Reseach Article
AI-Driven Fluorescence Peak Analysis for Chromosomal Aneuploidy Detection: A Python-Based Machine Learning Approach for Enhanced Accuracy and Efficiency
| Journal of Advanced Artificial Intelligence |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 1 - Number 7 |
| Year of Publication: 2025 |
| Authors: Krishna H. Goyani, Daisy Patel, Isha Sharma, Shalin Vaniawala, Pratap N. Mukhopadhyaya |
10.5120/jaai202431
|
Krishna H. Goyani, Daisy Patel, Isha Sharma, Shalin Vaniawala, Pratap N. Mukhopadhyaya . AI-Driven Fluorescence Peak Analysis for Chromosomal Aneuploidy Detection: A Python-Based Machine Learning Approach for Enhanced Accuracy and Efficiency. Journal of Advanced Artificial Intelligence. 1, 7 ( Apr 2025), 1-6. DOI=10.5120/jaai202431
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.
References
- Harton, G. L., & Tempest, H. G. (2011). Chromosomal disorders and male infertility. Asian Journal of Andrology, 14(1), 32–39. https://doi.org/10.1038/aja.2011.66
- Hassold, T., Abruzzo, M., Adkins, K., Griffin, D., Merrill, M., Millie, E., Saker, D., Shen, J., & Zaragoza, M. (1996). Human aneuploidy: Incidence, origin, and etiology. Environmental and Molecular Mutagenesis, 28(3), 167–175. https://doi.org/10.1002/(sici)1098-2280(1996)28:33.0.co;2-b
- Biancotti, J.-C., Narwani, K., Buehler, N., Yanuka, O., Lavon, N., Mandefro, B., Hill, D., Golan-Lev, T., Benvenisty, N., & Clark, A. (2010). Human embryonic stem cells as models for aneuploid chromosomal syndromes. Stem Cells, 28(9), 1530–1540. https://doi.org/10.1002/stem.483
- Vasudevan, A., Sausville, E. L., Schukken, K. M., Adebambo, O. A., Sheltzer, J. M., & Girish, V. (2021). Aneuploidy as a promoter and suppressor of malignant growth. Nature Reviews Cancer, 21(2), 89–103. https://doi.org/10.1038/s41568-020-00321-1
- Altug-Teber, Ö., Riess, O., Stappert, H., Mau-Holzmann, U. A., Dufke, A., Heilbronner, H., Walter, M., Tekesin, I., Bonin, M., & Nieselt, K. (2008). Specific transcriptional changes in human fetuses with autosomal trisomies. Cytogenetic and Genome Research, 119(3–4), 171–184. https://doi.org/10.1159/000112058
- Hwang, S., Cavaliere, P., Li, R., Zhu, L. J., Dephoure, N., & Torres, E. M. (2021). Consequences of aneuploidy in human fibroblasts with trisomy 21. Proceedings of the National Academy of Sciences, 118(6). https://doi.org/10.1073/pnas.2014723118
- Atef, S. H., Hafez, S., Helmy, S., & Helmy, N. (2011). QF-PCR as a rapid technique for routine prenatal diagnosis of fetal aneuploidies. Pediatric Research, 70(4), 412. https://doi.org/10.1038/pr.2011.938
- Slater, H. R., Bruno, D. L., Ren, H., Pertile, M., Schouten, J. P., & others. (2003). Rapid, high-throughput prenatal detection of aneuploidy using a novel quantitative method (MLPA). Journal of Medical Genetics, 40(12), 907–912. https://doi.org/10.1136/jmg.40.12.907
- Dudarewicz, L., Holzgreve, W., Jeziorowska, A., Jakubowski, L., & Zimmermann, B. (2005). Molecular methods for rapid detection of aneuploidy. Journal of Applied Genetics, 46(2), 207–215. https://doi.org/10.1007/BF03194525
- Boormans, E. M., Birnie, E., Oepkes, D., Galjaard, R. J., Schuring-Blom, G. H., & others. (2010). Comparison of multiplex ligation-dependent probe amplification and karyotyping in prenatal diagnosis. Obstetrics & Gynecology, 115(2), 297–303. https://doi.org/10.1097/AOG.0b013e3181cbd5ac
- Willis, A. S., Veyver, I., & Eng, C. M. (2012). Multiplex ligation-dependent probe amplification (MLPA) and prenatal diagnosis. Prenatal Diagnosis, 32(4), 315–320. https://doi.org/10.1002/pd.12345
- Bailey, J. A., Reinert, K., Myers, E. W., Eichler, E. E., Gu, Z., Samonte, R. V., Schwartz, S., Clark, R. A., Adams, M. D., & Li, P. W. (2002). Recent segmental duplications in the human genome. Science, 297(5583), 1003–1007. https://doi.org/10.1126/science.1072047
- Kong, X., Li, L., Sun, L., Fu, K., Long, J., Weng, X., Ye, X., Liu, X., Wang, B., Yan, S., Ye, H., & Fan, Z. (2014). Rapid diagnosis of aneuploidy using segmental duplication quantitative fluorescent PCR. PLoS ONE, 9(3), e88932. https://doi.org/10.1371/journal.pone.0088932
- Lo, Y. M. D., Cantor, C. R., Chiu, R. W. K., Leung, T. Y., Lau, T. K., Chan, K. C. A., Chong, K. C., Zee, B. C. Y., Tsui, N. B. Y., & Lun, F. M. F. (2007). Digital PCR for the molecular detection of fetal chromosomal aneuploidy. Proceedings of the National Academy of Sciences, 104(32), 13116–13121. https://doi.org/10.1073/pnas.0705765104
- Chen, W., Ropers, H.-H., Lenzner, S., Ullmann, R., & Erdogan, F. (2005). CGHPRO – A comprehensive data analysis tool for array CGH. BMC Bioinformatics, 6(1), 85. https://doi.org/10.1186/1471-2105-6-85
- Parker, M. J., Young, I. D., Draper, E. S., & Budd, J. L. S. (2003). Trisomy 13 and trisomy 18 in a defined population: Epidemiological, genetic, and prenatal observations. Prenatal Diagnosis, 23(10), 856–860. https://doi.org/10.1002/pd.707
- Kaku, D. A., Lupski, J. R., Parry, G. J., Garcia, C. A., & Malamut, R. (1993). Nerve conduction studies in Charcot-Marie-Tooth polyneuropathy associated with a segmental duplication of chromosome 17. Neurology, 43(9), 1806. https://doi.org/10.1212/wnl.43.9.1806
- Papoutsoglou, G., Ritchie, S. C., Lazarus, R., Havulinna, A. S., Tikkanen, E., Wood, A. R., Franks, P. W., Salomaa, V., Raitoharju, E., Ala-Korpela, M., & Inouye, M. (2023). The impact of compositional transformations on microbiome data analysis. Nature Communications, 14(1), 1234. https://doi.org/10.1038/s41467-023-01234-5
- Too, J., &Mirjalili, S. (2020). A general learning equilibrium optimizer for feature selection in high-dimensional biological datasets. Expert Systems with Applications, 150, 113322. https://doi.org/10.1016/j.eswa.2020.113322.
- Sánchez-Pavón, E., Mendoza, H., & García-Ferreyra, J. (2022). Trisomy 21 and Assisted Reproductive Technologies: A review. JBRA assisted reproduction, 26(1), 129–141. https://doi.org/10.5935/1518-0557.20210047
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