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 journalArticleAcademicpeer-review

55 Citations (Web of Science)

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. (C) 2020 The Authors. Published by Elsevier B.V.

Original languageEnglish
Pages (from-to)55-66
Number of pages12
JournalRadiotherapy and Oncology
Volume153
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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