PREDICTING RISK FACTORS DURING PREGNANCY AND ASSESSING FETAL WEIGHT USING 3D BODY SCANNING TECHNOLOGY AND HYBRID NEURAL NETWORKS
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Keywords: Pregnancy, adverse outcome, fetal weight, 3D body scanning, hybrid neural networks, artificial intelligence, prediction, medical imagingАннотация
Abstract. This article examines the effectiveness of 3D body scanning technology and hybrid neural network methods in identifying risk factors during pregnancy and determining fetal weight. With the help of a 3D body scanner, accurate and complete measurements of the mother's body are taken, and these data are entered into a hybrid neural network model. High accuracy is achieved in predicting risk factors and determining the estimated weight of the fetus through hybrid neural networks. The proposed method is an effective tool for monitoring the health of pregnant women and early diagnosis, which makes it possible to reduce complications during pregnancy. The research results show the prospects for the application of artificial intelligence and advanced technologies in the field of medicine.
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