切换至 "中华医学电子期刊资源库"

中华腔镜外科杂志(电子版) ›› 2025, Vol. 18 ›› Issue (03) : 172 -176. doi: 10.3877/cma.j.issn.1674-6899.2025.03.009

所属专题: 文献

综述

人工智能在子宫腺肌病微创治疗中的应用进展
赵婷, 易晓芳()   
  1. 200011 上海,复旦大学附属妇产医院
  • 收稿日期:2025-05-05 出版日期:2025-06-30
  • 通信作者: 易晓芳
  • 基金资助:
    国家自然科学基金(82301858); 上海市妇科疾病临床研究中心(22MC1940200)

The application progress of artificial intelligence in minimally invasive treatment of adenomyosis

Ting Zhao, Xiaofang Yi()   

  1. Obstetrics and Gynecology Hospital of Fudan University, 200011 Shanghai, China
  • Received:2025-05-05 Published:2025-06-30
  • Corresponding author: Xiaofang Yi
引用本文:

赵婷, 易晓芳. 人工智能在子宫腺肌病微创治疗中的应用进展[J/OL]. 中华腔镜外科杂志(电子版), 2025, 18(03): 172-176.

Ting Zhao, Xiaofang Yi. The application progress of artificial intelligence in minimally invasive treatment of adenomyosis[J/OL]. Chinese Journal of Laparoscopic Surgery(Electronic Edition), 2025, 18(03): 172-176.

本文全面综述了人工智能(artificial intelligence, AI)在子宫腺肌病微创治疗的"术前诊断-治疗决策-术中导航"全流程的应用现状;简要介绍了深度学习算法联合弹性超声对诊断子宫腺肌病的应用前景以及AI对道格拉斯窝封闭等特殊场景的前沿应用;同时也指出了面临的挑战,为进一步开展多学科研究、构建伦理共识与临床标准化应用提出了展望。AI赋能子宫腺肌病的微创治疗在术前精准评估、个性化治疗策略制定以及术中导航等方面表现出广阔应用前景,尚需前瞻性多中心研究验证其临床效益。

This paper comprehensively reviews the current application status of artificial intelligence (AI) in the entire process of "preoperative diagnosis - treatment decision-making - intraoperative navigation" for the minimally invasive treatment of adenomyosis. It briefly introduces the application prospects of deep learning algorithms combined with elastography in the diagnosis of adenomyosis, as well as the cutting-edge applications of AI in special scenarios such as the obliteration of the Douglas pouch. At the same time, it also points out the challenges faced and puts forward prospects for the further development of interdisciplinary research, the establishment of ethical consensus, and the standardized clinical application.

