IRCCS Azienda Ospedaliero-Universitaria di Bologna
Bologna, 40138, Italy
Location status: Recruiting
Location contact
Diego Raimondo, MD
CONTACT
Diego Raimondo, MD
PRINCIPAL_INVESTIGATOR
NCT Number: NCT06795711
Defining ultrasound criteria for normal uterine biometry and assessing the prevalence of repeat abortions in patients with abnormalities of the uterine cavity
Interested in participating?
Request Info18 year–60 year
Female
Observational
Bologna, 40138, Italy
Location status: Recruiting
Diego Raimondo, MD
CONTACT
Diego Raimondo, MD
PRINCIPAL_INVESTIGATOR
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:
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
Inclusion criteria
Exclusion criteria
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
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
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
Time frame: After enrollment on first visit
Evaluation of diagnostic accuracy of deep learning validated for ultrasound diagnosis of adenomyosis
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
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
Contact information is provided by the study sponsor or research team.
IRCCS Azienda Ospedaliero-Universitaria di Bologna
Other
Validation and Optimisation of Ultrasound Diagnosis of Adenomyosis: a Prospective Observational Study
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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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