The goal of this observational study is to develop and validate artificial intelligence (AI)-based algorithms that support ultrasound diagnosis of endometriosis, adenomyosis, myometrial masses, uterine malformations, and impaired endometrial receptivity in women aged 18-45 years. The main questions it aims to answer are:
Can AI algorithms, applied to standardized transvaginal ultrasound images, accurately detect and classify endometriosis and adenomyosis in real time during routine examination? Can AI-based ultrasound assessment predict the histological dignity of myometrial masses, and the likelihood of successful assisted reproduction (AR/IVF) outcome from endometrial features?
Participants attending the Department of Obstetrics and Gynecology, Semmelweis University, with clinical suspicion of endometriosis or adenomyosis, a confirmed myometrial mass scheduled for surgery, a suspected uterine anomaly, or scheduled IVF treatment, will undergo standardized transvaginal ultrasound examination (following the IDEA, MUSA, and IETA protocols) alongside collection of clinical, questionnaire, and, where surgery is performed, histopathological data. Imaging and clinical data will be used to build a database supporting the development and validation of the AI algorithms.
Women aged 18-45 years with clinical suspicion of endometriosis or adenomyosis, in whom the diagnosis of endometriosis is subsequently confirmed, including peritoneal, ovarian, and deep infiltrating endometriosis. The diagnosis is established by imaging, laparoscopy, or laparotomy, based on characteristic intraoperative findings and histological analysis (#Enzian classification).
Women undergoing ultrasound examination for suspected congenital uterine anomaly or other intracavitary uterine pathology.
Women with previously confirmed leiomyoma scheduled for surgical treatment.
Women scheduled for IVF treatment or embryo transfer.
TVUS is not technically feasible.
Postmenopausal status.
Pregnancy.
Puerperium (up to 3 months postpartum).
The study population comprises patients aged 18-45 years who attend the Department of Obstetrics and Gynecology at Semmelweis University. A total of 1600 participants will be enrolled: 1200 for the endometriosis/adenomyosis cohort, 200 for the myometrial mass dignity-prediction cohort, and 200 for the cohort addressing other benign infertility-related conditions and AR/IVF outcome prediction.
Cohort
Prospective
Not listed
Diagnostic accuracy of the AI algorithm for detection of endometriosis and adenomyosis on transvaginal ultrasound
Sensitivity, specificity, and area under the receiver operating characteristic curve (AUC) of the AI-based detection algorithm for endometriosis and adenomyosis, assessed against the reference standard of surgical findings and histopathological confirmation (for participants undergoing surgery) or, where surgery is not performed, expert consensus review of the standardized ultrasound examination.
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