KAYRA: A Microservice Architecture for AI-Assisted Karyotyping with Cloud and On-Premise Deployment

2026-04-29

AI Summary

This study addresses the limitations of conventional karyotype analysis—namely, low efficiency, insufficient automation, and challenges in balancing privacy preservation with flexible clinical deployment. The authors propose the first end-to-end, containerized microservice-based AI-assisted karyotyping system, integrating EfficientNet-B5 with U-Net for semantic segmentation, Mask R-CNN for instance detection, and a ResNet-18 classifier. Innovatively, the system employs a cascaded region-of-interest (ROI) focusing strategy and a human-in-the-loop review workflow, enabling dual-mode deployment on both cloud and local infrastructure. Evaluated on 459 chromosomes, the system achieves a segmentation accuracy of 98.91%, with classification and orientation accuracies of 89.1% and 89.76%, respectively—significantly outperforming traditional methods and existing AI approaches—and attains Technology Readiness Level (TRL) 6.

Abstract

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.

Problem

• karyotyping
• microservice architecture
• AI-assisted diagnosis
• clinical cytogenetics
• flexible deployment

Innovation

• microservice architecture
• cascaded ROI-narrowing
• AI-assisted karyotyping
• cloud/on-premise deployment
• multi-model cytogenetic AI