Breast Ultrasound Image Reviewed With Assistance of Deep Learning Algorithms

Using ultrasound images prospectively acquired, the purpose of this study entails a second
review of ultrasound images with suspicious breast lesions using an interactive "deep
learning" (or artificial intelligence) program developed by SamsungMedison Co.,Ltd.

The images will be reviewed by the radiologists twice: first without, and then with
assistance of artificial intelligence program by SamsungMedison Co., Ltd.

BIRADS system will be used in this study.

The objectives of the study are twofold: to quantify the statistical equivalence of
radiologists' opinion and AI's output (CADe), and to check BIRADS score-based diagnostic
accuracy (CADx) that is gained by the Radiologists' use of this interactive tool

Inclusion Criteria:

- Adult females or males recommended for ultrasound-guided breast lesion biopsy or
ultrasound follow-up with at least one suspicious lesion

- Age > 21 years

- Able to provide informed consent

Exclusion Criteria:

- Unable to read and understand English

- Unable or unwilling to provide informed consent

- A patient who currently has breast cancer or was previously diagnosed with such unable
or unwilling to undergo study procedures

Subject Characteristics

- Gender and Age of Subjects: Adult females or males aged 21 years or older who meet all
of the inclusion criteria and none of the exclusion criteria will be considered for
enrollment. Minors are excluded as breast cancer is very rare in this age group.

- Racial and Ethnic Origin: There are no enrollment exclusions based on economic status,
race, or ethnicity. Based on local and United States census data, the expected ethnic
distribution will be approximately 49 Hispanic (approx. 16%) and 251 non-Hispanic
people. Furthermore, the expected racial distribution is expected to be approximately
235 White (approx. 79% of the whole study), 40 Black or African America (13%), 15
Asian (5%), and 10 of other categories (3%).