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外科研究与新技术（中英文）

外科研究与新技术（中英文） >

2025 , Vol. 14 >Issue 4: 368 - 375

DOI: https://doi.org/10.3969/j.issn.2095-378X.2025.04.015

综述

人工智能在结直肠内镜诊疗中的应用研究

  • 陶冶 ,
  • 任一凡 ,
  • 许树长 ,
  • 孙会会
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  • 1.南通大学医学院,江苏 南通 226001;
    2.同济大学附属同济医院消化内科,上海 200065;
    3.同济大学医学院,上海 200092
陶冶（2004—）,男,本科生在读

收稿日期: 2025-04-15

基金资助

同济大学第十九期实验教学改革项目（1508104012）; 上海市2024年度“科技创新行动计划”科普专项（24DZ2300200）; 上海市经信委,上海市创新医疗器械应用示范项目（23SHS03100）; 上海市同济医院临床“五新”创新研发项目（ITJ（ZD）2409）; 南通大学医学院国家级大学生创新创业训练项目（202410304036Z）

收起

Application of artificial intelligence in endoscopy for colorectal diagnosis and treatment

  • TAO Ye ,
  • REN Yifan ,
  • XU Shuchang ,
  • SUN Huihui
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  • 1. School of Medicine, Nantong University, Nantong 226001, Jiangsu, China;
    2. Department of Gastroenterology, Tongji Hospital, Tongji University, Shanghai 200065, China;
    3. School of Medicine, Tongji University, Shanghai 200092, China

Received date: 2025-04-15

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摘要

结直肠癌是全球高发且致死率较高的恶性肿瘤,其早期筛查与精准诊疗的漏诊率高且成本昂贵。目前人工智能（AI）通过深度学习和机器学习技术,在内镜诊疗中展现出显著优势。在结直肠息肉及腺瘤检测中,AI辅助系统显著提高腺瘤检出率,降低漏诊率,并优化质量控制与撤镜效率。AI结合染色质分析、影像组学及病理学技术,提升早期病变检测灵敏度与特异度,辅助预测淋巴转移及病理分期。针对炎症性肠病,AI精准评估溃疡性结肠炎严重程度,识别克罗恩病狭窄与溃疡,其性能优于医生主观评估。在消化道内镜黏膜剥离术中,AI可实现手术流程的智能识别及标本自动测绘,提升手术安全性与效率。尽管AI可显著提高诊疗效率,但仍需解决数据异质性、泛化性不足等问题。未来,结合高质量数据与算法优化,AI将进一步推动结直肠疾病诊疗的精准化与智能化。

关键词： 人工智能; 结直肠癌; 内镜; 息肉; 炎症性肠病

本文引用格式

陶冶 , 任一凡 , 许树长 , 孙会会 . 人工智能在结直肠内镜诊疗中的应用研究[J]. 外科研究与新技术（中英文）, 2025 , 14(4) : 368 -375 . DOI: 10.3969/j.issn.2095-378X.2025.04.015

Abstract

Colorectal cancer represents a globally prevalent malignancy with significant mortality, where current challenges persist in suboptimal sensitivity of early screening modalities and prohibitive costs of precision diagnostics. Artificial intelligence （AI） leveraging deep learning and machine learning architectures have demonstrated transformative potential in endoscopic interventions. In colorectal polyp characterization and adenoma detection, AI-augmented systems significantly enhance adenoma detection rates, reduce miss rates, and optimize quality assurance protocols during colonoscope withdrawal phases. The integration of chromatin structural profiling, radiomic feature extraction, and histopathological analytics enables AI systems to improve diagnostic sensitivity/specificity for premalignant lesions while facilitating lymphatic invasion assessment and TNM staging predictions. Within inflammatory bowel disease management, AI-driven computational models achieve superior quantitation of endoscopic severity indices in ulcerative colitis and demonstrate enhanced discriminative capacity for stricturing phenotypes and penetrating lesions in Crohn's disease compared to conventional clinician evaluation. For advanced therapeutic endoscopy, AI-powered computer vision systems enable real-time procedural phase recognition and automated specimen margin mapping during endoscopic submucosal dissection, thereby improving en bloc resection rates and operative safety profiles. Notwithstanding these advancements, persistent limitations including training dataset heterogeneity and insufficient generalizability. Future development trajectories involving high-quality data and algorithmic optimization are anticipated to drive paradigm shifts toward AI-enabled precision coloproctology.

Key words： Artificial intelligence; Colorectal cancer; Endoscopy; Polyps; Inflammatory bowel disease

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