Clinical Application of Artificial Intelligence in Digital Breast Tomosynthesis

Area of Science:

• Radiology
• Medical Imaging
• Artificial Intelligence

Background:

• Digital breast tomosynthesis (DBT) offers superior cancer detection and reduced recall rates compared to digital mammography (DM).
• Widespread adoption of DBT for breast cancer screening is challenged by increased image volume and longer radiologist interpretation times.
• Artificial intelligence (AI) presents a significant opportunity to address DBT's limitations and enhance its benefits.

Purpose of the Study:

• To review the current state of AI technology in Digital Breast Tomosynthesis (DBT).
• To explore the clinical applications of AI in DBT for breast cancer screening and diagnosis.
• To discuss future considerations for AI implementation in DBT workflows.

Main Methods:

• Literature review of AI applications in DBT.
• Analysis of AI's role in screening and diagnostic phases of breast cancer detection.
• Examination of AI's impact on workflow efficiency and image interpretation.

Main Results:

• AI algorithms are crucial for breast cancer detection and characterization within DBT.
• AI can streamline radiologist workflows, reducing interpretation times.
• AI may also minimize radiation exposure and improve lesion visibility in synthetic 2D DM images.

Conclusions:

• AI holds substantial promise for optimizing DBT in breast cancer screening.
• AI integration can enhance diagnostic accuracy and operational efficiency in mammography.
• Further research and development are needed to fully realize AI's potential in DBT.