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. 2025 Sep:124:102575.
doi: 10.1016/j.compmedimag.2025.102575. Epub 2025 May 29.

Unpaired multi-modal training and single-modal testing for detecting signs of endometriosis

Yuan Zhang  1 , Hu Wang  2 , David Butler  3 , Brandon Smart  4 , Yutong Xie  2 , Minh-Son To  5 , Steven Knox  6 , George Condous  7 , Mathew Leonardi  8 , Jodie C Avery  9 , M Louise Hull  9 , Gustavo Carneiro  10
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Free article

Unpaired multi-modal training and single-modal testing for detecting signs of endometriosis

Yuan Zhang et al. Comput Med Imaging Graph. 2025 Sep.
Free article

Abstract

Endometriosis is a serious multifocal condition that can involve various pelvic structures, with Pouch of Douglas (POD) obliteration being a significant clinical indicator for diagnosis. To circumvent the need for invasive diagnostic procedures like laparoscopy, research has increasingly focused on imaging-based methods such as transvaginal ultrasound (TVUS) and magnetic resonance imaging (MRI). The limited diagnostic accuracy achieved through manual interpretation of these imaging techniques has driven the development of automated classifiers that can effectively utilize both modalities. However, patients often undergo only one of these two examinations, resulting in unpaired data for training and testing POD obliteration classifiers, where TVUS models tend to be more accurate than MRI models, but TVUS scanning are more operator dependent. This prompts a crucial question: Can a model be trained with unpaired TVUS and MRI data to enhance the performance of a model exclusively trained with MRI, while maintaining the high accuracy of the model individually trained with TVUS? In this paper we aim to answer this question by proposing a novel multi-modal POD obliteration classifier that is trained with unpaired TVUS and MRI data and tested using either MRI or TVUS data. Our method is the first POD obliteration classifier that can flexibly take either the TVUS or MRI data, where the model automatically focuses on the uterus region within MRI data, eliminating the need for any manual intervention. Experiments conducted on our endometriosis dataset show that our method significantly improves POD obliteration classification accuracy using MRI from AUC=0.4755 (single-modal training and testing, without automatically focusing on the uterus region) to 0.8023 (unpaired multi-modal training and single modality MRI testing, with automatic uterus region detection), while maintaining the accuracy using TVUS with AUC=0.8921 (single modality TVUS testing using either an unpaired multi-modal training or a single-modality training).

Keywords: Endometriosis; Pouch of douglas (POD) obliteration; Uncertainty; Unpaired multi-modal learning.

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Conflict of interest statement

Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: M Louise Hull reports financial support was provided by Australian Government MRFF. Jodie C Avery reports financial support was provided by Endometriosis Australia. M Louise Hull reports financial support was provided by Primary Health Care Research Data Infrastructure Grant 2020. Steven Knox reports a relationship with Siemens Healthineers that includes: consulting or advisory, non-financial support, and speaking and lecture fees. George Condous reports a relationship with Samsung that includes: consulting or advisory and speaking and lecture fees. George Condous reports a relationship with GE healthcare that includes: consulting or advisory and speaking and lecture fees. George Condous reports a relationship with ISUOG Board of trustees that includes: board membership. George Condous reports a relationship with WFUMB President-Elect that includes:. Gustavo Carneiro reports a relationship with EPSRC grant that includes: funding grants. Mathew Leonardi reports a relationship with Australian MRFF, AbbVie, CanSAGE, CIHR, Hamilton Health Sciences, Hyivy, Pfizer that includes: funding grants. Mathew Leonardi reports a relationship with AIUM, GE Healthcare, Bayer, AbbVie that includes: speaking and lecture fees. Mathew Leonardi reports a relationship with Abbvie, Hologic, Chugai, Gesynta, Roche Diagnostics, Afynia, Pfizer that includes: consulting or advisory. Mathew Leonardi reports a relationship with Imagendo, Specialized Ultrasound in Gynecology & Obstetrics that includes: funding grants. Yuan Zhang, Hu Wang, Steven Knox, George Condous, Mathew Leonardi, M Louise Hull, Gustavo Carneiro has patent #PCT/AU2024/050999 pending to THE UNIVERSITY OF ADELAIDE. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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