Abstract
Down syndrome is one of the most common genetic disorders. The distinctive facial features of Down syndrome provide an opportunity for automatic identification. Recent studies showed that facial recognition technologies have the capability to identify genetic disorders. However, there is a paucity of studies on the automatic identification of Down syndrome with facial recognition technologies, especially using deep convolutional neural networks. Here, we developed a Down syndrome identification method utilizing facial images and deep convolutional neural networks, which quantified the binary classification problem of distinguishing subjects with Down syndrome from healthy subjects based on unconstrained two-dimensional images. The network was trained in two main steps: First, we formed a general facial recognition network using a large-scale face identity database (10,562 subjects) and then trained (70%) and tested (30%) a dataset of 148 Down syndrome and 257 healthy images curated through public databases. In the final testing, the deep convolutional neural network achieved 95.87% accuracy, 93.18% recall, and 97.40% specificity in Down syndrome identification. Our findings indicate that the deep convolutional neural network has the potential to support the fast, accurate, and fully automatic identification of Down syndrome and could add considerable value to the future of precision medicine.
Highlights
Down syndrome is one of the most common genetic syndromes caused by a chromosome 21 abnormality with a prevalence of 1:1000–1100 worldwide [1]
The experimental results show that the proposed method substantially improved the performance of Down syndrome identification, with an AUPRC of 0.9854 and an AUROC of 0.9909 individuals do not face directly toward the camera
Our study presents a novel facial recognition method that uses deep convolutional neural network (DCNN) to identify Down syndrome automatically from 2D facial images in a large-scale facial recognition dataset
Summary
Down syndrome is one of the most common genetic syndromes caused by a chromosome 21 abnormality with a prevalence of 1:1000–1100 worldwide [1]. Patients with Down syndrome are typically associated with characteristic facial features, physical growth delays, mild to moderate intellectual disabilities [2,3,4,5,6,7], and an increased risk of complications for respiratory and hearing problems, as well as heart defects [5,8]. Diagnosis is necessary to prevent the occurrence of potential health problems and to benefit patients with lifelong healthcare involving physical, speech, cardiac, and neurological therapies [9]. The diagnosis of Down syndrome can be conducted during pregnancy or after birth [10,11]. Down syndrome can be identified by the presence
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