开发基于光学字符识别的质量控制过程,开发基于纸质的同意表格
Juyoung Lee1, Meehee Lee1, Hye Young Nam1
1Division of Biobank, National Biobank of Korea, National Institute of Health, Chungju Chungbuk-do, Korea.
Biopreservation and biobanking
|February 17, 2026
概括
这项研究开发了一种基于人工智能的光学字符识别 (OCR) 工具,用于自动处理基于纸张的同意表格,提高生物银行效率和同意管理的数据质量.
科学领域:
- 生物银行和数据管理数据管理
- 医疗保健中的人工智能
- 研究领域的数字化转型
背景情况:
- 有效的同意管理对于道德和法律的生物银行至关重要.
- 传统的基于纸张的同意表格在可读性和可扩展性方面存在挑战.
- 需要自动化阅读和质量控制同意表格.
研究的目的:
- 为了自动阅读和质量控制纸质同意表格.
- 为韩国手写同意文件开发可靠的光学字符识别 (OCR) 模型.
- 加强传统生物银行流程的数字化转型.
主要方法:
- 优化了一种专有光学字符识别 (OCR) 模型,用于手写的韩国字符.
- 为培训生成和使用合成标准和非标准的同意文件.
- 评估了模型在常规生物银行中的3790页同意表格上的表现.
主要成果:
- 优化的OCR模型在标准表单上达到88.94%的准确性,在非标准表单上达到91.88%的准确性.
- 应用于常规生物银行,该模型显示了91.25%的准确性和F1得分为0.91.1.
- 该模型表现出高性能和对同意数据的优秀概括能力.
结论:
- 开发了一种高效可靠的基于人工智能的OCR工具,用于基于纸张的同意管理.
- 这种方法促进了传统生物银行业的数字化转型.
- 优化的OCR模型增强了生物银行的同意文件处理.
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