Artificial Intelligence for the Intra-procedural Assessment of Uterine Artery Embolization

Trial statusNot yet recruiting
Trial phaseNot listed
Trial typeObservational
Biological sexFemale
Age18-55
SponsorEmanuele Barabino

About this trial

Uterine artery embolization is a minimally invasive treatment for symptomatic uterine fibroids, but intra-procedural assessment of embolization adequacy currently relies on subjective angiographic criteria. This study evaluates a proprietary angiographic analysis software (AQ-VERO) that extracts quantitative time-to-density perfusion metrics in real time. The study aims to (1) validate the accuracy and reproducibility of AQ-VERO during uterine artery mebolization, and (2) develop an AI-based decision support system using AQ-VERO-derived metrics to improve objective intra-procedural assessment of treatment endpoints.

Eligibility criteria

This trial does not accept healthy volunteers

Qualifiers

Female patients ≥18 years

Symptomatic uterine fibroids (e.g., bleeding, bulk symptoms, pain)

Underwent UAE as definitive therapy

Availability of baseline clinical/imaging data (for retrospective arm) or ability to provide informed consent (for prospective arm)

Disqualifiers

Lack of clinical follow-up

Poor quality or incomplete angiographic images.

Trial population

The study population consists of adult female patients aged 18 to 55 years with symptomatic uterine fibroids who underwent or are undergoing uterine artery embolization as their primary treatment. All participants must have angiographic imaging suitable for quantitative analysis and a minimum of 6 months of available clinical follow-up.

Trial design

Design model

Cohort

Time perspective

Other

Treatments tested in this trial

Not listed

Trial groups

250 Participants
are grouped into 1 trial group
Group A: 250 patients with a diagnosis of uterine fibroids who underwent uterine artery embolization

Trial outcomes

Primary outcomes

1

The primary outcome measure is the AUPRC of the predictive models.

The Area Under the Precision-Recall Curve (AUPRC) will be calculated to evaluate the performance of the AI-based decision support model in identifying clinically relevant embolization endpoints. AUPRC is a threshold-independent metric that summarizes the tradeoff between precision (positive predictive value) and recall (sensitivity) across all decision thresholds. It is particularly suitable for imbalanced datasets, where positive outcome events may be less frequent. Higher AUPRC values indicate better discriminative performance of the model.

Time frame
From treatment to the end of the required follow-up (6 months).

Secondary outcomes

Other outcomes

Sponsors and contacts

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Emanuele Barabino

Lead sponsor

IRCCS Azienda Ospedaliera Universitaria San Martino - IST Istituto Nazionale per la Ricerca sul Cancro, Genoa, Italy

Sponsor institution

This trial is not recruiting at the moment. You can still explore other options: