Application of artificial intelligence in prenatal diagnosis of genetic diseases

Mohammadreza Moradi¹ Arshan Amiri² Arsha Baradaran³ Mohammad Taha Eshghi⁴ Shayan Kazemi⁵

1) The Researcher of Damesh Sepahan Research and New Technologies Center, Isfahan, Iran
2) The Researcher of Damesh Sepahan Research and New Technologies Center, Isfahan, Iran
3) The Researcher of Damesh Sepahan Research and New Technologies Center, Isfahan, Iran
4) The Researcher of Damesh Sepahan Research and New Technologies Center, Isfahan, Iran
5) The Researcher of Damesh Sepahan Research and New Technologies Center, Isfahan, Iran

Publication : 3rd International Conference on Recent Advances in Engineering, Innovation and Technology - Belgium(eitconf.com/3rd)
Abstract :
In recent decades, technological advances in genetics and medicine have led to the development of advanced methods for prenatal diagnosis and prediction of genetic diseases. Artificial intelligence (AI) and machine learning (ML) are powerful tools that can analyze complex genetic data and identify hereditary diseases and genetic mutations with high accuracy. These technologies help doctors accelerate the diagnosis process, increase accuracy, and provide personalized treatments. Prenatal genetic diseases are one of the most important medical challenges, and their rapid and accurate diagnosis can help improve the quality of life of newborns and reduce treatment costs. In recent years, artificial intelligence (AI) and machine learning (ML) have been used as powerful tools in analyzing genetic data and predicting genetic disorders prenatally. These technologies enable more accurate and faster diagnosis of hereditary diseases by examining genomic sequences, medical images, and clinical data. In this article, the applications of artificial intelligence in genomic data analysis, genetic disease prediction, optimization of screening processes, and early detection of chromosomal abnormalities are examined. Also, the challenges of using artificial intelligence in this area, including ethical issues, data security, and algorithm accuracy, will be analyzed.
Keywords : artificial intelligence genetic diseases prenatal diagnosis machine learning genomic data gene editing