Artificial intelligence applications in endometriosis imaging

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. Similar content being viewed by others Data availability No datasets were generated or analysed during the current study.

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Accessed January 1, 2025. https://imagendo.org.au/ Wiggins WF, Kitamura F, Santos I, Prevedello LM. Natural Language Processing of Radiology Text Reports: Interactive Text Classification. Radiology: Artificial Intelligence. 2021;3(4):e210035. doi:https://doi.org/10.1148/ryai.2021210035 Author information Authors and Affiliations Contributions All authors contributed to the review article’s conception and design. Literature search was performed by SM, and analysis was performed by all authors. The first draft of the manuscript was written by SM, and all authors critically revised the manuscript. All authors read and approved the final manuscript. Corresponding author Ethics declarations Competing interests Angela Tong has received an honorarium from Siemens Healthineers as a speaker and research support from Siemens Healthineers in the form of hardware and software. The other authors have no relevant financial or non-financial interests to disclose. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Electronic supplementary material Below is the link to the electronic supplementary material. Rights and permissions Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. About this article Cite this article Mittal, S., Tong, A., Young, S. et al. Artificial intelligence applications in endometriosis imaging. Abdom Radiol 50, 4901–4913 (2025). https://doi.org/10.1007/s00261-025-04897-w Received: Revised: Accepted: Published: Version of record: Issue date: DOI: https://doi.org/10.1007/s00261-025-04897-w