The chapter starts out by going over the different kinds of pregnancy issues, their causes, and the difficulties in foreseeing them using conventional statistical techniques. The following section of the chapter gives a thorough summary of a number of research that employed machine learning methods to forecast pregnancy problems. In this chapter, the authors use a dataset of ultrasound images taken during pregnancy of different parts of the mother and the unborn child. After passing the dataset through pre-processing, where all the images were resized, the authors use data augmentation techniques to make the data more relevant for use in predictive modelling. To classify six classes, some pre-trained models were employed. The chapter ends with a review of the difficulties encountered by machine learning methods for anticipating pregnancy issues, such as the absence of extensive data sets and the requirement for additional validation studies.
Top1 Introduction
A number of physiological changes in the body of the motheroccur throughout pregnancy to suit the fetus's growth and developmentmaking it a complicated process. While most pregnancies progress without any major complications, some women may develop health issues which may have negative consequences on both the mother's health and the baby. Predicting these complications early on can be crucial in ensuring appropriate medical interventions and improving pregnancy outcomes.
In recent years, deep learning is now recognized as a potentially useful method for forecasting pregnancy problems. Deep learning refers to a field of AI that employs neural networks to analyze large datasets and learn from them in order to predict future events(LeCun et al., 2015). Having the capacity to manage massive volumes of data and spot minor trends, deep learning provides a tendency to raise precision and speed of predicting pregnancy complications(Bertini et al., 2022).
Ultrasound imaging has revolutionized the way obstetricians and gynecologists care for pregnant women(Campbell, 2013). The technology provides detailed information about fetal development and maternal health, allowing physicians to monitor the health of the pregnancy and identify potential complications. However, interpreting ultrasound images can be a challenging task, requiring significant expertise and experience(Burgos-Artizzu et al., 2020). Deep learning techniques have gained popularity recently and are now useful tools for analyzing medical images and improving diagnostic accuracy. In this chapter, the use of deep learning algorithms to predict pregnancy complications from ultrasound images will be explored.
The ability to predict pregnancy complications is critical for safeguarding the mother's and the child's health and safety. Pregnancy complications can arise for a variety of reasons, including maternal health conditions such as diabetes, hypertension, and obesity, as well as fetal growth abnormalities and genetic disorders. Early detection of these complications can help physicians develop a treatment plan to minimize the risks and improve outcomes. However, accurate prediction of pregnancy complications is a challenging task, and existing methods often rely on a combination of clinical factors and ultrasound imaging.
Deep learning offers the ability to completely transform how pregnancy complications are predicted and managed. The goal of deep learning algorithms is to automatically discover and identify features from big datasets, making them particularly well-suited for analyzing medical images. By training deep learning models on large datasets of ultrasound images and associated clinical data, models that are capable of accurately predicting pregnancy complications based on ultrasound images alone may be developed. This could potentially reduce the need for invasive procedures and other diagnostic tests, improving patient outcomes and reducing healthcare costs.
Lack of large datasets with annotations is one of the biggest obstacles to creating precise deep learning algorithms to anticipate pregnancy problems. For the purpose of training deep learning models, annotated datasets are crucial, as they allow the models to learn from examples and generalize to new cases. However, creating annotated datasets for ultrasound images can be a time-consuming and challenging task. This is particularly true for rare pregnancy complications, where there may be limited examples available for training.
Despite these challenges, there has been significant progress in developing deep learning models for predicting pregnancy complications from ultrasound images. In recent years, a number of deep learning architectures, such as convolutional neural networks, recurrent neural networks, and attention-based models were examined(Deepika et al., 2021). These models have shown promising results for predicting a variety of pregnancy issues, including as premature delivery and fetal development limitation.
Fetal growth restriction is a condition where the fetus does not grow at the expected rate, often due to a placental insufficiency. Numerous issues, including as low birth weight, premature delivery, and fetal discomfort, may result from growth restriction. Deep learning models have shown promising results for predicting fetal growth restriction from ultrasound images.