Evidence map›Paper›PMID 40232413›Full record

ArticleAbdominal radiology (New York)2026

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

Mana Moassefi, Shahriar Faghani, Ceylan Colak, Shannon P Sheedy, Pamela L Causa Andrieu, Sherry S Wang, Rachel L McPhedran, Kristina T Flicek, Garima Suman, Hiroaki Takahashi and 4 more

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Article in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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3citing papers in PubMed
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3 · Its place in the literature

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3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

14 authors.

Mana MoassefiMayo Clinic, Rochester, USA.
Shahriar FaghaniMayo Clinic, Rochester, USA.
Ceylan ColakMayo Clinic, Rochester, USA.
Shannon P SheedyMayo Clinic, Rochester, USA.
Pamela L Causa AndrieuMayo Clinic, Rochester, USA.
Sherry S WangMayo Clinic, Rochester, USA.
Rachel L McPhedranMayo Clinic, Rochester, USA.
Kristina T FlicekMayo Clinic, Rochester, USA.
Garima SumanMayo Clinic, Rochester, USA.
Hiroaki TakahashiMayo Clinic, Rochester, USA.
Candice A BookwalterMayo Clinic, Rochester, USA.
Tatnai L BurnettMayo Clinic, Rochester, USA.
Bradley J EricksonMayo Clinic, Rochester, USA.
Wendaline M VanBurenMayo Clinic, Rochester, USA. Vanburen.wendaline@mayo.edu.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

background and purposeEndometriosis 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.

methodWe 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.

results395 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.

conclusionThis 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.

Indexed as

Deep LearningEndometriosisImage Interpretation, Computer-AssistedMagnetic Resonance ImagingAdultFemaleHumansMiddle AgedRetrospective StudiesSensitivity and SpecificityArtificial intelligenceDeep learningDiagnosisEndometriosisMRI

Identifiers

PMID40232413

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