Mittal Sneha, Tong Angela, Young Scott, Jha Priyanka
University of Tennessee Health Science Center, Memphis, USA.
New York University, New York, USA.
Abdom Radiol (NY). 2025 Apr 1. doi: 10.1007/s00261-025-04897-w.
Artificial intelligence (AI) may have the potential to improve existing diagnostic challenges in endometriosis imaging. To better direct future research, this descriptive review summarizes the general landscape of AI applications in endometriosis imaging. Articles from PubMed were selected to represent different approaches to AI applications in endometriosis imaging. Current endometriosis imaging literature focuses on AI applications in ultrasound (US) and magnetic resonance imaging (MRI). Most studies use US data, with MRI studies being limited at present. The majority of US studies employ transvaginal ultrasound (TVUS) data and aim to detect deep endometriosis implants, adenomyosis, endometriomas, and secondary signs of endometriosis. Most MRI studies evaluate endometriosis disease diagnosis and segmentation. Some studies analyze multi-modal methods for endometriosis imaging, combining US and MRI data or using imaging data in combination with clinical data. Current literature lacks generalizability and standardization. Most studies in this review utilize small sample sizes with retrospective approaches and single-center data. Existing models only focus on narrow disease detection or diagnosis questions and lack standardized ground truth. Overall, AI applications in endometriosis imaging analysis are in their early stages, and continued research is essential to develop and enhance these models.
人工智能（AI）可能有潜力改善子宫内膜异位症成像中现有的诊断难题。为了更好地指导未来的研究，这篇描述性综述总结了AI在子宫内膜异位症成像中的应用概况。从PubMed中选取文章以代表AI在子宫内膜异位症成像中的不同应用方法。当前的子宫内膜异位症成像文献聚焦于AI在超声（US）和磁共振成像（MRI）中的应用。大多数研究使用超声数据，目前MRI研究有限。大多数超声研究采用经阴道超声（TVUS）数据，旨在检测深部子宫内膜异位症植入物、子宫腺肌病、子宫内膜异位囊肿以及子宫内膜异位症的次要征象。大多数MRI研究评估子宫内膜异位症疾病的诊断和分割。一些研究分析用于子宫内膜异位症成像的多模态方法，将超声和MRI数据相结合，或使用成像数据与临床数据相结合。当前文献缺乏普遍性和标准化。本综述中的大多数研究采用小样本量、回顾性方法和单中心数据。现有模型仅关注狭窄的疾病检测或诊断问题，且缺乏标准化的基本事实。总体而言，AI在子宫内膜异位症成像分析中的应用尚处于早期阶段，持续的研究对于开发和改进这些模型至关重要。
