Aycromo: An Open-Source Platform for Automatic Chromosome Detection in Metaphase Images Based on Deep Learning

TL;DR

Aycromo is an open-source desktop platform that streamlines chromosome detection in metaphase images using deep learning, offering user-friendly features for clinical cytogenetic analysis.

Contribution

It introduces a comprehensive, user-friendly platform integrating deep learning models, benchmarking, and manual correction for chromosome detection in a clinical setting.

Findings

YOLOv11 achieves 99.40% mAP@50 on the CRCN-NE dataset.

The platform reduces analysis time per slide to seconds.

Aycromo facilitates AI-assisted cytogenetic analysis without command-line use.

Abstract

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