1
Capezzuoli T, Toscano F, Ceccaroni M, et al. Conservative surgical treatment for adenomyosis: New options for looking beyond uterus removal[J]. Best Pract Res Clin Obstet Gynaecol, 2024, 95:102507.
2
Andres MP, Borrelli GM, Abrão MS. Advances on minimally invasive approach for benign total hysterectomy: a systematic review[J]. F1000Res, 2017, 6:1295.
3
Ribeiro F, Ferreira H. Novel minimally invasive surgical approaches to endometriosis and adenomyosis: a comprehensive review[J]. J Clin Med, 2024, 13(22):6844.
4
Barat M, Dohan A, Kohi M, et al. Treatment of adenomyosis, abdominal wall endometriosis and uterine leiomyoma with interventional radiology: a review of current evidences[J]. Diagn Interv Imaging, 2024, 105(3):87-96.
5
Gracia M, de Guirior C, Valdés-Bango M, et al. Adenomyosis is an independent risk factor for complications in deep endometriosis laparoscopic surgery[J]. Scientific reports, 2022;12(1):7086.
6
Lin M, Lin L, Lin L, et al. A bibliometric analysis of the advance of artificial intelligence in medicine[J]. Front Med (Lausanne), 2025, 12:1504428.
7
Aydin S, Karabacak M, Vlachos V, et al. Large language models in patient education: a scoping review of applications in medicine[J]. Front Med (Lausanne), 2024, 11:1477898.
8
Oliveira JA, Eskandar K, Kar E, et al. Understanding AI′s role in endometriosis patient education and evaluating its information and accuracy: systematic review[J]. Jmir ai, 2024, 3:e64593.
9
Tellum T, Nygaard S, Lieng M. Noninvasive diagnosis of adenomyosis: a structured review and meta-analysis of diagnostic accuracy in imaging[J]. Journal of minimally invasive gynecology, 2020, 27(2):408-418.e3.
10
Tan S, Leonardi M, Lo G, et al. Role of ultrasonography in the diagnosis of endometriosis in infertile women: Ovarian endometrioma, deep endometriosis, and superficial endometriosis[J]. Best practice & research Clinical obstetrics & gynaecology, 2024, 92:102450.
11
Canis M, Gremeau AS, Bourdel N. Elusive adenomyosis: a plea for an international classification system to allow artificial intelligence approaches to reset our clinical management[J]. Fertility and sterility, 2018, 110(6):1039-1040.
12
Sahni NR, Carrus B. Artificial intelligence in U.S. Health care delivery[J]. N Engl J Med, 2023, 389(4):348-358.
13
Torres-Velázquez M, Chen WJ, Li X, et al. Application and construction of deep learning networks in medical imaging[J]. IEEE Trans Radiat Plasma Med Sci, 2021, 5(2):137-159.
14
Rana M, Bhushan M. Machine learning and deep learning approach for medical image analysis: diagnosis to detection[J]. Multimed Tools Appl, 2022:1-39.
15
Zhao Q, Yang T, Xu C, et al. Automatic diagnosis for adenomyosis in ultrasound images by deep neural networks[J]. Eur J Obstet Gynecol Reprod Biol, 2024, 301:128-134.
16
Raimondo D, Raffone A, Aru AC, et al. Application of deep learning model in the sonographic diagnosis of uterine adenomyosis[J]. Int J Environ Res Public Health, 2023, 20(3):1724.
17
Guiot J, Vaidyanathan A, Deprez L, et al. A review in radiomics: making personalized medicine a reality via routine imaging[J]. Med Res Rev, 2022, 42(1):426-440.
18
Maniaci A, Lavalle S, Gagliano C, et al. The integration of radiomics and artificial intelligence in modern medicine[J]. Life (Basel), 2024, 14(10):1248.
19
Burla L, Sartoretti E, Mannil M, et al. MRI-based radiomics as a promising noninvasive diagnostic technique for adenomyosis[J]. Journal of clinical medicine, 2024, 13(8):2344.
20
Brunelli AC, Brito LGO, Moro FAS, et al. Ultrasound elastography for the diagnosis of endometriosis and adenomyosis: a systematic review with meta-analysis[J]. Ultrasound Med Biol, 2023, 49(3):699-709.
21
