Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance

Liesbeth Vandewinckele, Michael Claessens, Anna Dinkla*, Charlotte Brouwer, Wouter Crijns, Dirk Verellen, Wouter van Elmpt

*Corresponding author for this work

    Research output: Contribution to journalReview articleAcademicpeer-review

    25 Citations (Scopus)
    21 Downloads (Pure)

    Abstract

    Artificial Intelligence (AI) is currently being introduced into different domains, including medicine. Specifically in radiation oncology, machine learning models allow automation and optimization of the workflow. A lack of knowledge and interpretation of these AI models can hold back wide-spread and full deployment into clinical practice. To facilitate the integration of AI models in the radiotherapy workflow, generally applicable recommendations on implementation and quality assurance (QA) of AI models are presented. For commonly used applications in radiotherapy such as auto-segmentation, automated treatment planning and synthetic computed tomography (sCT) the basic concepts are discussed in depth. Emphasis is put on the commissioning, implementation and case-specific and routine QA of AI models needed for a methodical introduction in clinical practice.

    Original languageEnglish
    Pages (from-to)55-66
    Number of pages12
    JournalRadiotherapy and Oncology
    Volume153
    Early online date10-Sep-2020
    DOIs
    Publication statusPublished - Dec-2020

    Keywords

    • Artificial intelligence
    • Radiotherapy
    • Commissioning
    • Quality assurance
    • Auto-contouring
    • Treatment planning
    • CONVOLUTIONAL NEURAL-NETWORK
    • MODULATED ARC THERAPY
    • RADIATION-THERAPY
    • AT-RISK
    • OPTIMIZATION ENGINE
    • ACCURATE PREDICTION
    • PLAN QUALITY
    • BIG DATA
    • HEAD
    • NECK

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