Artificial Intelligence Into Ultrasound Diagnostics in Reproductive Medicine

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.

Recruiting

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Key information

Conditions

Endometriosis Adenomyosis Adenomyosis of Uterus Congenital Abnormalities Congenital, Hereditary, and Neonatal Diseases and Abnormalities Connective Tissue Diseases Female Urogenital Diseases Female Urogenital Diseases and Pregnancy Complications Genital Diseases Genital Diseases, Female Genital Neoplasms, Female Infertility Infertility, Female Leiomyoma Leiomyoma (Uterine Fibroids) Myofibroma Neoplasms Neoplasms by Histologic Type Neoplasms by Site Neoplasms, Connective Tissue Neoplasms, Connective and Soft Tissue Neoplasms, Muscle Tissue Skin and Connective Tissue Diseases Urogenital Diseases Urogenital Neoplasms Uterine Diseases Uterine Neoplasms

Age range

18 year–45 year

Sex eligibility

Female

Study type

Observational

Primary location

Department of Obstetrics and Gynecology, Semmelweis University

Budapest, 1088, Hungary

Location status: Recruiting

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

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) (19).
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.
Willingness and capacity to provide written informed consent prior to enrolment.

Exclusion criteria

TVUS is not technically feasible.
Postmenopausal status.
Pregnancy.
Puerperium (up to 3 months postpartum).
Suspected premalignancy, or presence or history of malignancy.
Chronic comorbidities, including uncontrolled diabetes mellitus, severe cardiovascular or respiratory disease, systemic autoimmune disease, or uncontrolled thyroid dysfunction.
Other conditions, including psychiatric illness or substance use, and any circumstance in which study participation could pose a risk to the patient, or in which enrolment could bias the study results.
For the endometrial receptivity sub-study, confirmed uterine or endometrial pathology other than adenomyosis.

Treatment and study plan

Primary outcomes

Diagnostic accuracy of the AI algorithm for detection of endometriosis and adenomyosis on transvaginal ultrasound

Time frame: 3 years

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.

Study contacts

Contact information is provided by the study sponsor or research team.

Nandor Acs, PhD

CONTACT

[email protected]

+3620825001

Sponsors and collaborators

Lead sponsor

Semmelweis University

Other

Registry information

Official study title

Innovation for Women's Health: the Integration of Artificial Intelligence in the Diagnostic Ultrasound Assessment of Reproductive Medicine

Acronym: AUDRIE

Important dates

Study start: 2026
Primary completion: 2030
Study completion: 2030
First posted: Sep 9, 2026
Registry last updated: Sep 9, 2026

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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