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对AI-PBRTQC和传统的PBRTQC模型在识别质量风险方面的质量控制有效性的比较研究
Xucai Dong1, Xi Meng1, Bin Li1
1Xi'an Area Medical Laboratory Center, Xi'an, Shaanxi, P.R. China.
Biochemia medica
|June 17, 2024
概括
与传统方法相比,人工智能基于患者的实时质量控制 (AI-PBRTQC) 在识别实验室质量风险方面表现出更高的效率. 这种人工智能方法即使在患者样本有限的情况下也能有效地检测出问题,从而提高临床实验室整体质量控制.
科学领域:
- 临床化学 临床化学
- 实验室医学 实验室医学
- 医疗保健中的人工智能
背景情况:
- 基于患者的实时质量控制 (PBRTQC) 对于保持实验室准确性至关重要.
- 传统的PBRTQC方法的效率和检测范围可能受到限制.
- 人工智能的整合为质量控制过程提供了潜在的进步.
研究的目的:
- 将AI-PBRTQC的质量控制效率与传统的PBRTQC进行比较.
- 在临床环境中建立PBRTQC更广泛应用的条件.
- 评估AI-PBRTQC在识别各种质量风险方面的有效性.
主要方法:
- 在五个月内收集了五种常见分析剂 (TT4,AMH,ALT,TC,尿素,ALB) 的数据.
- 成立了两组:AI-PBRTQC和传统的PBRTQC.
- AI-PBRTQC使用智能截断范围选择,而传统的PBRTQC使用Box-Cox转换.
- 验证模型评估了错误检测,错误阳性/负率和样本检测时间.
主要成果:
- 对每个分析物来说,确定了AI-PBRTQC的最佳参数设置.
- 与传统的PBRTQC相比,AI-PBRTQC在识别质量风险方面表现出更高的效率.
- AI-PBRTQC有效地检测出质量风险,即使是少数患者样本.
结论:
- 在检测实验室质量风险方面,AI-PBRTQC比传统的PBRTQC更有效.
- AI-PBRTQC对生物化学和免疫学分析剂都有效.
- AI-PBRTQC可以识别特定的质量风险,包括试剂校准,机上时间和品牌变化.
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