RadDeploy: A Framework for Integrating In-house Developed Software and Artificial Intelligence Models Seamlessly into Radiotherapy Workflows

Overview

Journal Phys Imaging Radiat Oncol

Specialty Oncology

Date 2024 Jul 29

PMID 39071159

Authors

Mathis Ersted Rasmussen

Casper Dueholm Vestergaard

Jesper Folsted Kallehauge

Jintao Ren

Maiken Haislund Guldberg

Ole Norrevang

Ulrik Vindelev Elstrom

Stine Sofia Korreman

Affiliations

Soon will be listed here.

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.

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