RadDeploy: A framework for integrating in-house developed software and artificial intelligence models seamlessly into radiotherapy workflows

Mathis Ersted Rasmussen*, Casper Dueholm Vestergaard, Jesper Folsted Kallehauge, Jintao Ren, Maiken Haislund Guldberg, Ole Nørrevang, Ulrik Vindelev Elstrøm, Stine Sofia Korreman

*Corresponding author af dette arbejde

Publikation: Bidrag til tidsskrift/Konferencebidrag i tidsskrift /Bidrag til avisTidsskriftartikelForskningpeer review

1 Citationer (Scopus)

Abstract

The use of and research in automation and artificial intelligence (AI) in radiotherapy is moving with incredible pace. Many innovations do, however, not make it into the clinic. One technical reason for this may be the lack of a platform to deploy such software into clinical practice. We suggest RadDeploy as a framework for integrating containerized software in clinical workflows outside of treatment planning systems. RadDeploy supports multiple DICOM as input for model containers and can run model containers asynchronously across GPUs and computers. This technical note summarizes the inner workings of RadDeploy and demonstrates three use-cases with varying complexity.

OriginalsprogEngelsk
Artikelnummer100607
TidsskriftPhysics and Imaging in Radiation Oncology
Vol/bind31
DOI
StatusUdgivet - jul. 2024

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