Advancing endometriosis detection in daily practice: a deep learning-enhanced multi-sequence MRI analytical model

Background and purpose Endometriosis affects 5–10% of women of reproductive age. Despite its prevalence, diagnosing endometriosis through imaging remains challenging. Advances in deep learning (DL) are revolutionizing the diagnosis and management of complex medical conditions. This study aims to evaluate DL tools in enhancing the accuracy of multi-sequence MRI-based detection of endometriosis.

We gathered a patient cohort from our institutional database, composed of patients with pathologically confirmed endometriosis from 2015 to 2024. We created an age-matched control group that underwent a similar MR protocol without an endometriosis diagnosis. We used sagittal fat-saturated T1-weighted (T1W FS) pre- and post-contrast and T2-weighted (T2W) MRIs. Our dataset was split at the patient level, allocating 12.5% for testing and conducting seven-fold cross-validation on the remainder. Seven abdominal radiologists with experience in endometriosis MRI and complex surgical planning and one women’s imaging fellow with specific training in endometriosis MRI reviewed a random selection of images and documented their endometriosis detection.

395 and 356 patients were included in the case and control groups respectively. The final 3D-DenseNet-121 classifier model demonstrated robust performance. Our findings indicated the most accurate predictions were obtained using T2W, T1W FS pre-, and post-contrast images. Using an ensemble technique on the test set resulted in an F1 Score of 0.881, AUROCC of 0.911, sensitivity of 0.976, and specificity of 0.720. Radiologists achieved 84.48% and 87.93% sensitivity without and with AI assistance in detecting endometriosis. The agreement among radiologists in predicting labels for endometriosis was measured as a Fleiss’ kappa of 0.5718 without AI assistance and 0.6839 with AI assistance.

This study introduced the first DL model to use multi-sequence MRI on a large cohort, showing results equivalent to human detection by trained readers in identifying endometriosis. Key findings Endometriosis diagnosis using multi-sequence MRI and a 3D-DenseNet-121 deep-learning model showed promising results. The model achieved F1 Score: 0.881, AUROCC: 0.911, sensitivity: 0.976, and specificity: 0.720. AI slightly improved radiologists’ sensitivity from 84.48 to 87.93% and increased their agreement (Fleiss’ kappa: 0.5718 to 0.6839), highlighting its potential to enhance clinical detection of endometriosis while reducing reading time. Highlights Deep-learning models leveraging multi-sequence MRI significantly enhance endometriosis detection accuracy, surpassing traditional methods and improving radiologists’ performance, marking an advance in imaging-based diagnostics. AbstractSection Graphical abstractSimilar content being viewed by others Data availability No datasets were generated or analysed during the current study. Abbreviations - CNNs: - Convolutional Neural Networks - DL: - Deep Learning - MRI: - Magnetic Resonance Imaging - AI: - Artificial Intelligence - ROS: - Reactive Oxygen Species - T2W: - T2-weighted imaging - T1W: - T1-weighted images - CE-T1W: - Contrast-enhanced T1-weighted images - AUROCC: - Area Under the ROC Curve - US: - Ultrasonography or Ultrasound - FS: - Fat saturated

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Funding: None.Statement and Declaration: Competing Interest: Dr. VanBuren has received funding from the following societies to support travel for educational lectures at: the American Association of Gynecological Laparoscopists (AAGL), the American Society of Reproductive Medicine (ASRM), the American Roentgen Ray Society (ARRS), the World Endometriosis Congress (WEC) and from the Society for Women’s Health Research (SWHR) to advocate for endometriosis on Capitol Hill. She is the founder of the International Endometriosis Imaging Congress. Author information Authors and Affiliations Contributions M.M. and S.F. authored the initial draft of the manuscript and revised it based on feedback provided by B.E. and W.V. The development of the AI model was conducted by M.M. and S.F., under the supervision of B.E. Data collection and curation were contributed by C.C., W.V., T.B., and S.G. All other authors participated in the radiological review of the imaging data and the detection of endometriosis. The final manuscript was reviewed and approved by all authors. Corresponding author Ethics declarations Competing interests The authors declare no competing interests. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. 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 Moassefi, M., Faghani, S., Colak, C. et al. Advancing endometriosis detection in daily practice: a deep learning-enhanced multi-sequence MRI analytical model. Abdom Radiol 51, 238–249 (2026). https://doi.org/10.1007/s00261-025-04942-8 Received: Revised: Accepted: Published: Version of record: Issue date: DOI: https://doi.org/10.1007/s00261-025-04942-8