Prediction of chromosomal abnormalities in the screening of the first trimester of pregnancy using machine learning methods: a study protocol.

ABSTRACT

BACKGROUND: For women in the first trimester, amniocentesis or chorionic villus sampling is recommended for screening. Machine learning has shown increased accuracy over time and finds numerous applications in enhancing decision-making, patient care, and service quality in nursing and midwifery. This study aims to develop an optimal learning model utilizing machine learning techniques, particularly neural networks, to predict chromosomal abnormalities and evaluate their predictive efficacy. METHODS/ DESIGN: This cross-sectional study will be conducted in midwifery clinics in Mashhad, Iran in 2024. The data will be collected from 350 pregnant women in the high-risk group who underwent screening tests in the first trimester (between 11-14 weeks) of pregnancy. Information collected includes maternal age, BMI, smoking habits, history of trisomy 21 and other chromosomal disorders, CRL and NT levels, PAPP-A and B-HCG levels, presence of insulin-dependent diabetes, and whether the pregnancy resulted from IVF. The study follows up with the women during their clinic visits and tracks the results of amniocentesis. Sampling is based on Convenience Sampling, and data is gathered using a checklist of characteristics and screening/amniocentesis results. After preprocessing, feature extraction is conducted to identify and predict relevant features. The model is trained and evaluated using K-fold cross-validation. DISCUSSION: There is a growing interest in utilizing artificial intelligence methods, like machine learning and deep learning, in nursing and midwifery. This underscores the critical necessity for nurses and midwives to be well-versed in artificial intelligence methods and their healthcare applications. It can be beneficial to develop a machine learning model, specifically focusing on neural networks, for predicting chromosomal abnormalities. ETHICAL CODE IR.MUMS.NURSE.REC. 1402.134.

Approximately 3% of newborns are affected by congenital abnormalities and genetic diseases, leading to disability and death. Among live births, around 3000 cases of Down syndrome (trisomy 21) can be expected based on the country's birth rate. Pregnant women carrying fetuses with Down syndrome face an increased risk of pregnancy complications. Artificial intelligence methods, such as machine learning and deep learning, are being used in nursing and midwifery to improve decision-making, patient care, and research. Nurses need to actively participate in the development and implementation of AI-based decision support systems. Additionally, nurses and midwives should play a key role in evaluating the effectiveness of artificial intelligence-based technologies in professional practice.