A Review of Artificial Intelligence in Brachytherapy

TL;DR

This review explores how artificial intelligence, especially machine learning and deep learning, is transforming brachytherapy by improving personalization, efficiency, and outcomes across various clinical tasks.

Contribution

It systematically categorizes AI applications in brachytherapy, providing detailed summaries of models, data, and results, and discusses current challenges and future directions.

Findings

AI enhances treatment personalization and efficiency.

AI models improve outcome prediction accuracy.

AI integration faces challenges like data quality and clinical validation.

Abstract

Artificial intelligence (AI) has the potential to revolutionize brachytherapy's clinical workflow. This review comprehensively examines the application of AI, focusing on machine learning and deep learning, in facilitating various aspects of brachytherapy. We analyze AI's role in making brachytherapy treatments more personalized, efficient, and effective. The applications are systematically categorized into seven categories: imaging, preplanning, treatment planning, applicator reconstruction, quality assurance, outcome prediction, and real-time monitoring. Each major category is further subdivided based on cancer type or specific tasks, with detailed summaries of models, data sizes, and results presented in corresponding tables. This review offers insights into the current advancements, challenges, and the impact of AI on treatment paradigms, encouraging further research to expand its clinical utility.