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

2026-04-27

AI Summary

This study addresses the inefficiency and reliance on expert manual intervention in conventional karyotype analysis, which hinders its scalability for high-throughput clinical applications. To overcome these limitations, the authors present the first open-source desktop platform integrating a high-performance chromosome detection model. Built with Electron for an intuitive graphical user interface and leveraging ONNX Runtime for efficient deployment of deep learning models—including YOLOv11—the platform enables loading of pre-trained models, architectural comparisons, and interactive corrections without requiring command-line operations. Evaluated on the CRCN-NE dataset, YOLOv11 achieves a mAP@50 of 99.40%, reducing the analysis time per metaphase image to seconds. This advancement significantly enhances the usability and efficiency of AI-assisted cytogenetic analysis in practical settings.

Technology Category

• Machine Learning: Evaluation and Analysis
• Computer Vision: Large Vision Models
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI
• Web Mining and Content Analysis: Large pretrained models with web data

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

Problem

• chromosome detection
• karyotyping
• deep learning
• clinical workflow
• metaphase images

Innovation

• deep learning
• chromosome detection
• open-source platform
• AI-assisted cytogenetics
• graphical user interface