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机器学习在子宫内膜异位症诊断成像中的现状与未来潜力:文献综述

Current Status and Future Potential of Machine Learning in Diagnostic Imaging of Endometriosis : A Literature Review.

作者信息

Shrestha Palpasa, Shrestha Bibek, Sherestha Jati, Chen Jun

机构信息

Department of Radiology, Kenmin Hospital of Wuhan University, Jiefang Koad, Wuhan, Hubei Province, People's Republic of China.

Department of Kadiology, Zhongnan Hospital of Wuhan University, Wuhan, Hubei Province People's Republic of China.

出版信息

JNMA J Nepal Med Assoc. 2025 Mar;63(283):205-211. doi: 10.31729/jnma.8897. Epub 2025 Mar 31.

Abstract

The presence of endometrial tissue outside the uterus is a defining characteristic of endometriosis, a chronic systemic illness that affects women of childbearing age. Despite its enigmatic nature, laparoscopy remains the gold standard for diagnosis, while noninvasive methods such as transvaginal ultrasonography and magnetic resonance imaging are commonly used to aid in preoperative planning. In healthcare, AI has emerged as a game-changing innovation, enhancing patient outcomes, reducing costs, and revolutionizing healthcare delivery, particularly in diagnostic radiology. Images can be analyzed using machine learning, a pattern recognition method. The machine learning algorithm first computes the image characteristics deemed significant for making predictions or diagnoses about unseen images.

摘要

子宫外存在子宫内膜组织是子宫内膜异位症的一个决定性特征,子宫内膜异位症是一种影响育龄女性的慢性全身性疾病。尽管其本质神秘,但腹腔镜检查仍是诊断的金标准,而经阴道超声检查和磁共振成像等非侵入性方法通常用于辅助术前规划。在医疗保健领域,人工智能已成为一项改变游戏规则的创新技术,改善了患者治疗效果,降低了成本,并彻底改变了医疗服务的提供方式,尤其是在诊断放射学方面。图像可以使用机器学习这种模式识别方法进行分析。机器学习算法首先计算被认为对预测或诊断未知图像具有重要意义的图像特征。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2d9e/12122278/7c0b81c3e54b/JNMA-63-283-205-g1.jpg

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