Artificial Intelligence in Gynecologic Imaging
What was done? A review of artificial intelligence (AI) applications for the imaging of uterine fibroids, endometriosis, and adenomyosis. What was found? AI models can assist with the recognition, segmentation, and localization of uterine fibroids, and the differentiation of benign fibroids and sarcomas. Models can aid in the diagnosis of adenomyosis and endometriosis, and the prediction of the impact of endometriosis on fertility. What the findings mean? Deployed thoughtfully, AI tools could reduce variability, shorten read times, and add objective measurements to routine care. Studies evaluating these models are limited by single-institution designs and continued reliance on expert sonologists and radiologists.
Authors
- Timothy L. Kline (ORCID: https://orcid.org/0000-0002-7917-9853)
- Sarah L. Cohen Rassier (ORCID: https://orcid.org/0000-0002-2594-7933)
- Isabel C Green
- Morgan Briggs
- Ayesha Saif
- Wendaline VanBuren
Institutions
- Mayo Clinic (US)
- Minimally Invasive Pain Institute (US)
- Center for Special Minimally Invasive and Robotic Surgery (US)
Publication Details
- Journal
- Clinical Obstetrics & Gynecology
- Published
- 2026-01-06
- DOI
- https://doi.org/10.1097/grf.0000000000000984
- Primary Topic
- Endometriosis Research and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00