Background: Flow cytometric detection of minimal residual disease (MRD) in chronic lymphocytic leukemia (CLL) is complex, time-consuming, and subject to inter-operator variability. Deep neural networks (DNNs) offer potential for standardization and efficiency improvement, but require rigorous validation and monitoring for safe clinical implementation.Methods: We evaluated a DNN-assisted human-in-the-loop approach for CLL MRD detection. Initial validation included method comparison against manual analysis (n = 240), precision studies, and analytical sensitivity verification. Post-implementation monitoring comprised four components: daily electronic quality control, input data drift detection, error analysis, and attribute acceptance sampling. Laboratory efficiency was assessed through a timing study of 161 cases analyzed by five technologists.Results: Method comparison demonstrated 97.5% concordance with manual analysis for qualitative classification (sensitivity 100%, specificity 95%) and excellent correlation for quantitative assessment (r = 0.99, Deming slope = 0.99). Precision studies confirmed high repeatability and within-laboratory precision across multiple operators. Analytical sensitivity was verified at 0.002% MRD. Post-implementation monitoring identified 2.97% of cases (26/874) with input data drift, primarily high-burden CLL and non-CLL neoplasms. Error analysis showed the DNN alone achieved 97% sensitivity compared to human-in-the-loop-reviewed results, with 13 missed cases (1.5%) showing atypical immunophenotypes. Attribute acceptance sampling confirmed 98.8% of reported negative cases were true negatives. The DNN-assisted workflow reduced average analysis time by 60.3% compared to manual analysis (4.2 ± 2.3 vs. 10.5 ± 5.8 min).Conclusions: The implementation of a DNN-assisted approach for CLL MRD detection in a clinical laboratory provides diagnostic performance equivalent to expert manual analysis while substantially reducing analysis time. Comprehensive performance monitoring ensures ongoing safety and effectiveness in routine clinical practice. This approach provides a model for responsible AI integration in clinical laboratories, balancing automation benefits with expert oversight.
背景：慢性淋巴细胞白血病（CLL）微小残留病（MRD）的流式细胞术检测过程复杂、耗时且易受操作者间差异影响。深度神经网络（DNN）为实现标准化与效率提升提供了可能，但其临床安全应用需经过严格验证与持续监测。 方法：我们评估了一种DNN辅助的人机协同方法用于CLL MRD检测。初步验证包括与人工分析方法对比（n=240）、精密度研究及分析灵敏度验证。实施后监测包含四个组成部分：每日电子质控、输入数据漂移检测、错误分析及属性验收抽样。通过五位技术人员对161例样本的分析耗时研究评估实验室效率。 结果：方法学比较显示，在定性分类方面与人工分析的一致性达97.5%（灵敏度100%，特异性95%），定量评估呈现极佳相关性（r=0.99，Deming斜率=0.99）。精密度研究证实该方法在不同操作者间具有高度重复性和实验室内精密度。分析灵敏度验证为0.002% MRD水平。实施后监测发现2.97%的病例（26/874）存在输入数据漂移，主要为高负荷CLL及非CLL肿瘤。错误分析显示，相较于经人机协同复核的结果，单独使用DNN的灵敏度达97%，漏检的13例（1.5%）均呈现非典型免疫表型。属性验收抽样确认98.8%的报告阴性病例为真阴性。与人工分析相比，DNN辅助工作流程使平均分析时间减少60.3%（4.2±2.3分钟 vs 10.5±5.8分钟）。 结论：在临床实验室实施DNN辅助的CLL MRD检测方法，在保持与专家人工分析等效诊断性能的同时，显著缩短了分析时间。全面的性能监测确保了该方法在常规临床实践中的持续安全性与有效性。这种在自动化优势与专家监督之间取得平衡的方法，为临床实验室负责任地整合人工智能提供了可借鉴的模式。
肿瘤(癌症)患者之家