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 ev...
| Published in: | Abdominal Radiology Vol. 51; no. 1; pp. 238 - 250 |
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| Main Authors: | , , , , , , , , , , , , , |
| Format: | Journal Article |
| Published: |
Springer Nature
Jan2026
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=191138639&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 191138639 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 2366004X JT14 jtl: Abdominal Radiology issn: 2366004X maglogo: N pubinfo: dt: Jan2026 vid: 51 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 191138639 184488109 10.1007/s00261-025-04942-8 191138639 ppf: 238 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Advancing endometriosis detection in daily practice: a deep learning-enhanced multi-sequence MRI analytical model. aug: au: Moassefi, Mana Faghani, Shahriar Colak, Ceylan Sheedy, Shannon P. Andrieu, Pamela L. Causa Wang, Sherry S. McPhedran, Rachel L. Flicek, Kristina T. Suman, Garima Takahashi, Hiroaki Bookwalter, Candice A. Burnett, Tatnai L. Erickson, Bradley J. VanBuren, Wendaline M. affil: https://ror.org/02qp3tb03 Mayo Clinic, Rochester, USA sug: ab: 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. Method: 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. Results: 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. Conclusion: 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. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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