Keywords: Breast, Machine Learning/Artificial Intelligence, Fatty tissue, ScreeningBI-RADS breast tissue composition defines which imaging modality is best suited for tissue examination. However it is subjective and varies between readers whereas AI techniques have been shown to remove subjectivity. We evaluate the use of state-of-the-art AI algorithms on a general whole-body noncontrast MRI to quantify the amount of fat versus nonfat tissue and compare with radiologists reports. Our results show significant correlation between the AI and radiologists' decisions. Further, we show on large dataset that the rate of replacement of nonfat fibroglandular tissue with fatty tissue is almost triple the rate in premenopausal women than postmenopausal women.
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