Artificial Intelligence for breast cancer detection: Technology, challenges, and prospects

Area of Science:

• Radiology
• Medical Imaging
• Artificial Intelligence

Background:

• Digital mammography and tomosynthesis are standard breast cancer screening tools.
• Artificial intelligence (AI) is emerging as a transformative technology in medical diagnostics.
• Automated detection systems aim to enhance the accuracy and efficiency of breast cancer screening.

Purpose of the Study:

• To review the current state of AI for automated breast cancer detection in digital mammography (DM) and digital breast tomosynthesis (DBT).
• To discuss AI technologies, available systems, and challenges in breast cancer screening.
• To explore the potential of AI in improving diagnostic accuracy and reducing workload.

Main Methods:

• Review of AI development in breast cancer detection, emphasizing deep learning (DL) techniques.
• Comparison of DL with traditional computer-aided detection (CAD) systems.
• Discussion of data pre-processing, learning paradigms, and validation strategies for AI systems.

Main Results:

• Deep learning-based AI systems demonstrate significant improvements in breast cancer detection accuracy.
• AI has the potential to reduce false negatives and positives, detecting subtle abnormalities.
• Challenges include lack of standardized datasets, potential data bias, and regulatory hurdles.

Conclusions:

• AI can enhance breast cancer screening accuracy and reduce radiologist workload.
• DL-based AI shows promise in improving detection performance and observer consistency.
• Standardized guidelines, trustworthy AI practices, and further validation are essential for clinical adoption.