FetalAI: A deep learning web-based application for predicting birthweight from prenatal ultrasound measurements

Abstract

FetalAI is a web-based application that utilizes a longitudinal learning procedure that integrates data augmentation and deep learning as back-end, with a purpose of predicting an newborn’s projected birthweights and the associated risks of excessive fetal growth conditions, including large-for-gestational-age (LGA) and macrosomia. This article provides an end-to-end overview of FetalAI’s development process, which includes data sourcing and preprocessing, back-end model design and training, application output interpretation, and user interactions. We begin by describing the original training data sources and their mathematical structures. We then outline the data engineering pipeline that acquires and pre-processes user input. After data sourcing and pre-processing, we provide details on the back-end model, which applies mixed-effects models for data augmentation and pre-trained recurrent neural networks (RNNs) to forecast birthweight sequences. After back-end, we discuss the front-end development of FetalAI, with an emphasis on creating the graphical user interface (GUI) using Streamlit and the application’s ability to handle corner cases and anomalous inputs. Our article concludes by highlighting the key functionalities and potential use cases of FetalAI, as well as outlining future directions for research and development in this domain. FetalAI represents a significant step forward in the application of deep learning techniques to improve prenatal care and support clinical decision-making.

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