KAYRA用微服务架构实现可云端或本地部署的智能染色体分析，准确率显著优于传统方法。
KAYRA: A Microservice Architecture for AI-Assisted Karyotyping with Cloud and On-Premise Deployment
- 分阶段聚焦染色体区域，融合多模型提升检测精度
- 在459条染色体上实现98.91%分割准确率，优于商用系统
- 支持临床数据不出院，适合对隐私要求高的医院使用
我们提出KAYRA，一个面向临床细胞遗传实验室操作约束的端到端染色体核型分析系统。该系统采用容器化微服务架构，其机器学习组件结合EfficientNet-B5 + U-Net语义分割器、Mask R-CNN（ResNet-50 + FPN）实例检测器与ResNet-18分类器，通过级联的感兴趣区域缩小策略，使下游模型专注染色体区域。相同容器镜像既可用于云服务，也可部署于本地，满足患者数据不外传的临床环境需求。在10个中期分裂相共459条染色体的试点临床评估中，分割准确率达98.91%（对比商用系统78.21%/40.52%），分类准确率为89.1%（对比86.9%/54.5%），旋转准确率为89.76%（对比94.55%/78.43%）。KAYRA在所有三方面均显著优于旧版密度阈值法（p < 0.0001），分割性能也优于现代AI参考系统（p < 0.0001）；分类性能与现代AI参考系统无显著差异（p = 0.34）。系统已达技术成熟度6级，并集成人机协同专家复核流程，符合诊断实践要求。论文核心观点：多模型细胞遗传学AI服务可通过微服务架构灵活部署，兼具高性能与合规性。
原文摘要 · Abstract (English)
We present KAYRA, an end-to-end karyotyping system that operates inside the operational constraints of a clinical cytogenetic laboratory. KAYRA is architected as a containerized microservice pipeline whose ML stack combines an EfficientNet-B5 + U-Net semantic segmenter, a Mask R-CNN (ResNet-50 + FPN) instance detector, and a ResNet-18 classifier, orchestrated through a cascaded ROI-narrowing strategy that focuses each downstream model on the chromosome-bearing region. The same container images are deployed both as a cloud service and as an on-premise installation, supporting clinical environments where patient-data egress is not permitted as well as those where it is. A pilot clinical evaluation against two commercial reference karyotyping systems on 459 chromosomes from 10 metaphase spreads shows segmentation accuracy of 98.91 % (vs. 78.21 % / 40.52 %), classification accuracy of 89.1 % (vs. 86.9 % / 54.5 %), and rotation accuracy of 89.76 % (vs. 94.55 % / 78.43 %). KAYRA improves over the older density-thresholding reference on all three axes (p < 0.0001 for segmentation and classification by Fisher's exact test on chromosome-level counts), and on segmentation also against the modern AI- supported reference (p < 0.0001); on classification the difference vs. the modern AI reference is not statistically significant at the present test-set size (p = 0.34). The system reaches TRL 6 maturity and integrates the human-in-the-loop expert-review workflow that diagnostic cytogenetic practice requires. The thesis of this paper is that a multi-model cytogenetic AI service can be packaged as a microservice architecture supporting flexible deployment - cloud-hosted or on-premise - while delivering strong empirical performance on a pilot clinical evaluation.
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