ArticleReproductive health2024
Prediction of chromosomal abnormalities in the screening of the first trimester of pregnancy using machine learning methods: a study protocol.
Article in Reproductive health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Transforming perinatal health with AI-predictive models in precision and digital innovations for maternal and fetal well-being: a systematic review.Oxford open digital health · 2026Article
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6 authors.
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Abstract
backgroundFor 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/
designThis 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.
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