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

IF 3.6 2区医学 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH Reproductive Health Pub Date : 2024-07-03 DOI:10.1186/s12978-024-01839-5

Mahla Shaban, Sanaz Mollazadeh, Saeid Eslami, Fatemeh Tara, Samaneh Sharif, Fatemeh Erfanian Arghavanian

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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.

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在妊娠头三个月筛查中使用机器学习方法预测染色体异常：研究方案。

背景：对于妊娠头三个月的妇女，建议采用羊膜腔穿刺术或绒毛取样术进行筛查。随着时间的推移，机器学习的准确性不断提高，在提高护理和助产决策、患者护理和服务质量方面应用广泛。本研究旨在利用机器学习技术（尤其是神经网络）开发一种最佳学习模型，以预测染色体异常，并评估其预测效果：这项横断面研究将于 2024 年在伊朗马什哈德的助产诊所进行。将从 350 名在妊娠头三个月（11-14 周）接受筛查的高危孕妇中收集数据。收集的信息包括孕妇年龄、体重指数、吸烟习惯、21 三体综合征和其他染色体疾病史、CRL 和 NT 水平、PAPP-A 和 B-HCG 水平、是否患有胰岛素依赖型糖尿病以及是否通过试管婴儿怀孕。该研究在妇女就诊期间对其进行跟踪，并跟踪羊膜腔穿刺术的结果。抽样采用便利抽样法，通过特征和筛查/羊水穿刺结果核对表收集数据。预处理后，进行特征提取以识别和预测相关特征。使用 K 倍交叉验证对模型进行训练和评估：人们对在护理和助产中使用人工智能方法（如机器学习和深度学习）的兴趣与日俱增。这强调了护士和助产士精通人工智能方法及其医疗应用的重要性。开发一个机器学习模型，特别是侧重于神经网络的模型，对预测染色体异常是有益的：伦理守则：ir.mums.nurse.rec.1402.134.

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来源期刊

Reproductive Health PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH-

CiteScore

6.00

自引率

5.90%

发文量

220

审稿时长

>12 weeks

期刊介绍： Reproductive Health focuses on all aspects of human reproduction. The journal includes sections dedicated to adolescent health, female fertility and midwifery and all content is open access. Reproductive health is defined as a state of physical, mental, and social well-being in all matters relating to the reproductive system, at all stages of life. Good reproductive health implies that people are able to have a satisfying and safe sex life, the capability to reproduce and the freedom to decide if, when, and how often to do so. Men and women should be informed about and have access to safe, effective, affordable, and acceptable methods of family planning of their choice, and the right to appropriate health-care services that enable women to safely go through pregnancy and childbirth.