Validation and Optimisation of Ultrasound Diagnosis of Adenomyosis

Scientific Institute for Research Hospitalization and Healthcare (IRCCS)

Status

Enrolling

Conditions

Adenomyosis

Study type

Observational

Funder types

Other

Identifiers

NCT06795711

ADENAS

Details and patient eligibility

About

Defining ultrasound criteria for normal uterine biometry and assessing the prevalence of repeat abortions in patients with abnormalities of the uterine cavity

Full description

Adenomyosis is a gynaecological disorder with a high prevalence in women of childbearing age and is characterised by the presence of glands and endometrial stroma within the myometrium, associated or not with hypertrophy and hyperplasia of the surrounding myometrium. Adenomyosis may cause pelvic pain and/or abnormal uterine bleeding. Transvaginal ultrasound may be considered the main non-invasive diagnostic modality for the diagnosis of adenomyosis. The aim is to optimise the ultrasound diagnosis of uterine pathology and in particular of adenomyosis by defining uterine biometric parameters (longitudinal, transverse and anteroposterior diameters and their ratios; uterine volume) allowing patients to be divided into 3 groups:

Uterus affected by adenomyosis (group A): adenomyosis is a gynaecological condition with high prevalence in women of childbearing age and is characterised by the presence of endometrial tissue (innermost layer of the uterus) within the uterine muscle. Adenomyosis can cause abdominal pain and abnormal uterine bleeding.
Uterus affected by fibromatosis (group B): uterine fibromatosis is a gynaecological condition characterised by the appearance of numerous fibroids in the uterus. It is a very frequent condition in the general population and its frequency increases as the age of the patients increases.
Normal uterus (group C). Transvaginal ultrasound, although a reference diagnostic tool, still remains an operator-dependent examination to date: our secondary objective is to build models that can simplify diagnosis through the use of artificial intelligence. The aim is to create various artificial intelligence software that can 'learn to make a diagnosis'. This method has already been applied in radiology, proving capable of discriminating between benign and malignant tumours from images from different diagnostic methods with performance similar to that of experienced radiologists.

Enrollment

465 estimated patients

Sex

Female

Ages

18 to 60 years old

Volunteers

No Healthy Volunteers

Inclusion criteria