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人工智能与子宫肌瘤：诊断和治疗的有效组合

Artificial Intelligence and Uterine Fibroids: A Useful Combination for Diagnosis and Treatment.

作者信息

Tinelli Andrea, Morciano Andrea, Sparic Radmila, Hatirnaz Safak, Malgieri Lorenzo E, Malvasi Antonio, D'Amato Antonio, Baldini Giorgio Maria, Pecorella Giovanni

机构信息

Department of Obstetrics and Gynecology, CERICSAL [CEntro di RIcerca Clinico SALentino], Veris delli Ponti Hospital, 73020 Scorrano, Lecce, Italy.

Department of Obstetrics and Gynecology, Cardinal Panico Hospital, 73039 Tricase, Lecce, Italy.

出版信息

J Clin Med. 2025 May 15;14(10):3454. doi: 10.3390/jcm14103454.

DOI:10.3390/jcm14103454
PMID:40429449
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12112542/
Abstract

This manuscript examines the role of artificial intelligence (AI) in the diagnosis and treatment of uterine fibroids and uterine sarcomas, offering a comprehensive assessment of AI-supported diagnostic and therapeutic techniques. Through the use of radiomics, machine learning, and deep neural network models, AI shows promise in identifying benign and malignant uterine lesions, directing therapeutic decisions, and improving diagnostic accuracy. It also demonstrates significant capabilities in the timely detection of fibroids. Additionally, AI improves surgical precision, real-time structure detection, and patient outcomes by transforming surgical techniques such as myomectomy, robot-assisted laparoscopic surgery, and High-Intensity Focused Ultrasound (HIFU) ablation. By helping to forecast treatment outcomes and monitor progress during procedures like uterine fibroid embolization, AI also offers a fresh and fascinating perspective for improving the clinical management of these conditions. This review critically assesses the current literature, identifies the advantages and limitations of various AI approaches, and provides future directions for research and clinical implementation.

摘要

本手稿探讨了人工智能（AI）在子宫肌瘤和子宫肉瘤诊断与治疗中的作用，对人工智能支持的诊断和治疗技术进行了全面评估。通过使用放射组学、机器学习和深度神经网络模型，人工智能在识别子宫良性和恶性病变、指导治疗决策以及提高诊断准确性方面显示出前景。它在及时检测子宫肌瘤方面也展现出显著能力。此外，人工智能通过变革诸如子宫肌瘤切除术、机器人辅助腹腔镜手术和高强度聚焦超声（HIFU）消融等手术技术，提高了手术精度、实时结构检测能力和患者治疗效果。通过帮助预测子宫纤维瘤栓塞等手术过程中的治疗结果并监测进展情况，人工智能还为改善这些病症的临床管理提供了全新且引人入胜的视角。本综述批判性地评估了当前文献，确定了各种人工智能方法的优缺点，并为研究和临床应用提供了未来方向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44bf/12112542/3e16e5a10c49/jcm-14-03454-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44bf/12112542/d1fe50a33f69/jcm-14-03454-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44bf/12112542/a63f03ac02ce/jcm-14-03454-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44bf/12112542/e0a66a6714c4/jcm-14-03454-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44bf/12112542/9817dcfca760/jcm-14-03454-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44bf/12112542/e26081c00164/jcm-14-03454-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44bf/12112542/896511d15b48/jcm-14-03454-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44bf/12112542/3e16e5a10c49/jcm-14-03454-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44bf/12112542/d1fe50a33f69/jcm-14-03454-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44bf/12112542/a63f03ac02ce/jcm-14-03454-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44bf/12112542/e0a66a6714c4/jcm-14-03454-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44bf/12112542/9817dcfca760/jcm-14-03454-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44bf/12112542/e26081c00164/jcm-14-03454-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44bf/12112542/896511d15b48/jcm-14-03454-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/44bf/12112542/3e16e5a10c49/jcm-14-03454-g007.jpg

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本文引用的文献

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Int J Hyperthermia. 2025 Dec;42(1):2473754. doi: 10.1080/02656736.2025.2473754. Epub 2025 Mar 23.
2
Application of artificial intelligence to ultrasound imaging for benign gynecological disorders: systematic review.人工智能在良性妇科疾病超声成像中的应用：系统评价
Ultrasound Obstet Gynecol. 2025 Mar;65(3):295-302. doi: 10.1002/uog.29171. Epub 2025 Jan 31.
3
Multimodal MRI radiomics-based stacking ensemble learning model with automatic segmentation for prognostic prediction of HIFU ablation of uterine fibroids: a multicenter study.
基于多模态MRI影像组学的自动分割堆叠集成学习模型用于子宫肌瘤高强度聚焦超声消融预后预测的多中心研究
Front Physiol. 2024 Dec 20;15:1507986. doi: 10.3389/fphys.2024.1507986. eCollection 2024.
4
Preoperative surgical planning MRI for fibroids: What the surgeon needs to know and what to report.子宫肌瘤术前手术规划的磁共振成像：外科医生需要了解的内容及报告要点
J Med Imaging Radiat Oncol. 2024 Dec 27. doi: 10.1111/1754-9485.13816.
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Radiomics and quantitative multi-parametric MRI for predicting uterine fibroid growth.用于预测子宫肌瘤生长的影像组学和定量多参数磁共振成像
J Med Imaging (Bellingham). 2024 Sep;11(5):054501. doi: 10.1117/1.JMI.11.5.054501. Epub 2024 Sep 12.
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nnU-Net based segmentation and 3D reconstruction of uterine fibroids with MRI images for HIFU surgery planning.基于 nnU-Net 的 MRI 图像子宫纤维瘤分割与 3D 重建用于 HIFU 手术规划。
BMC Med Imaging. 2024 Sep 6;24(1):233. doi: 10.1186/s12880-024-01385-3.
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