Artificial intelligence for prenatal chromosome analysis.

Area of Science:

• Genetics and Bioinformatics
• Computational Biology
• Medical Diagnostics

Background:

• Prenatal diagnostics focus on detecting chromosomal abnormalities like Down syndrome.
• Traditional methods face limitations in accuracy and scope.
• Advancements in computational methods offer new avenues for improved prenatal screening.

Purpose of the Study:

• To review the application and effectiveness of machine learning (ML) and deep learning (DL) in prenatal diagnostics.
• To examine how computational methodologies enhance the detection and management of chromosomal abnormalities.
• To explore advancements in Non-Invasive Prenatal Testing (NIPT), genomics, and medical imaging.

Main Methods:

• Comprehensive review of existing literature on ML, DL, and data analysis in prenatal diagnostics.
• Analysis of advancements in NIPT, genomics, and medical imaging.
• Examination of specific ML techniques like ensemble models and transfer learning.

Main Results:

• ML and DL have substantially improved detection rates and accuracy for prenatal conditions.
• Ensemble models and transfer learning show promise, especially with limited datasets.
• Optimal feature selection and high-dimensional feature fusion are critical for predictive model development.

Conclusions:

• AI and ML techniques are crucial for early detection and improved management of prenatal conditions.
• Further research is needed to address limitations in sample size and model generalizability.
• The integration of advanced computational methods is transforming prenatal diagnostics.