Artificial Intelligence in Endometriosis Imaging: A Scoping Review

Rawan AlSaad, Thomas J. Farrell, Ali M. Elhenidy, Shima Elbakhit M. E. Albasha, Rajat M. Thomas
In: AI · 2026 · vol. 7(2) , pp. 43 · doi:10.3390/ai7020043 · W7126069505
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This scoping review found that AI applications in endometriosis imaging are rapidly evolving but early-stage, predominantly using deep learning for classification on ultrasound, with significant methodological gaps requiring further multi-center and prospective validation.

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Abstract

Endometriosis is a chronic gynecological condition characterized by endometrium-like tissue outside the uterus. In clinical practice, diagnosis and anatomical mapping rely heavily on imaging, yet performance remains operator- and modality-dependent. Artificial intelligence (AI) has been increasingly applied to endometriosis imaging. We conducted a PRISMA-ScR-guided scoping review of primary machine learning and deep learning studies using endometriosis-related imaging. Five databases (MEDLINE, Embase, Scopus, IEEE Xplore, and Google Scholar) were searched from 2015 to 2025. Of 413 records, 32 studies met inclusion and most were single-center, retrospective investigations in reproductive-age cohorts. Ultrasound predominated (50%), followed by laparoscopic imaging (25%) and MRI (22%); ovarian endometrioma and deep infiltrating endometriosis were the most commonly modeled phenotypes. Classification was the dominant AI task (78%), typically using convolutional neural networks (often ResNet-based), whereas segmentation (31%) and object detection (3%) were less explored. Nearly all studies relied on internal validation (97%), most frequently simple hold-out splits with heterogeneous, accuracy-focused performance reporting. The minimal AI-method quality appraisal identified frequent methodological gaps across key domains, including limited reporting of patient-level separation, leakage safeguards, calibration, and data and code availability. Overall, AI-enabled endometriosis imaging is rapidly evolving but remains early-stage; multi-center and prospective validation, standardized reporting, and clinically actionable detection–segmentation pipelines are needed before routine clinical integration.

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Condition tags

endometriosisdie_deep_infiltratingendometrioma

Citation neighborhood

Papers in the corpus that this work cites (lower rings, blue) and that cite this one (upper rings, green). Dot size scales with the paper's in-corpus citation count — bigger dot = more influential within the endo/adeno field. Click a dot to open that paper. [ expand to 2 hops ] — adds papers reached through this work's immediate citers/citees. Heavier; up to 60 extra dots.

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