AI Impact On Mammograms, Breast Health & The OBGYN Field

Artificial intelligence (AI) has invaded our daily lives, and in the last decade, there have been very promising applications of AI in the field of medicine, including medical imaging, in vitro diagnosis, intelligent rehabilitation, Gynecology and prognosis. Artificial intelligence (AI) is commonly defined as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation”.

Over the past 50 years, the dramatic growth of computer functions related to big data intrusion has pushed AI applications into new areas.  Breast cancer is one of the common malignant tumors in women and seriously threatens women’s physical and mental health. Early screening for breast cancer via mammography, ultrasound and magnetic resonance imaging (MRI) can significantly improve the prognosis of patients. AI has shown excellent performance in image recognition tasks and has been widely studied in breast cancer screening. This paper introduces the background of AI and its application in breast medical imaging (mammography, ultrasound and MRI), such as in the identification, segmentation and classification of lesions; breast density assessment; and breast cancer risk assessment. In addition, we also discuss the challenges and future perspectives of the application of AI in medical imaging of the breast.

Mammography, a breast exam, is one of the most widely used methods for breast cancer screening. Mammography is a non-invasive detection method associated with relatively low pain, easy operation, high resolution, and good repeatability. The retained image can be compared before and after and is not limited by age or body shape. Mammography can detect breast masses that cannot be palpated by doctors and can reliably identify benign lesions and malignant tumors of the breast. Mammograms are currently acquired with full-field digital mammography (DM) systems and are provided in both for-processing (the raw imaging data) and for-presentation. To date, AI has been used to analyze mammography images in most studies mainly for the detection and classification of breast mass and microcalcifications, breast mass segmentation, breast density assessment, breast cancer risk assessment and image quality improvement.

Breast Cancer Risk Assessment

The high incidence and mortality of breast cancer are seriously threatening women’s physical and mental health. At present, there are many known risk factors for breast cancer.  These include aging, family history, reproductive factors (early menarche, late menopause, late age at first pregnancy and low parity), estrogen (endogenous and exogenous estrogens), lifestyle (excessive alcohol consumption, too much dietary fat intake, smoking) are all risk factors for breast cancer.  The early detection and prevention of breast cancer can be promoted by increasing the overall understanding and recognition of breast cancer risk.

Relevant literature shows that the research of AI in breast cancer risk prediction is also very extensive.  As a result, AI predicts breast cancer risk with higher accuracy than other methods, which in turn helps physicians guide high-risk populations to conduct appropriate interventions to reduce the incidence of breast cancer.

Contributed with help from our Glendale Register & Queens Ledger Featured Gynecologist: EMU Breast Surgery, Exams & Mammograms Center of Queens 8340 Woodhaven Blvd Ste 6, Queens, NY 11385 (929) 299-6124

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