# Development of an Artificial Intelligence-Based Chromosome Interpretation System for Amniotic Fluid Karyotyping

## TL;DR

This paper introduces an AI system that automates chromosome analysis in prenatal diagnosis, reducing manual labor and increasing efficiency.

## Contribution

A modular AI workflow is developed for automated chromosome interpretation in amniotic fluid karyotyping, achieving high accuracy.

## Key findings

- The AI system achieved high classification accuracy across training, validation, and testing cohorts.
- The overlap-recognition module effectively reduced errors in composite chromosome regions.
- The workflow successfully generated draft karyotypes from unsorted images with expert-level concordance.

## Abstract

Conventional G-banded karyotyping remains indispensable in prenatal diagnosis but continues to rely on labor-intensive, expertise-dependent visual examination. To address these challenges, we developed a modular artificial intelligence (AI) workflow that automates chromosome interpretation from amniotic fluid metaphase images. The system integrates image denoising, chromosome segmentation, overlap screening, and morphology-based classification, and was trained using 13,223 clinical cases comprising more than 50,000 manually annotated chromosomes. Across training, temporal validation, and independent testing cohorts, classification accuracy remained consistently high (97.45%, 96.95%, and 95.72%, respectively). The overlap-recognition module further reduced downstream errors by reliably identifying composite chromosome regions. When applied to unsorted metaphase images from a later clinical cohort, the workflow successfully generated draft karyotypes without manual sorting and maintained close concordance with expert review. These findings demonstrate that an AI-assisted pipeline can support cytogenetic laboratories by streamlining the most labor-intensive steps of karyotyping, potentially enhancing diagnostic efficiency while preserving interpretive reliability.