IJMS, Vol. 27, Pages 1746: Development of an Artificial Intelligence-Based Chromosome Interpretation System for Amniotic Fluid Karyotyping

International Journal of Molecular Sciences doi: 10.3390/ijms27041746

Authors:
Kuan-Han Wu
Hsuan-Wei Huang
Chia Yun Lin
Hsu-Tung Huang
Tzuo-Yau Fan
Yueh-Peng Chen
Yung-Chiao Chang
Te-Yao Hsu
Kuo-Chung Lan

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.

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Kuan-Han Wu www.mdpi.com