Xue LY, Jiang ZY, Fu TT, et al. Transfer learning radiomics based on multimodal ultrasound imaging for staging liver fibrosis[J]. Eur Radiol, 2020, 30(5):2973-2983.
22
Dessouky R, Gamil SA, Nada MG, et al. Management of uterine adenomyosis: current trends and uterine artery embolization as a potential alternative to hysterectomy[J]. Insights Imaging, 2019, 10(1):48.
23
Jin W, Wang S, Wang T, et al. Multi-machine learning model based on habitat subregions for outcome prediction in adenomyosis treated by uterine artery embolization[J]. Acad Radiol, 2024,31(12):4985-4995.
24
Bahutair SN, Alhubaishi LY. High-intensity focused ultrasound in adenomyosis treatment: insights on safety, efficacy, and reproductive prospects[J]. Women′s health (London, England), 2024,20:17455057241295593.
25
Li Z, Zhang J, Song Y, et al. Utilization of radiomics to predict long-term outcome of magnetic resonance-guided focused ultrasound ablation therapy in adenomyosis[J]. Eur Radiol, 2021, 31(1):392-402.
26
Ying J, Jing X, Gao F, et al. Prediction of ablation rate for high-intensity focused ultrasound therapy of adenomyosis in MR images based on multi-model fusion[J]. J Imaging Inform Med, 2024,37(4):1579-1590.
27
Reid S, Condous G. Transvaginal sonographic sliding sign: accurate prediction of pouch of Douglas obliteration[J]. Ultrasound Obstet Gynecol, 2013, 41(6):605-607.
28
Manganaro L, Vittori G, Vinci V, et al. Beyond laparoscopy: 3-T magnetic resonance imaging in the evaluation of posterior cul-de-sac obliteration[J]. Magn Reson Imaging. Dec, 2012,30(10):1432-1438.
29
Maicas G, Leonardi M, Avery J, et al. Deep learning to diagnose pouch of Douglas obliteration with ultrasound sliding sign[J]. Reproduction & fertility, 2021, 2(4):236-243.
30
Wang H, Butler D, Zhang Y, et al. Human-AI collaborative multi-modal multi-rater learning for endometriosis diagnosis[J]. Phys Med Biol, 2024, 70(1).
31
Saremi A, Bahrami H, Salehian P, et al. Treatment of adenomyomectomy in women with severe uterine adenomyosis using a novel technique[J]. Reproductive biomedicine online, 2014, 28(6):753-760.
32
Bourdel N, Chauvet P, Calvet L, et al. Use of augmented reality in gynecologic surgery to visualize adenomyomas[J]. Journal of minimally invasive gynecology, 2019,26(6):1177-1180.
33
Levin I, Rapoport Ferman J, Bar O, et al. Introducing surgical intelligence in gynecology: Automated identification of key steps in hysterectomy[J]. Int J Gynaecol Obstet, 2024, 166(3):1273-1278.
34
Chu Z, Jia L, Dai J, et al. Effects of different treatment methods on clinical efficacy and fertility outcomes of patients with adenomyosis[J]. J Ovarian Res, 2024, 17(1):16.
35
Sadeghi-Goughari M, Rajabzadeh H, Han JW, et al. Artificial intelligence-assisted ultrasound-guided focused ultrasound therapy: a feasibility study[J]. Int J Hyperthermia, 2023, 40(1):2260127.
36
Zimmermann C, Michelmann A, Daniel Y, et al. Application of deep learning for real-time ablation zone measurement in ultrasound imaging[J]. Cancers (Basel), 2024, 16(9):1700.
37
Xiong Y, Zheng Y, Long W, et al. Study on microwave ablation temperature prediction model based on grayscale ultrasound texture and machine learning[J]. PLoS One, 2024, 19(9):e0308968.
38
Zhang S, Wu S, Shang S, et al. Detection and monitoring of thermal lesions induced by microwave ablation using ultrasound imaging and convolutional neural networks[J]. IEEE J Biomed Health Inform, 2020, 24(4):965-973.
39
Luan S, Ji Y, Liu Y, et al. AI-powered ultrasonic thermometry for HIFU therapy in deep organ[J]. Ultrason Sonochem, 2024, 111:107154.
40
Ning G, Zhang X, Zhang Q, et al. Real-time and multimodality image-guided intelligent HIFU therapy for uterine fibroid[J]. Theranostics2020, 10(10):4676-4693.
41
