Authors :
Dr. Harini P Shetty; Dr. Manjula S Patil; Dr. Shwetha Yadav; Dr. Venugopal Reddy.I
Volume/Issue :
Volume 9 - 2024, Issue 10 - October
Google Scholar :
https://tinyurl.com/3mbsfk5y
Scribd :
https://tinyurl.com/2uj7z8xb
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24OCT1344
Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.
Abstract :
Artificial Intelligence (AI) and Machine
Learning (ML) are at the forefront of innovations in
medical diagnostics, including prenatal screening and
genetic analysis. The development of advanced
algorithms, data processing capabilities, and predictive
modeling has significantly enhanced the sensitivity,
specificity, and accuracy of non-invasive prenatal testing
(NIPT). This article explores recent advancements in AI-
driven prenatal screening, the methodologies employed,
and the future potential of AI in predictive prenatal
health diagnostics, with a particular focus on improving
genetic disorder detection and fetal health outcomes. The
role of ethical considerations in AI-driven diagnostics is
also discussed.
Keywords :
Artificial Intelligence, Machine Learning, Prenatal Screening, Genetic Analysis, Non-Invasive Prenatal Testing, Predictive Health.
References :
- Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: Past, present, and future. Stroke and Vascular Neurology, 2(4), e000101.
- Vollset, S. E., Gakidou, E., Flaxman, A. D., & Murray, C. J. L. (2020). Development of an artificial intelligence-based non-invasive prenatal testing model to predict fetal aneuploidy. The Lancet Digital Health, 2(7), e359-e367.
- Deng, X., Wu, S., & Cheng, Y. (2018). Advances in non-invasive prenatal testing for Down syndrome and other genetic disorders using AI and deep learning. Clinical and Molecular Teratology, 112(6), 508-514.
- Park, Y., Jackson, S. R., & Yoon, H. J. (2021). Machine learning techniques in genetic risk prediction: Applications to fetal health and prenatal screening. Genetic Epidemiology, 45(5), 453-466.
- Zou, J., Huss, M., Abid, A., Mohammadi, P., Torkamani, A., & Telenti, A. (2019). A primer on deep learning in genomics. Nature Genetics, 51(1), 12-18.
- Ahuja, A. S. (2019). The impact of artificial intelligence in medicine on the future role of the physician. PeerJ, 7, e7702.
- Hu, H., Liu, H., Zhang, Y., & Ma, J. (2020). Ethical challenges of using artificial intelligence for non-invasive prenatal testing. BMC Medical Ethics, 21(1), 10.
- Cheng, Y., Chen, M., & Du, X. (2021). AI-powered non-invasive prenatal testing: Enhancing diagnostic accuracy through computational innovation. Artificial Intelligence in Medicine, 114, 102044.
- Chen, R., & Snyder, M. (2019). Promise of personalized omics to precision medicine. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 11(6), e1457.
- Krittanawong, C., Johnson, K. W., Rosenson, R. S., Ting, H. H., Gersh, B. J., & Wang, Z. (2021). Deep learning for cardiovascular medicine: A practical primer. European Heart Journal, 42(21), 2094-2100.
Artificial Intelligence (AI) and Machine
Learning (ML) are at the forefront of innovations in
medical diagnostics, including prenatal screening and
genetic analysis. The development of advanced
algorithms, data processing capabilities, and predictive
modeling has significantly enhanced the sensitivity,
specificity, and accuracy of non-invasive prenatal testing
(NIPT). This article explores recent advancements in AI-
driven prenatal screening, the methodologies employed,
and the future potential of AI in predictive prenatal
health diagnostics, with a particular focus on improving
genetic disorder detection and fetal health outcomes. The
role of ethical considerations in AI-driven diagnostics is
also discussed.
Keywords :
Artificial Intelligence, Machine Learning, Prenatal Screening, Genetic Analysis, Non-Invasive Prenatal Testing, Predictive Health.