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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

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.

1
Yu J, Huang C, Sun Y, et al. Effect of laparoscopic vs open distal gastrectomy on 3-year disease-free survival in patients with locally advanced gastric cancer: the CLASS-01 randomized clinical trial [J]. JAMA, 2019, 321 (20): 1983-1992.
2
Stevenson AR, Solomon MJ, Lumley JW, et al. Effect of laparoscopic-assisted resection vs open resection on pathological outcomes in rectal cancer: the ALaCaRT randomized clinical trial[J]. JAMA, 2015, 314(13):1356-1363.
3
Leibetseder A, Petscharnig S, Primus M J, et al. LapGyn4: a dataset for 4 automatic content analysis problems in the domain of laparoscopic gynecology[C]// Proceedings of the 9th ACM Multimedia Systems Conference (MMSys 2018), Amsterdam, the Netherlands, 12-15 Jun 2018. New York: Association for Computing Machinery, 2018: 357-362. DOI: 10.5281/zenodo.1219280.
4
Zhou R, Wang D, Zhang H, et al. Vision techniques for anatomical structures in laparoscopic surgery: a comprehensive review[J]. Front Surg, 2025, 12: 1557153.
5
Saeidi H, Opfermann JD, Kam M, et al. Autonomous robotic laparoscopic surgery for intestinal anastomosis[J]. Sci Robot, 2022, 7 (62): eabj2908.
6
Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis[J]. Med Image Anal, 2017, 42: 60-88.
7
Kojima S, Kitaguchi D, Igaki T, et al. Deep-learning-based semantic segmentation of autonomic nerves from laparoscopic images of colorectal surgery: an experimental pilot study[J]. Int J Surg, 2023, 109 (4): 813-820.
8
Narihiro S, Kitaguchi D, Hasegawa H, et al. Deep learning-based real-time ureter identification in laparoscopic colorectal surgery[J]. Dis Colon Rectum, 2024, 67 (10): e1596-e1599.
9
Madani A, Namazi B, Altieri M S, et al. Artificial intelligence for intraoperative guidance: using semantic segmentation to identify surgical anatomy during laparoscopic cholecystectomy[J]. Ann Surg, 2022, 276 (2): 363-369.
10
马周,易跃雄,陈雨柔,等. 基于深度学习YOLOv5网络的机器人辅助单孔腹腔镜子宫切除术实时解剖标志指示系统[J]. 武汉大学学报(医学版), 2024, 45(2):152-158.
11
Madad Zadeh S, Francois T, Calvet L, et al. SurgAI: deep learning for computerized laparoscopic image understanding in gynaecology[J]. Surg Endosc, 2020, 34 (12): 5377-5383.
12
Kitaguchi D, Lee Y, Hayashi K, et al. Development and validation of a model for laparoscopic colorectal surgical instrument recognition using convolutional neural network-based instance segmentation and videos of laparoscopic procedures[J]. JAMA Netw Open, 2022, 5 (8): e2226265.
13
Nwoye CI, Padoy N. SurgiTrack: fine-grained multi-class multi-tool tracking in surgical videos[J]. Med Image Anal, 2025, 101: 103438.
14
Twinanda AP, Shehata S, Mutter D, et al. EndoNet: a deep architecture for recognition tasks on laparoscopic videos [J]. IEEE Trans Med Imaging, 2017, 36 (1): 86-97.
15
Czempiel T, Paschali M, Keicher M, et al. Tecno: Surgical phase recognition with multi-stage temporal convolutional networks[C]. Medical Image Computing and Computer Assisted Intervention-MICCAI 2020: 23rd International Conference, Lima, Peru, October 4-8, 2020, Proceedings, Part III 23. Cham: Springer International Publishing, 2020: 343-352.
16
Horita K, Hida K, Itatani Y, et al. Real-time detection of active bleeding in laparoscopic colectomy using artificial intelligence[J]. Surg Endosc, 2024, 38 (6): 3461-3469.