Khattar H, Goel R, Kumar P. Artificial intelligence in gynaecological malignancies: perspectives of a clinical oncologist[J]. Cureus, 2023,15(9):e45660.
42
Bang CS, Lee JJ, Baik GH. Artificial intelligence for the prediction of helicobacter pylori infection in endoscopic images: systematic review and meta-analysis of diagnostic test accuracy[J]. J Med Internet Res, 2020, 22(9):e21983.
43
Ogawa M, Miyoshi N, Tamura S, et al. Ergonomic and sustainable posture for gynecological laparoscopic surgeons determined based on images analyzed using artificial intelligence[J]. Biomed Rep, 2024,21(6):174.
44
Chapron C, Vannuccini S, Santulli P, et al. Diagnosing adenomyosis: an integrated clinical and imaging approach[J]. Human reproduction update, 2020,26(3):392-411.
45
Leaf MC, Musselman K, Wang KC. Cutting-edge care: unleashing artificial intelligence′s potential in gynecologic surgery[J]. Curr Opin Obstet Gynecol, 2024, 36(4):255-259.
46
Shim JI, Jo EH, Kim M, et al. A comparison of surgical outcomes between robot and laparoscopy-assisted adenomyomectomy[J]. Medicine (Baltimore), 2019, 98(18):e15466.
47
Chung YJ, Kang SY, Choi MR, et al. Robot-assisted laparoscopic adenomyomectomy for patients who want to preserve fertility[J]. Yonsei Med J, 2016, 57(6):1531-1534.
48
Hijazi A, Chung YJ, Sinan N, et al. A novel technique for myometrial defect closure after robot-assisted laparoscopic adenomyomectomy: a retrospective cohort study[J]. Taiwan J Obstet Gynecol, 2022,61(1):75-79.
49
Zhang C, Hallbeck MS, Salehinejad H, et sl. The integration of artificial intelligence in robotic surgery: a narrative review[J]. Surgery, 2024, 176(3):552-557.
50
Cetera GA-O, Tozzi AA-OX, Chiappa V, et al. Artificial intelligence in the management of women with endometriosis and adenomyosis: can machines ever be worse than humans?[J]. J Clin Med, 2024, 13(10):2950.
51
Liu BHM, Lin Y, Long X, et al. Utilizing AI for the identification and validation of novel therapeutic targets and repurposed drugs for endometriosis[J]. Adv Sci (Weinh), 2025, 12(5):e2406565.
[1] 周欣, 梁豪进, 邓振宇, 肖菊花, 周小军. 基于人工智能技术评价江西省孕11~13+6周产前超声筛查质量现状及提出能力提升对策[J/OL]. 中华医学超声杂志(电子版), 2025, 22(09): 850-857.
[2] 张振奇, 齐艺涵, 王璐, 胡紫玥, 李婷婷, 卢漫. 大语言模型DeepSeek-R1在甲状腺超声报告质量控制中的初步应用[J/OL]. 中华医学超声杂志(电子版), 2025, 22(09): 832-837.
[3] 江瑶, 蒋程, 余翔, 谭莹, 温昕, 温慧莹, 彭桂艳, 李胜利. 基于注意力机制改进的子宫解剖结构检测与分割多任务模型的性能评估[J/OL]. 中华医学超声杂志(电子版), 2025, 22(08): 703-710.
[4] 陈明朗, 许凯, 黄稚熙, 梁博诚, 贺杰, 黄海珊, 马微波, 谭莹, 邹志英, 刘晓棠, 彭桂艳, 陈家希, 钟晓红. MobileNetV4:面向产前超声的主动脉弓分支异常智能诊断研究[J/OL]. 中华医学超声杂志(电子版), 2025, 22(08): 711-720.
[5] 杨丽仙, 黄稚熙, 梁博诚, 欧阳淑媛, 陈明朗, 赵英丽, 马薇波, 缪敬, 王磊, 袁鹰. 基于产前时序超声数据的新生儿出生体重智能预测[J/OL]. 中华医学超声杂志(电子版), 2025, 22(08): 721-732.
[6] 刘晴晴, 俞劲, 徐玮泽, 张志伟, 潘晓华, 舒强, 叶菁菁. OBICnet图像分类模型在小儿先天性心脏病超声筛查中的应用价值[J/OL]. 中华医学超声杂志(电子版), 2025, 22(08): 754-760.
[7] 梅昊楠, 杨瑞, 刘修恒. 人工智能辅助病理学图像分析在前列腺癌诊断中的研究进展[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2026, 20(01): 1-7.
[8] 丁小博, 陈洁, 王艳波. 人工智能在泌尿系结石诊治中的应用进展[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2026, 20(01): 15-21.
[9] 樊帆, 黄浩, 付莉丽, 周春梅, 马雪霞, 黄海. 下尿路功能障碍患者智能化尿控标准病房的建设及成效[J/OL]. 中华腔镜泌尿外科杂志(电子版), 2026, 20(01): 44-50.
[10] 嵇振岭. 疝与腹壁外科补片研发国外进展[J/OL]. 中华疝和腹壁外科杂志(电子版), 2025, 19(06): 614-622.
[11] 唐玥, 陈家璐, 覃德龙, 李宗龙, 汤朝晖, 全志伟. 基于AI的多模态影像在肝癌诊治中应用及面临挑战[J/OL]. 中华肝脏外科手术学电子杂志, 2026, 15(01): 4-9.
[12] 戴宗伯, 张城硕, 郭庭维, 何知远, 赵昊宇, 张宇慈, 张佳林. 基于MRI影像组学机器学习构建肝细胞癌微血管侵犯预测模型[J/OL]. 中华肝脏外科手术学电子杂志, 2026, 15(01): 36-44.
[13] 谢钰嵘, 唐流康, 陈明政, 王伟利, 缪文学, 谢峰. 人工智能在肝胆外科临床教学中的应用[J/OL]. 中华肝脏外科手术学电子杂志, 2025, 14(06): 822-827.
[14] 荣锦, 骆明星, 王禹, 刘婷婷, 张宏斌. 全膝关节置换术后慢性疼痛影响因素的多种模型预测性能比较[J/OL]. 中华老年骨科与康复电子杂志, 2025, 11(06): 337-344.
[15] 常芳媛, 乔春梅, 王欣, 王博冉, 赵梓孚, 李春歌, 王晓磊. 多模态超声及人工智能在细菌性和非细菌性关节炎中应用的研究进展[J/OL]. 中华临床医师杂志(电子版), 2025, 19(08): 606-611.
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?