预期部署一个自动化实现解决方案的人工智能翻译临床辐射瘤学
Christopher E Kehayias1, Yujie Yan1, Dennis Bontempi2,3
1Department of Radiation Oncology, Brigham and Women's Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States.
Frontiers in oncology
|January 19, 2024
概括
深度学习按需助理 (DL-ODA) 平台将人工智能 (AI) 集成到放射性瘤学工作流程中. 这个自动化系统提供了人工智能驱动的细分和质量保证,提高了治疗计划的效率和可靠性.
科学领域:
- 辐射瘤学 辐射瘤学
- 医疗成像医学成像
- 人工智能的人工智能
背景情况:
- 辐射瘤学的人工智能辅助工具在临床翻译方面面临着挑战.
- 对于各种疾病场所的自动化,端到端的人工智能平台存在需求.
研究的目的:
- 引入深度学习按需助理 (DL-ODA),这是一个用于辐射瘤学的AI干预的新型自动化临床平台.
- 展示DL-ODA在自动化模型培训,细分和质量保证 (QA) 的能力.
主要方法:
- 该DL-ODA系统的开发,测试,并从前期部署在一所大学医院.
- 建立了两个激活途径:即时人工智能决策支持的On-Demand和放射治疗计划的日常质量保证的Ambient.
- 训练了一种新的乳房细分模型,并对肺和脊柱放射治疗病例进行了回顾性评估.
主要成果:
- 乳房细分模型的训练在一天内完成,产生了临床上可行的轮.
- DL-ODA在心脏,肺,肝脏和脏等器官的划分方面表现出高度相似性.
- 未来环境QA处理了超过2700个心脏和脊柱部位的病例,自动提醒员工潜在的问题.
结论:
- DL-ODA平台成功地集成到一个大型的学术放射治疗部门中,展示了通用的AI干预能力.
- 多模式,自动化的DL-ODA代表着向可通用AI框架的重大进步,以提高放射治疗的效率和可靠性.
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