17
Sunakawa T, Kitaguchi D, Kobayashi S, et al. Deep learning-based automatic bleeding recognition during liver resection in laparoscopic hepatectomy[J]. Surg Endosc, 2024, 38 (12): 7656-7662.
18
Zhang J, Huang J, Jin S, et al. Vision-language models for vision tasks: a survey[J]. IEEE Trans Pattern Anal Mach Intell, 2024, 46 (8): 5625-5644.
19
Yu G, Sun K, Xu C, et al. Accurate recognition of colorectal cancer with semi-supervised deep learning on pathological images[J]. Nat Commun, 2021, 12 (1): 6311.
20
Messaoudi H, Abbas M, Badic B, et al. Automatic future remnant segmentation in liver resection planning[J/OL]. Int J Comput Assist Radiol Surg, 2025, 20(5):837-845.
21
Nassar AHM, Hodson J, Ng HJ, et al. Predicting the difficult laparoscopic cholecystectomy: development and validation of a pre-operative risk score using an objective operative difficulty grading system[J]. Surg Endosc, 2020, 34(10):4549-4561.
22
Mascagni P, et al. Artificial intelligence for surgical safety: automatic assessment of the critical view of safety in laparoscopic cholecystectomy using deep learning[J]. Ann Surg, 2022, 275 (5): 955-961.
23
Chung P, Fong CT, Walters AM, et al. Large language model capabilities in perioperative risk prediction and prognostication[J]. JAMA Surg, 2024, 159 (8): 928-937.
24
Bihorac A, Ozrazgat-Baslanti T, Ebadi A, et al. MySurgeryRisk: development and validation of a machine-learning risk algorithm for major complications and death after surgery[J]. Ann Surg, 2019, 269 (4): 652-662.
25
Taha-Mehlitz S, Wentzler L, Angehrn F, et al. Machine-learning-based preoperative analytics for the prediction of anastomotic leakage in colorectal surgery: a Swiss pilot study[J]. Surg Endosc, 2024, 38 (7): 3672-3683.
26
Kiyasseh D, Ma R, Haque TF, et al. A vision transformer for decoding surgeon activity from surgical videos[J]. Nat Biomed Eng, 2023, 7 (6): 780-796.
27
祁宝莲,钟坤华,陈芋文. 基于卷积神经网络的半监督手术视频流程识别[J]. 计算机科学2020, 47(z1):172-175.
28
Gao X, Jin Y, Long Y, et al. Trans-svnet: Accurate phase recognition from surgical videos via hybrid embedding aggregation transformer[C]//Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th International Conference, Strasbourg, France, September 27-October 1, 2021, Proceedings, Part IV 24. Cham: Springer International Publishing, 2021: 593-603.
29
Zhao Y, Li Y, Xing L, et al. The performance of artificial intelligence in cervical colposcopy: a retrospective data analysis [J]. J Oncol, 2022, 2022: 4370851.
30
Haenssle HA, Fink C, Schneiderbauer R, et al. Man against machine: diagnostic performance of a deep-learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists[J]. Ann Oncol, 2018, 29 (8): 1836-1842.
31
Kletz S, Schoeffmann K, Husslein H. Learning the representation of instrument images in laparoscopy videos[J]. Healthc Technol Lett, 2019, 6 (6): 197-203.
32
花苏榕,王智弘,王晶,等. 深度学习技术识别纱布在腹腔镜胰腺手术中的应用价值[J]. 中华消化外科杂志2021, 20(12):1324-1330.
33
Tashtoush A, Wang Y, Khasawneh MT, et al. Real-time object segmentation for laparoscopic cholecystectomy using YOLOv8[J]. Neural Computing and Applications, 2025, 37(4): 2697-2710. DOI: 10.1007/s00521-024-10713-1.
