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中华腔镜外科杂志(电子版) ›› 2025, Vol. 18 ›› Issue (03) : 177 -183. doi: 10.3877/cma.j.issn.1674-6899.2025.03.010

所属专题: 文献

综述

人工智能驱动的腔镜外科发展:研究进展与未来趋势
施薇薇1,2, 楼微华1,2, 狄文1,2, 严斌1,2, 张楠1,2, 王酉1,2,()   
  1. 1200127 上海交通大学医学院附属仁济医院妇产科
    2200127 上海市妇科肿瘤重点实验室
  • 收稿日期:2025-05-06 出版日期:2025-06-30
  • 通信作者: 王酉

Artificial intelligence-driven evolution in laparoscopic surgery: advances and future trends

Weiwei Shi1,2, Weihua Lou1,2, Wen Di1,2, Bin Yan1,2, Nan Zhang1,2, You Wang1,2,()   

  1. 1Department of Obstetrics and Gynecology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, 200127, China
    2Shanghai Key Laboratory of Gynecologic Oncology, 200127, China
  • Received:2025-05-06 Published:2025-06-30
  • Corresponding author: You Wang
引用本文:

施薇薇, 楼微华, 狄文, 严斌, 张楠, 王酉. 人工智能驱动的腔镜外科发展:研究进展与未来趋势[J/OL]. 中华腔镜外科杂志(电子版), 2025, 18(03): 177-183.

Weiwei Shi, Weihua Lou, Wen Di, Bin Yan, Nan Zhang, You Wang. Artificial intelligence-driven evolution in laparoscopic surgery: advances and future trends[J/OL]. Chinese Journal of Laparoscopic Surgery(Electronic Edition), 2025, 18(03): 177-183.

腔镜手术作为现代外科的重要组成部分,虽具备微创优势,但也面临术野受限、操作复杂及学习曲线陡峭等挑战。人工智能(artificial intelligence, AI),特别是机器学习与深度学习技术,为克服这些挑战、提升手术质量提供了革命性工具。本综述旨在系统梳理AI在腹腔镜外科的应用现状、关键技术、面临挑战及未来趋势,重点关注其如何赋能手术全流程以提升精准性、安全性、效率与标准化、培训效果及个体化水平。在提升精准性方面,AI通过自动化术前影像分析优化手术规划,并在术中实时识别关键解剖结构与手术目标,辅助精准导航与操作。AI不仅能在术前量化评估手术风险与难度,还能在术中实时监测并预警出血等高风险事件,辅助识别并保护重要结构,检测手术异物,并在术后早期预测并发症发生风险,构筑智能安全防线。在促进效率与标准化方面,AI可自动化手术规划流程、识别手术阶段以优化工作流程,并建立客观、标准化的手术质量评估体系,减少主观变异。针对外科培训,AI通过分析手术视频、器械运动及术者生理信号,实现客观技能量化评估,并有望提供个性化反馈,加速学习曲线,革新传统培训模式。最后,AI通过深度挖掘多源临床与影像数据,实现个体化风险分层、生存/功能预后预测以及风险驱动式的随访管理,推动精准医疗在外科领域的落地。尽管AI在腔镜手术中展现出卓越潜力,但仍面临数据隐私、跨中心泛化、模型可解释性及临床集成等挑战。未来研究需加强多模态数据共享与联邦学习、可解释模型设计,以及与机器人系统的深度融合,推动腔镜手术向更智能、安全、高效的方向发展,为精准外科奠定技术基础。

Laparoscopic surgery, despite its minimally invasive benefits, faces challenges like limited visualization and complexity. Artificial intelligence (AI), particularly machine learning and deep learning, offers revolutionary tools to enhance surgical quality. This review surveys AI applications across the laparoscopic surgical workflow, focusing on its power to improve precision, safety, efficiency/standardization, training, and personalization. AI enhances precision by optimizing surgical planning and guiding intraoperative navigation through real-time anatomical recognition. It bolsters safety via preoperative risk assessment, intraoperative hazard warnings (e.g., hemorrhage), vital structure protection, and postoperative complication prediction. AI promotes efficiency and standardization by automating planning, recognizing surgical phases for workflow optimization, and enabling objective quality assessment. For training, AI provides objective skill evaluation using video, kinematic, and physiological data, potentially accelerating learning curves with personalized feedback. Furthermore, AI facilitates personalization through risk stratification, outcome prediction, and risk-driven follow-up, advancing precision surgery.Despite the remarkable potential demonstrated by AI in laparoscopic surgery, challenges remain, including data privacy, cross-center generalization, model interpretability, and clinical integration. Future research should focus on enhancing multimodal data sharing and federated learning, the design of explainable models, and deep integration with robotic systems, to propel laparoscopic surgery towards greater intelligence, safety, and efficiency, thereby laying the technological foundation for precision surgery.

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