RecruitingObservational

Clinical Evidence Generation of AI Enabled Fast, Accurate and Precise Screening and Staging of Benign vs Malignant Pelvic Abnormalities

NCT ID: NCT07407959Sponsor: Mayo ClinicLast updated: 2026-02-17

Summary

This study evaluates whether newly developed non-FDA approved image processing techniques \[Adaptive Image Reconstruction (AIR Recon) Deep Learning (DL) and Sonic DL\] can provide improved quality and decreased time compared to current scanning techniques.

Arms & interventions

OtherNon-Interventional Study
Non-interventional study

Outcome measures

Primary

Change in image quality (healthy volunteers)
Three expert radiologists will read scans (blinded to technique) and provide qualitative feedback to score image quality for standard of care (SOC) imaging, SOC with AirRecon DL, SOC with Sonic DL, and SOC with AirRecon DL and Sonic DL. Reader scoring will use a 1-5 Likert scale (1-5; 1:poor, 5:excellent).
Time frame: Baseline
Change in scan time
Will be considered improved if the duration of the added imaging does not exceed 10 minutes
Time frame: Baseline

Eligibility criteria

Sex: AllAge: 18 Years and olderHealthy volunteers: Yes

Inclusion Criteria: * Patient over the age of 18 * For prostate group, any patient referred for prostate MR for screening purposes with no prior prostate related treatment or prior biopsy * For endometriosis group, any patient referred for possible endometriosis evaluation (pre-surgical) Exclusion Criteria: * Individuals unable to undergo MRI imaging (MR-conditional or MR-nonconditional devices which would need additional procedures/conditions for scanning, pregnant people, individuals with implanted metal, etc.) * Patient under the age of 18 * Patient who is unable to consent

Study locations (1)

Mayo Clinic in Rochester

Rochester, Minnesota, 55905

Recruiting

Clinical Trials Referral Office · Contact

855-776-0015 mayocliniccancerstudies@mayo.edu

Bobbie Ott · Contact

507-293-0922

Candice A. Bookwalter, MD, PhD · Principal Investigator