34
Maier-Hein L, Vedula SS, Speidel S, et al. Surgical data science for next-generation interventions[J]. Nat Biomed Eng, 2017, 1 (9): 691-696.
35
Kitaguchi D, Takeshita N, Matsuzaki H, et al. Development and validation of a 3-dimensional convolutional neural network for automatic surgical skill assessment based on spatiotemporal video analysis[J]. JAMA Netw Open, 2021, 4(8):e2120786.
36
Igaki T, Kitaguchi D, Matsuzaki H, et al. Automatic surgical skill assessment system based on concordance of standardized surgical field development using artificial intelligence[J]. JAMA Surg, 2023, 158 (8): e231131.
37
Nguyen XA, Ljuhar D, Pacilli M, et al. Surgical skill levels: classification and analysis using deep neural network model and motion signals[J]. Comput Methods Programs Biomed, 2019, 177:1-8.
38
Brown JD, O Brien CE, Leung SC, et al. Using contact forces and robot arm accelerations to automatically rate surgeon skill at peg transfer[J]. IEEE Trans Biomed Eng, 2017, 64(9):2263-2275.
39
Shafiei SB, Shadpour S, Mohler JL, et al. Classification of subtask types and skill levels in robot-assisted surgery using EEG, eye-tracking, and machine learning[J]. Surg Endosc, 2024, 38 (9): 5137-5147.
40
何晓芳,陈洁,李秋萍,等. 基于机器学习算法的腹腔镜结直肠癌根治术后肠梗阻预测模型[J]. 机器人外科学杂志(中英文), 2024, 5(6):1205-1210.
41
李济振,朱恒立,付晴安,等. 基于机器学习的LPD术后临床相关胃排空延迟风险预测模型的构建[J]. 中华肝胆外科杂志2025, 31(2):101-106.
42
胡晔,黄磊,吴慧,等. 基于机器学习的老年患者腹腔镜胆囊切除术后感染预测模型的构建[J]. 临床药物治疗杂志2024, 22(6):66-70.
43
王晨,刘蕾,王蕾,等. 胸腔镜单肺叶切除术患者术后住院时间延长预测模型的构建[J]. 中华麻醉学杂志2022, 42(10):1187-1191.
44
罗治文,陈晓,张业繁,等. 机器学习算法和COX列线图在肝细胞癌术后生存预测中的应用价值[J]. 中华消化外科杂志2020, 19(2):166-178.
45
Li C, Qiao G, Li J, et al. An ultrasonic-based radiomics nomogram for distinguishing between benign and malignant solid renal masses[J]. Front Oncol, 2022, 12: 847805.
46
Kunze KN, Polce EM, Clapp I, et al. Machine learning algorithms predict functional improvement after hip arthroscopy for femoroacetabular impingement syndrome in athletes[J]. J Bone Joint Surg Am, 2021, 103(12):1055-1062.
47
Arai J, Aoki T, Sato M, et al. Machine-learning-based personalized prediction of gastric cancer incidence using the endoscopic and histologic findings at the initial endoscopy[J]. Gastrointest Endosc, 2022, 95(5): 864-872.
48
Hirsch R, Caron M, Cohen R, et al. Self-supervised learning for endoscopic video analysis[C]//International Conference on Medical Image Computing and Computer-Assisted Intervention. Cham: Springer Nature Switzerland, 2023: 569-578.
49
Ramesh S, Srivastav V, Alapatt D, et al. Dissecting self-supervised learning methods for surgical computer vision[J]. Med Image Anal, 2023, 88: 102844.
50
Tajbakhsh N, Jeyaseelan L, Li Q, et al. Embracing imperfect datasets: a review of deep learning solutions for medical image segmentation[J]. Med Image Anal, 2020, 63:101693.
51
Shahzad H, Veliky C, Le H, et al. Preserving privacy in big data research: the role of federated learning in spine surgery[J]. Eur Spine J, 2024, 33: 4076-4081.
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