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This paper introduces Aycromo, an open-source, user-friendly desktop platform for AI-assisted cytogenetic analysis that integrates pre-trained deep learning models for chromosome detection. The platform, built with Electron and ONNX Runtime, features a benchmarking module for comparing model architectures and an interactive annotation interface for manual correction. Experiments using YOLOv11 on the CRCN-NE dataset demonstrate a 99.40% mAP@50, with the platform reducing per-slide analysis time to seconds.
Cytogeneticists can now slash chromosome analysis time from days to seconds with Aycromo, an open-source platform that democratizes access to high-performance deep learning models.
Chromosome analysis is a fundamental step in the diagnosis of genetic diseases, but the manual karyotyping workflow is time-consuming and heavily dependent on expert specialists, often requiring several days per patient. Although Deep Learning models have achieved high performance in chromosome detection, most proposed solutions remain restricted to research prototypes or lack graphical interfaces suitable for clinical use. In this work, we present Aycromo, an open-source desktop platform for AI-assisted cytogenetic analysis. Built on Electron and ONNX Runtime, the tool allows cytogeneticists to load pre-trained models, compare architectures through an integrated benchmarking module, and manually correct detections via an interactive annotation interface, all without command-line interaction. Preliminary experiments on metaphase images from the CRCN-NE dataset demonstrate that YOLOv11 achieves 99.40% mAP@50, while the platform reduces per-slide analysis to seconds
