KAYRA: A Microservice Architecture for AI-Assisted Karyotyping with Cloud and On-Premise Deployment
2026-04-29 • Machine Learning
Machine LearningComputer Vision and Pattern Recognition
AI summaryⓘ
The authors developed KAYRA, a computer system designed to help analyze chromosomes in clinical labs. It uses several machine learning models working together to identify and classify chromosomes accurately. KAYRA can be run either on cloud servers or directly in a lab without sending data outside, which meets privacy needs. When tested on real samples, it performed better than some existing systems in identifying chromosome parts and classifying them. The system also supports human experts reviewing results, making it practical for actual medical use.
karyotypingmachine learningEfficientNetU-NetMask R-CNNmicroservice architecturechromosome classificationclinical cytogeneticscloud computinghuman-in-the-loop
Authors
Attila Pintér, Javier Rico, Attila Répai, Jalal Al-Afandi, Adrienn Éva Borsy, András Kozma, Hajnalka Andrikovics, György Cserey
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.