Paper Overview
Field: Computer Vision Authors: Attila Pintér, Javier Rico, Attila Répai, Jalal Al-Afandi, Adrienn Éva Borsy, András Kozma, Hajnalka Andrikovics, György Cserey Published: 2026-04-29 arXiv: 2604.26869
Summary
KAYRA is an end-to-end karyotyping system designed to operate within the operational constraints of clinical cytogenetics laboratories. It is architected as a containerized microservice pipeline whose ML stack combines:
- EfficientNet-B5 + U-Net semantic segmenter
- Mask R-CNN (ResNet-50 + FPN) instance detector
- ResNet-18 classifier
- The system reaches TRL 6 maturity.
- It integrates a human-in-the-loop expert-review workflow, a requirement for diagnostic cytogenetics practice.
- Core argument of the paper: multi-model cytogenetics AI services can be packaged as a microservice architecture, enabling flexible cloud or on-premise deployment while delivering strong empirical performance in a pilot clinical evaluation.
These models are orchestrated through a cascaded ROI-narrowing strategy, allowing each downstream model to focus on chromosome-bearing regions.
The same container images support two deployment modes: as a cloud service and as an on-premise installation. The latter enables clinical environments where patient data must not leave the premises.
Pilot Clinical Evaluation Results
Evaluation on 459 chromosomes from 10 metaphase spreads, compared against two commercial reference karyotyping systems:
| Metric | KAYRA | Reference A | Reference B | |---|---|---|---| | Segmentation accuracy | 98.91% | 78.21% | 40.52% | | Classification accuracy | 89.1% | 86.9% | 54.5% | | Rotation accuracy | 89.76% | 94.55% | 78.43% |
KAYRA outperformed the older density-threshold reference on all three metrics (p < 0.0001), and outperformed the modern AI-supported reference on segmentation (p < 0.0001).
Key Takeaways
Original Abstract
> We present KAYRA, an end-to-end karyotyping system operating inside clinical cytogenetic laboratory constraints. KAYRA is architected as a containerized microservice pipeline combining EfficientNet-B5 + U-Net semantic segmenter, Mask R-CNN instance detector, and ResNet-18 classifier, orchestrated through cascaded ROI-narrowing. The same container images deploy both as cloud service and on-premise installation. A pilot clinical evaluation 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 reaches TRL 6 maturity and integrates human-in-the-loop expert-review workflow.
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