志202340(3):206-211.
[35]
Wang J, Li YChen B,et al.A real-time deep learning-based system for colorectal polyp size estimation by white-light endoscopy: development and multicenter prospective validation[J].Endoscopy2024, 56(4):260-270.
[36]
Wang JWang ZChen M,et al.An interpretable artificial intelligence system for detecting risk factors of gastroesophageal variceal bleeding[J]. NPJ Digit Med20225(1):183.
[37]
Lebwohl BKastrinos FGlick M,et al.The impact of suboptimal bowel preparation on adenoma miss rates and the factors associated with early repeat colonoscopy[J].Gastrointest Endosc201173(6):1207-1214.
[38]
Lee JYPark JLee HJ,et al.Automatic assessment of bowel preparation by an artificial intelligence model and its clinical applicability[J].J Gastroenterol Hepatol202439(9):1917-1923.
[39]
Zhu YZhang DFWu HL,et al.Improving bowel preparation for colonoscopy with a smartphone application driven by artificial intelligence[J].NPJ Digit Med20236(1):41.
[40]
Yao LLi XWu Z,et al.Effect of artificial intelligence on novice-performed colonoscopy:a multicenter randomized controlled tandem study[J].Gastrointest Endosc202499(1):91-99.
[41]
Yamaguchi DShimoda RMiyahara K,et al.Impact of an artificial intelligence‐aided endoscopic diagnosis system on improving endoscopy quality for trainees in colonoscopy:prospective,randomized,multicenter study[J].Dig Endosc202436(1):40-48.
[42]
Biscaglia GCocomazzi FGentile M,et al.Real-time,computer-aided, detection-assisted colonoscopy eliminates differences in adenoma detection rate between trainee and experienced endoscopists[J].Endosc Int Open202210(5):E616-E621.
[43]
Chang PWNguyen DDKong N,et al.Impact of artificial intelligence-assisted colonoscopy on gastroenterology fellow performance:A pragmatic randomized controlled trial[J].Gastrointest Endosc2026103(5):1043-1051.
[44]
Troya JFitting DBrand M,et al.The influence of computer-aided polyp detection systems on reaction time for polyp detection and eye gaze[J].Endoscopy202254(10):1009-1014.
[45]
Vleugels JLAHassan CSenore C,et al.Diminutive polyps with advanced histologic features do not increase risk for metachronous advanced colon neoplasia[J].Gastroenterology2019156(3):623-634.e3.
[46]
Houwen BBSLHassan CCoupé VMH,et al.Definition of competence standards for optical diagnosis of diminutive colorectal polyps: European society of gastrointestinal endoscopy (ESGE) position statement[J].Endoscopy202254(1):88-99.
[47]
Hassan CMisawa MRizkala T,et al.Computer-aided diagnosis for leaving colorectal polyps in situ:a systematic review and meta-analysis [J].Ann Intern Med2024177(7):919-928.
[1] 黄楚曦, 吴卓. 人工智能在膀胱癌影像中的应用进展[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2026, 20(03): 241-247.
[2] 焦克凡, 李涛. 人工智能在腹腔镜外科腹部肿瘤手术中的应用进展与前景[J/OL]. 中华腔镜外科杂志(电子版), 2026, 19(02): 122-128.
[3] 石亚超, 魏六木, 东小鸽, 樊海宁, 侯立朝, 杜凯豪, 汪占金, 薛伟伟, 刘海刚, 王展. 肝癌多模式诊断研究进展与展望[J/OL]. 中华肝脏外科手术学电子杂志, 2026, 15(03): 326-336.
[4] 邓玉飞, 王志鑫, 娄珂, 张林轩, 马桂春, 港措. 影像组学在肝癌精准诊断、疗效评估及治疗方案决策优化中应用[J/OL]. 中华肝脏外科手术学电子杂志, 2026, 15(02): 172-180.
[5] 张忠, 王喆, 黄其日麦拉图, 齐岩松. 人工智能在肘关节骨关节炎诊疗中的应用进展[J/OL]. 中华肩肘外科电子杂志, 2026, 14(02): 109-114.
[6] 崔妍, 祖盼云, 宋宇, 谢克亮. 基于人工智能的线上案例平台在研究生重症医学教学中的应用[J/OL]. 中华重症医学电子杂志, 2026, 12(02): 172-179.
[7] 康欢, 高媛媛, 刘文雄. 丙泊酚-瑞芬太尼闭环反馈靶控输注系统对无痛胃肠镜检查患者体动反应及术野清晰度的优化作用[J/OL]. 中华消化病与影像杂志(电子版), 2026, 16(03): 274-277.
[8] 陈小坤, 杜顺达. 影像组学在肝细胞癌中的应用进展及挑战[J/OL]. 中华消化病与影像杂志(电子版), 2026, 16(02): 97-100.
[9] 张敏洁, 刘艳龙. 多模态影像学检查技术与AI放射组学在诊断输尿管肿瘤中的研究进展[J/OL]. 中华诊断学电子杂志, 2026, 14(02): 133-137.
[10] 王孝盼, 张克明, 杜明威, 雷文知, 廖万清, 潘炜华, 方文捷, 潘搏. 人工智能在军校学员皮肤病理教学中的应用与展望[J/OL]. 中华诊断学电子杂志, 2026, 14(02): 144-148.
[11] 梁怡凡, 牟婧宇, 吴雅婷, 邳靖陶, 陈乐, 武剑. 不同人工智能模型预测脑卒中不良预后诊断效能的荟萃分析[J/OL]. 中华脑血管病杂志(电子版), 2026, 20(03): 308-319.
[12] 张思远, 宋晓微, 王丽君, 范玉华, 武剑. 人工智能赋能脑小血管病的血流动力学评估:研究进展与临床转化[J/OL]. 中华脑血管病杂志(电子版), 2026, 20(03): 327-333.
[13] 徐林, 简讯, 刘道权, 易东, 鄢华. 人工智能在心脑血管内介入诊疗中的应用进展——从单一影像模式到多模态影像融合[J/OL]. 中华脑血管病杂志(电子版), 2026, 20(03): 334-341.
[14] 刘诗馨, 宋晓微, 武剑. 临床视角下人工智能赋能脑卒中康复的范式重塑[J/OL]. 中华脑血管病杂志(电子版), 2026, 20(03): 231-239.
[15] 孙庆利, 叶珊, 樊东升, 傅瑜. 人工智能在医工结合专业人才培养中的作用及其在医学教育中的应用[J/OL]. 中华脑血管病杂志(电子版), 2026, 20(02): 204-208.
阅读次数
全文


摘要


AI


AI小编
你好!我是《中华医学电子期刊资源库》AI小编,有什么可以帮您的吗?