RadDeploy：将内部开发的软件和人工智能模型无缝集成到放射治疗工作流程中的框架

IF 3.4 Q2 ONCOLOGY

Physics and Imaging in Radiation Oncology Pub Date : 2024-07-01 DOI:10.1016/j.phro.2024.100607

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

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引用次数: 0

摘要

自动化和人工智能（AI）在放射治疗中的应用和研究正以惊人的速度发展。然而，许多创新并未进入临床。其中一个技术原因可能是缺乏将此类软件部署到临床实践中的平台。我们建议将 RadDeploy 作为一个框架，将容器化软件集成到治疗计划系统之外的临床工作流程中。RadDeploy 支持将多个 DICOM 作为模型容器的输入，并能在 GPU 和计算机上异步运行模型容器。本技术说明总结了 RadDeploy 的内部工作原理，并演示了三个具有不同复杂性的用例。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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

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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来源期刊

Physics and Imaging in Radiation Oncology Physics and Astronomy-Radiation

CiteScore

5.30

自引率

18.90%

发文量

审稿时长

6 weeks