使用人工智能光学字符识别的SPECT质量保证的自动趋势和分析
Shanli Ding1, Rachel M Barbee1, Osama Mawlawi1
1The University of Texas MD Anderson Cancer Houston, Houston, Texas, USA.
Medical physics
|September 4, 2025
概括
一个新的核医学质量保证 (NMQA) 服务器与人工智能深度学习 (AIDL) 光学字符识别 (OCR) 自动化了SPECT和马相机质量保证数据,大大减少了质量保证的时间和错误.
科学领域:
- 医学成像
- 核医学技术
- 医疗保健中的人工智能
背景情况:
- 高质量的患者扫描需要对SPECT和马相机进行严格的质量保证 (QA).
- 质量保证的重要组成部分包括绩效评估,数据审查和详细的文档.
研究的目的:
- 开发一个与AI深度学习 (AIDL) 光学字符识别 (OCR) 系统集成的新型核医学质量保证 (NMQA) 服务器.
- 自动检索和审查来自SPECT和马摄像头的质量保证数据,提高医院网络的效率,趋势,存储和审计.
主要方法:
- 在Linux上使用Python,DCMTK,Pydicom和MySQL实现NMQA服务器,用于数据管理和趋势.
- 在三个阶段开发了AIDL OCR系统:特征提取,序列标记和转录,并进行ROI提取和字符识别.
- 与其他四个OCR系统相比,AIDL的OCR准确性和速度使用了洪水和COR图像的质量保证数据集.
主要成果:
- NMQA服务器自动查询质量保证数据,将审查时间从60分钟缩短到几分钟.
- 与其他OCR相比,AIDL OCR显示出更高的速度 (0.3秒/图像) 和精度 (93.53%),在特定字体的数字值中达到99.9%的精度.
- 该系统成功建立了一个质量保证数据库,用于趋势和分析,最大限度地减少排版错误.
结论:
- 通过NMQA服务器自动查询和审查质量管理数据,显著提高效率并减少错误.
- 它可以全面分析和审计质量保证数据,这对于核医学的质量保证和改进至关重要.
- AIDL OCR 组件提供了准确和快速的质量保证数据识别,提高了系统的整体性能.
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