Using AI-Supported Point-of-Care Ultrasound for Breast Cancer Screening in Women

What this Trial Is About

Researchers are evaluating the feasibility, safety, and clinical performance of a community-based breast cancer screening approach that uses clinical breast exams CBE followed by targeted artificial intelligence AI-supported breast point-of-care ultrasound POCUS for triage. This study takes place in limited-resource settings and aims to improve early detection of breast cancer by involving trained examiners and AI technology. The screening process begins with a clinical breast exam for all participating women, both asymptomatic and symptomatic. Those with positive CBE results or symptoms undergo AI-supported POCUS performed by clinical nurses or officers trained and certified in this technique. The study has two stages initially, an expert breast radiologist is present on site for examinations later, the radiologist reviews ultrasound images remotely. Women with positive ultrasound findings or concerning clinical symptoms will be referred for further follow-up. Participants will attend screening following an awareness campaign and will undergo clinical exams and ultrasound imaging as part of the study. Breast radiologists evaluate ultrasound images on site during the first stage and remotely in the second. The study will measure diagnostic accuracy such as sensitivity, specificity, and predictive values over two months, as well as cancer characteristics and staging up to 12 months. Usability and acceptability of the screening method will also be assessed, with ongoing monitoring of participants as required.

System-integrated Point-of-Care Ultrasound in Breast Cancer Screening