IODeep:用于在DICOM标准中引入深度学习的IOD
Salvatore Contino1, Luca Cruciata1, Orazio Gambino1
1Department of Engineering, University of Palermo, Palermo, 90128, Sicily, Italy.
Computer methods and programs in biomedicine
|March 13, 2024
概括
IODeep是一个新的DICOM信息对象定义,将训练有素的人工智能 (AI) 模型集成到临床工作流程中. 这使得能够为放射学量身定制的人工智能,改善医生决策和诊断准确度.
科学领域:
- 生物医学成像技术 生物医学成像技术
- 人工智能在医学中的应用
- 医疗信息学 医疗信息学
背景情况:
- 深度神经网络 (DNN) 在生物医学图像细分方面表现有前途,但在临床实践中未得到充分利用.
- 整合到诊断工作流程和可解释性是AI在医疗保健采用的关键障碍.
- 标准化对于在临床环境中有效使用人工智能工具至关重要.
研究的目的:
- 推出IODeep,一个新的DICOM信息对象定义 (IOD) 用于存储训练的DNN模型.
- 为了促进人工智能模型在现有的图片存档和通信系统 (PACS) 中无集成.
- 支持将人工智能模型定制为特定的临床数据,并改善诊断决策.
主要方法:
- 开发了IODeep来存储DNN架构和权重,与图像采集细节 (模式,解剖,疾病) 相关联.
- 实现了基于元数据标签的PACS检索的DNN选择算法.
- 创建了一个PACS查看器,用于在不修改PACS的情况下展示DICOM集成.
主要成果:
- IODeep使训练有素的人工智能模型在DICOM基础设施内完全集成.
- 证明了有效的DICOM集成与专门的PACS查看器.
- 开发了一个基于服务的架构,支持AI模型集成工作流.
结论:
- IODeep促进训练有素的人工智能模型与DICOM环境的集成.
- 支持与当地医院数据或跨机构联合学习微调模型.
- 允许人工智能模型适应现实世界放射学数据,增强医生决策支持.
相关概念视频
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01:14Imaging Studies I: CT and MRI
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Computed Tomography (CT) scan:
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Computed Tomography (CT) scan:
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01:27Imaging Studies III: Computed Tomography
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