Detecting Fetal Alcohol Spectrum Disorder in children using Artificial Neural Network

• URL: http://arxiv.org/abs/2105.15074v1
• Date: Mon, 31 May 2021 16:02:01 GMT
• Title: Detecting Fetal Alcohol Spectrum Disorder in children using Artificial Neural Network
• Authors: Vannessa de J. Duarte, Paul Leger, Sergio Contreras and Hiroaki Fukuda
• Abstract summary: Fetal alcohol spectrum disorder (FASD) is a syndrome whose only difference compared to other children's conditions is the mother's alcohol consumption during pregnancy. This study focus on evaluating the use of the artificial neural network (ANN) to classify children with FASD.
• Score: 3.9954080397757954
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Fetal alcohol spectrum disorder (FASD) is a syndrome whose only difference compared to other children's conditions is the mother's alcohol consumption during pregnancy. An earlier diagnosis of FASD improving the quality of life of children and adolescents. For this reason, this study focus on evaluating the use of the artificial neural network (ANN) to classify children with FASD and explore how accurate it is. ANN has been used to diagnose cancer, diabetes, and other diseases in the medical area, being a tool that presents good results. The data used is from a battery of tests from children for 5-18 years old (include tests of psychometric, saccade eye movement, and diffusion tensor imaging (DTI)). We study the different configurations of ANN with dense layers. The first one predicts 75\% of the outcome correctly for psychometric data. The others models include a feature layer, and we used it to predict FASD using every test individually. The models accurately predict over 70\% of the cases, and psychometric and memory guides predict over 88\% accuracy. The results suggest that the ANN approach is a competitive and efficient methodology to detect FASD. However, we could be careful in used as a diagnostic technique.

Related papers

• Brain Tumor Classification on MRI in Light of Molecular Markers [61.77272414423481]
  Co-deletion of the 1p/19q gene is associated with clinical outcomes in low-grade gliomas. This study aims to utilize a specially MRI-based convolutional neural network for brain cancer detection.
  arXiv  Detail & Related papers  (2024-09-29T07:04:26Z)
• DDxT: Deep Generative Transformer Models for Differential Diagnosis [51.25660111437394]
  We show that a generative approach trained with simpler supervised and self-supervised learning signals can achieve superior results on the current benchmark. The proposed Transformer-based generative network, named DDxT, autoregressively produces a set of possible pathologies, i.e., DDx, and predicts the actual pathology using a neural network.
  arXiv  Detail & Related papers  (2023-12-02T22:57:25Z)
• Screening Autism Spectrum Disorder in childrens using Deep Learning Approach : Evaluating the classification model of YOLOv8 by comparing with other models [0.0]
  We propose a practical solution for ASD screening using facial images using YoloV8 model. Our model achieved a remarkable 89.64% accuracy in classification and an F1-score of 0.89.
  arXiv  Detail & Related papers  (2023-06-25T18:02:01Z)
• Predi\c{c}\~ao da Idade Cerebral a partir de Imagens de Resson\^ancia Magn\'etica utilizando Redes Neurais Convolucionais [57.52103125083341]
  Deep learning techniques for brain age prediction from magnetic resonance images are investigated. The identification of biomarkers is useful for detecting an early-stage neurodegenerative process, as well as for predicting age-related or non-age-related cognitive decline. The best result was obtained by the 2D model, which achieved a mean absolute error of 3.83 years.
  arXiv  Detail & Related papers  (2021-12-23T14:51:45Z)
• Detecting Autism Spectrum Disorders with Machine Learning Models Using Speech Transcripts [0.0]
  Autism spectrum disorder (ASD) can be defined as a neurodevelopmental disorder that affects how children interact, communicate and socialize with others. Current methods to accurately diagnose ASD are invasive, time-consuming, and tedious. New technologies are rapidly emerging that include machine learning models using speech, computer vision from facial, retinal, and brain MRI images of patients to accurately and timely detect this disorder.
  arXiv  Detail & Related papers  (2021-10-07T09:10:15Z)
• Development of an autism screening classification model for toddlers [0.0]
  Autism spectrum disorder ASD is a neurodevelopmental disorder associated with challenges in communication, social interaction, and repetitive behaviors. This work contributes to the early screening of toddlers by helping identify those who have ASD traits and should pursue formal clinical diagnosis.
  arXiv  Detail & Related papers  (2021-09-29T09:07:39Z)
• Brain Age Estimation From MRI Using Cascade Networks with Ranking Loss [75.03117866578913]
  A novel 3D convolutional network, called two-stage-age-network (TSAN), is proposed to estimate brain age from T1-weighted MRI data. Experiments with $6586$ MRIs showed that TSAN could provide accurate brain age estimation.
  arXiv  Detail & Related papers  (2021-06-06T07:11:25Z)
• Convolutional Neural Networks for Sleep Stage Scoring on a Two-Channel EEG Signal [63.18666008322476]
  Sleep problems are one of the major diseases all over the world. Basic tool used by specialists is the Polysomnogram, which is a collection of different signals recorded during sleep. Specialists have to score the different signals according to one of the standard guidelines.
  arXiv  Detail & Related papers  (2021-03-30T09:59:56Z)
• Detecting Autism Spectrum Disorder using Machine Learning [3.2861753207533937]
  Sequential minimal optimization (SMO) based Support Vector Machines (SVM) classifier outperforms all other benchmark machine learning algorithms. Relief Attributes algorithm is the best to identify the most significant attributes in ASD datasets.
  arXiv  Detail & Related papers  (2020-09-30T08:33:12Z)
• Early Autism Spectrum Disorders Diagnosis Using Eye-Tracking Technology [62.997667081978825]
  Lack of money, absence of qualified specialists, and low level of trust to the correction methods are the main issues that affect the in-time diagnoses of ASD. Our team developed the algorithm that will be able to predict the chances of ASD according to the information from the gaze activity of the child.
  arXiv  Detail & Related papers  (2020-08-21T20:22:55Z)
• Diagnosis of Autism in Children using Facial Analysis and Deep Learning [0.0]
  We introduce a deep learning model to classify children as either healthy or potentially autistic with 94.6% accuracy using Deep Learning. Autistic patients struggle with social skills, repetitive behaviors, and communication, both verbal and nonverbal. Based on our accuracy, we propose that the diagnosis of autism can be done effectively using only a picture.
  arXiv  Detail & Related papers  (2020-08-06T22:15:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.