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NCT Number: NCT06795711

Validation and Optimisation of Ultrasound Diagnosis of Adenomyosis

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

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Key information

Age range

18 year–60 year

Sex eligibility

Female

Study type

Observational

Primary location

IRCCS Azienda Ospedaliero-Universitaria di Bologna

Bologna, 40138, Italy

Location status: Recruiting

Location contact

Diego Raimondo, MD

CONTACT

[email protected]

+393290636618

Diego Raimondo, MD

PRINCIPAL_INVESTIGATOR

About this study

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.

Who can participate

Healthy volunteers accepted: No

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

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

Treatment and study plan

Primary outcomes

  1. Definition of uterine biometric parameters

    Time frame: 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

  2. Diagnostic capacity of 'globular uterus' for the diagnosis of adenomyosis

    Time frame: 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 outcomes

  1. Construction of deep learning models on uterine ultrasound images

    Time frame: 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

  2. Evaluation of diagnostic accuracy of deep learning validated

    Time frame: After enrollment on first visit

    Evaluation of diagnostic accuracy of deep learning validated for ultrasound diagnosis of adenomyosis

  3. Identification of the frequency of finding ultrasound signs of adenomyosis in the cervix

    Time frame: 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

  4. Evaluation of diagnostic accuracy

    Time frame: 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

Study contacts

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

Diego Raimondo, MD

CONTACT

[email protected]

+393290636618

Sponsors and collaborators

Lead sponsor

IRCCS Azienda Ospedaliero-Universitaria di Bologna

Other

Registry information

Official study title

Validation and Optimisation of Ultrasound Diagnosis of Adenomyosis: a Prospective Observational Study

Important dates

Study start
2022
Primary completion
2024
Study completion
2025
First posted
Jan 28, 2025
Registry last updated
Jan 28, 2025

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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