Identification of Breast Abnormalities Using Feminai-Breast Examination Patch: A Feasibility Study

Early detection remains critical for reducing breast-cancer mortality, yet millions of women worldwide, particularly those in low-resource, rural, or underserved communities, face significant barriers to screening. Clinic-based imaging modalities such as mammography, ultrasound, and MRI require specialized infrastructure, trained personnel, and in-person attendance, contributing to persistent underscreening and sometimes late-stage diagnoses. Feminai is a disposable, wearable self-breast-examination patch that integrates heat, blood flow, tissue conductivity, and density sensing, with AI-driven analysis. This study evaluates the device's accuracy in identifying abnormal breast findings.

This prospective, noninterventional validation study enrolled 150 women aged 25 to 75 undergoing breast-cancer screening at the "Merav" Clinic, Tel Hashomer Hospital. Participants completed a medical questionnaire and underwent a 5-minute scan using the Feminai wearable patch. All participants underwent standard imaging with mammography, with diagnostic ultrasound or MRI as indicated. Sensor-derived heat and tissue conductivity data were analyzed using a proprietary AI algorithm and compared against radiological assessments and biopsy results.

Among 150 women (mean age 49 years), screening mammography identified 75 as BI-RADS 1 to 2, and 75 as BI-RADS 4 to 5. The Feminai device identified 70 of 75 BI-RADS 4 to 5 cases as suspicious, correctly detecting all biopsy-proven malignant lesions and five benign cases as nonsuspicious, corresponding to 96% sensitivity, 82% specificity, and 98% negative predictive value.

The Feminai Breast Examination Kit showed high accuracy, particularly in sensitivity and NPV, indicating strong potential as a remote, cost-effective, and user-friendly early-detection tool. By enabling self-administered screening without reliance on specialized facilities, Feminai offers a scalable pathway to improve access in rural settings, low-resource communities, and medically underserved populations, groups most vulnerable to delayed diagnosis. Further large-scale validation is warranted, but these findings support its promise as an impactful addition to global breast-cancer screening strategies.