PROJECT SUMMARY Breast cancer (BC) is a formidable global health challenge, affecting millions of women worldwide annually. This malignancy is a leading cause of death among women, underscoring its profound significance. Detecting BC at an early stage not only improves treatment outcomes but also significantly enhances survival rates. Currently, mammography is the standard of care for diagnosis of BC, and it is recommended that screening procedures commence at the age of 40, particularly due to the rising incidence of BC in women who are under the age of 50. Although mammography decreases BC-associated mortality, there exist distinct challenges with mammography alone for general population screening. For example, there is often inadequate healthcare accessibility for certain populations that exist due to socioeconomic disparities. BC mortality is heightened for Black women relative to White women, which is attributed to lack of access to regular screening, earlier age of disease incidence, and higher rates of triple-negative breast cancer. Inaccessibility of mammography to rural populations and a fear of painful mammogram experience also contribute to the lack of timely BC diagnosis in underrepresented populations. Screening mammography also exhibits inherent sensitivity limitations and often misses BC at earlier stages, especially for women with dense breasts, who comprise approximately 47% of women in the U.S. It is thus recommended that women with dense breasts upon mammographic exam undergo supplemental screening procedures such as ultrasound or MRI. Unfortunately, these imaging procedures are expensive and/or dependent upon the availability of specially trained breast sonographers and are not widely available to underserved populations. There is thus an urgent need for the development of innovative screening methods that facilitate BC detection in nascent stages and that are also deployable to the primary care setting and can be operated independent of user experience level. To address these problems, iSono Health has developed the ATUSA imaging platform, which represents a major advancement in supplemental BC screening. This novel system consists of an adjustable wearable imaging device that can rapidly perform whole breast scans in ~2 minutes coupled with artificial intelligence (AI)-assisted algorithms that accurately identify and diagnose BC at an early stage. The goal of this Direct-to-Phase II proposal is to develop and validate Lesion Classification, Lesion Segmentation, and Density Calculator AI models for 3D ATUSA images. Successful outcomes of this project will establish baseline measures of the ATUSA diagnostic performance. Validation results will allow iSono Health to submit a second FDA clearance of AI indications and commercially launch ATUSA. The ATUSA platform will transform how women access supplemental breast imaging and increase the number of women receiving adequate breast imaging and timely diagnosis, thus saving lives.