Deep Learning-Based Architecture for Down Syndrome Assessment During Early Pregnancy Using Fetal Ultrasound Images
DOI:
https://doi.org/10.52756/ijerr.2024.v38.017Keywords:
Trisomy 21, Down syndrome, Artificial Intelligence, Convolutional Neural Network, Nuchal TranslucencyAbstract
Down's syndrome, also known as trisomy 21, is a prevalent genetic condition characterized by intellectual disability and developmental delay in children. The current study has prioritized the development of precise screening techniques for trisomy 21 in the initial stages of pregnancy in order to facilitate prompt diagnosis. This study presents an innovative paradigm for categorizing sagittal views in obstetric ultrasound examinations, specifically identifying Nuchal Translucency, a crucial component within the fetal brain, during the 11th to 14th weeks of pregnancy. The suggested deep learning-based system effectively detects the presence of the essential cerebral structure known as Nuchal Translucency, hence aiding in the diagnosis of Down's syndrome. A dataset comprising more than 1100 pre-processed 2D sagittal-view ultrasound images was gathered to train, test, and validate the proposed convolutional neural network model. The model results were utilized to quantify neurotensin levels and assess the presence of Down's syndrome by image classification. The performance of the model was assessed by measuring its sensitivity, specificity, and area under the curve metrics. These metrics were then compared to those of human experts who had received training in prenatal and ultrasound techniques. Notably, the suggested model attained an outstanding area under the curve score of 0.97. Our study suggests the most common non-invasive method for screening pregnant women for fetal abnormalities is ultrasound, which examines the unborn organs. The application of deep learning and machine learning techniques has significantly enhanced the diagnosis. In order to detect or anticipate conditions like Down syndrome, it assesses the intercranial structures of the developing embryo during the early stages of pregnancy. Developing an improved iteration of this model could serve as the foundation for an effective automated system for diagnosing Down's syndrome in its early stages within a clinical environment.
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