Course Description
Mammography is the cornerstone of breast cancer screening and has made a major contribution to reducing disease-related mortality. Many interval cancers are missed, however, and a large portion of these are found retrospectively to have been detectable. Although offering superior accuracy compared with mammography, three-dimensional digital breast tomosynthesis (DBT), also has a number of shortcomings, including longer reading times and differences in cancer conspicuity by mammographic view. Traditional computer-aided detection, now a mainstay of radiology practices, sought to overcome these problems, but its clinical value remains a subject of debate. Artificial intelligence (AI), the use of computer-based technologies to develop and exploit algorithms that capture cognitive human behavior and processes, has been proposed as a means to overcome all of these deficiencies. Recent advances in AI, which involve more sophisticated algorithms and more powerful computers, have the potential to identify essential and visually undetectable anatomic features and then, via machine-learning models and deep neural networks, assist radiology professionals in assessing breast cancer risk, identifying true-positive and true-negative findings on mammography and DBT, determining treatment response, and predicting the risk of recurrence or metastasis. Emerging evidence confirms the utility of AI algorithms in the evaluation of mammographic acquisitions and in improving workflow, reducing costs, and optimizing the quality of patient care.

This course will investigate the emerging place of AI in mammography. It will begin with a discussion of the basic terms and techniques found in the most common AI models and methods of validation, evaluation, and interpretation. It will focus on the key fields and subfields, the various types of learning processes, and the technical challenges in mammography. The primary clinical applications will be examined along with AI's impact on cancer detection rates, risk stratification, and its intersection with another emerging computer-based approach, radiomics. The intersection of AI and DBT also will appraised. Finally, the challenges and limitations of AI in mammography will be described as will its future prospects including the adoption of AI-based tools in community clinical practice.

Learning Objectives
After completing this course, the participant should be able to:

• DEFINE the current terminology used to describe artificial intelligence (AI) and computer-aided diagnosis (CAD) as they apply to the interpretation of full-field mammograms and digital breast tomosynthetic (DBT) images.
• APPRAISE the several AI models and techniques of validation, evaluation, and interpretation.
• SUMMARIZE the primary clinical applications of deep-learning and CAD techniques in breast imaging and their role in cancer detection, recall rates, and risk stratification.
• EXPLAIN how the intersection of AI and radiomics can better classify tumors and improve prognosis and treatment.
• ASSESS the strengths and weaknesses of current AI approaches and future directions in the application of AI systems to breast imaging.

 Categories: Mammography, Digital Radiography, Technology

CE Information

In order to receive CE credit, you must first complete the activity content. When completed, go to the "Take CE Test!" link to access the post-test.

Submit the completed answers to determine if you have passed the post-test assessment. You must answer 14 out of 18 questions correctly to receive the CE credit. You will have no more than 3 attempts to successfully complete the post-test.

Participants successfully completing the activity content and passing the post-test will receive 2.25 ARRT Category A credits.

Approved by the American Society of Radiologic Technologists for ARRT Category A credit.

Approved by the state of Florida for ARRT Category A credit.

Texas direct credit.

This activity may be available in multiple formats or from different sponsors. ARRT does not allow CE activities such as Internet courses, home study programs, or directed readings to be repeated for CE credit in the same biennium.