Adenomyosis
Conditions
Keywords
adenomyosis, gynaecology, ultrasound
Brief summary
Defining ultrasound criteria for normal uterine biometry and assessing the prevalence of repeat abortions in patients with abnormalities of the uterine cavity
Detailed 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.
Interventions
None listed
Sponsors
IRCCS Azienda Ospedaliero-Universitaria di Bologna
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
FEMALE
Age
18 Years to 60 Years
Healthy volunteers
No
Inclusion criteria
* age between 18 and 60; * obtaining informed consent
Exclusion criteria
* Hysterectomised patients; * Virgo patients (hymenal integrity); * Patients reporting intolerance to transvaginal ultrasound; * Gynaecological oncology; * Recent pregnancy or childbirth (within 6 months); * Menopausal patients
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Definition of uterine biometric parameters | After enrollment on first visit | Definition of uterine biometric parameters for the diagnosis of adenomyotic uterus (group A), fibromatous uterus (group B) and normal uterus (group C) by means of transvaginal ultrasound, performed as per the care procedure. Evaluation of the diagnostic capacity of 'globular uterus' for the diagnosis of adenomyosis as an additional parameter to those already known in the literature with possible subsequent identification of a biometric cut-off |
| Diagnostic capacity of 'globular uterus' for the diagnosis of adenomyosis | After enrollment on first visit | Evaluation of the diagnostic capacity of 'globular uterus' for the diagnosis of adenomyosis as an additional parameter to those already known in the literature with possible subsequent identification of a biometric cut-off |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Construction of deep learning models on uterine ultrasound images | After enrollment on first visit | Construction of deep learning models trained, validated and tested on uterine ultrasound images for the ultrasound diagnosis of adenomyosis and evaluation of their diagnostic accuracy |
| Evaluation of diagnostic accuracy of deep learning validated | After enrollment on first visit | Evaluation of diagnostic accuracy of deep learning validated for ultrasound diagnosis of adenomyosis |
| Identification of the frequency of finding ultrasound signs of adenomyosis in the cervix | After enrollment on first visit | In patients with a diagnosis of adenomyosis made on the basis of ultrasound features at the level of the uterine body and fundus |
| Evaluation of diagnostic accuracy | After enrollment on first visit | Evaluation of the diagnostic accuracy of trainees when experienced (identifying experienced operators as doctors in specialised training in Gynaecology and Obstetrics for at least four years, with an experience of at least 500 gynaecological ultrasound cases) and moderately experienced (identifying moderately experienced operators as doctors in specialised training in Gynaecology and Obstetrics for at least two years, with an experience of at least 200 gynaecological ultrasound cases |
Countries
Italy
Contacts
Primary ContactDiego Raimondo, MD
Outcome results
None listed