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基于深度学习的兴奋剂药物文本识别系统的验证研究,以确保运动员安全使用药物
Sang-Yong Lee1, Jae-Hyeon Park1, Jiwun Yoon1
1Center for Sports and Performance Analysis, Korea National Sport University, Seoul 05541, Republic of Korea.
Healthcare (Basel, Switzerland)
|June 28, 2023
概括
这项研究开发了一个人工智能驱动的系统,使用光学字符识别 (OCR) 识别兴奋剂药物. 该系统准确地识别禁止物质,帮助运动员保持公平竞争.
科学领域:
- 运动科学 运动科学 运动科学
- 分析化学 分析化学
- 人工智能的人工智能
背景情况:
- 体育运动中的兴奋剂对公平竞争和运动员健康构成重大威胁.
- 现有的识别禁止物质的方法可能很复杂,运动员无法使用.
- 需要一个用户友好的工具来帮助运动员验证药物含量.
研究的目的:
- 开发和验证一种英语系统,使用基于深度学习的光学字符识别 (OCR) 识别兴奋剂药物.
- 创建一个通过智能手机和网站访问的混合系统,让运动员检查是否有被禁止的物质.
- 评估开发的药物识别系统的准确性和有效性.
主要方法:
- 从WADA和韩国制药资源中编制了336种禁止物质的数据库.
- 开发了一个利用Tesseract OCR模型的混合系统,将886张药物物质图像的数据增强纳入其中.
- 该系统使用886张图像进行了评估,包括处方和标签,以确定准确性,灵敏性和特异性.
主要成果:
- 该系统表现出高的字符识别准确度 (98.3%),在5470个单词中正确提取了5379个.
- 对物质图像的分类准确度很高:可接受的100%;禁止物质的95.8%.
- 验证性分析证实了高性能,精度为 (0.95),灵敏度为 (1.00) 和特异性为 (0.93).
结论:
- 开发的基于OCR的系统有效地识别了被禁止的兴奋剂物质,具有高准确性和有效性.
- 这个工具可以使运动员能够快速验证药物,为公平健康的体育环境做出贡献.
- 该系统为寻求避免无意中违反兴奋剂规则的运动员提供了切实可行的解决方案.
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