AI-Driven Fluorescence Peak Analysis for Chromosomal Aneuploidy Detection: A Python-Based Machine Learning Approach for Enhanced Accuracy and Efficiency

Abstract

Chromosomal aneuploidy, a condition characterized by an abnormal number of chromosomes, is a major genetic disorder affecting human reproduction, leading to infertility, pregnancy loss, and developmental disabilities. Trisomies of chromosomes 13, 18, and 21 result in Patau, Edwards, and Down syndromes, respectively. While conventional methods like karyotyping and QF-PCR facilitate aneuploidy detection, they are often time-consuming and limited by genetic polymorphism variability. This study introduces an advanced AI-driven approach integrating segmental duplication-based fluorescence probe analysis with machine learning for efficient and accurate aneuploidy detection. Amniotic fluid samples were collected from pregnant mothers, and DNA was extracted for QF-PCR amplification of segmental duplications on target chromosomes. Fluorescence intensity data were analyzed using a Python-based computational pipeline employing an XGBoost classifier trained on 80% of the dataset and tested on the remaining 20%. The model demonstrated high accuracy in detecting trisomies 13, 18, and 21, with results validated against conventional karyotyping as the gold standard. Furthermore, the AI-based approach successfully predicted fetal gender by computing fluorescence intensity ratios of X and Y chromosomes relative to reference chromosomes. The automated method significantly reduced analysis time from 45 minutes (manual interpretation) to 1.7 seconds while minimizing human errors. The integration of two reference chromosomes for fluorescence normalization improved diagnostic precision, reducing false positives and negatives. This study highlights the potential of AI-enhanced QF-PCR analysis for rapid and reliable prenatal aneuploidy screening, paving the way for its implementation in clinical diagnostics to enhance reproductive healthcare outcomes.