scholarly journals Autism Spectrum Disorder’s Severity Prediction System Using Utterance Features

2020 ◽  
Vol 35 (3) ◽  
pp. B-J45_1-11
Author(s):  
Masahito Sakishita ◽  
Chihiro Ogawa ◽  
Kenji J. Tsuchiya ◽  
Toshiki Iwabuchi ◽  
Taishiro Kishimoto ◽  
...  
Keyword(s):  
Autism Spectrum ◽  
Prediction System ◽  
Severity Prediction

Autism Spectrum Disorder’s Severity Prediction Model Using Utterance Features for Automatic Diagnosis Support

2019 ◽  
pp. 83-95 ◽  
Author(s):  
Masahito Sakishita ◽  
Chihiro Ogawa ◽  
Kenji J. Tsuchiya ◽  
Toshiki Iwabuchi ◽  
Taishiro Kishimoto ◽  
...  
Keyword(s):  
Prediction Model ◽  
Autism Spectrum ◽  
Automatic Diagnosis ◽  
Severity Prediction

scholarly journals Severity Prediction System for Real Time Pothole Detection

2021 ◽  
Vol 23 (07) ◽  
pp. 1328-1334
Author(s):  
Sumit Bhimte ◽  
◽  
Hrishikesh hasabnis ◽  
Rohit Shirsath ◽  
Saurabh Sonar ◽  
...  
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Detection System ◽  
Detection Algorithm ◽  
Prediction System ◽  
Image Processing Algorithm ◽  
Efficient Technology ◽  
Severity Prediction ◽  
Pothole Detection ◽  
Real Time Application

Pothole Detection System using Image Processing or using Accelerometer is not a new normal. But there is no real time application which utilizes both techniques to provide us with efficient solution. We present a system which can be useful for the drivers to determine the intensity of Pothole using both Image Processing Technology and Accelerometer device-based Algorithm. The challenge in building this system was to efficiently detect a Pothole present in roads, to analyze the severity of Pothole and to provide users with information like Road Quality and best possible route. We have used various algorithms for frequency-based pothole detection. We compared the results. Apart from that, we selected the best approach suitable for achieving the project goals. We have used a Simple Differentiation-based Edge Detection Algorithm for Image Processing. The system has been built on Map Interfaces for Android devices using Android Studio, which consists of usage of Image Processing Algorithm based Python frameworks which is a sub field of Machine Learning. It is backed by powerful DBMS. This project facilitates use of most efficient technology tools to provide a good user experience, real time application, reliability and improved efficiency.

scholarly journals Predicting symptom severity in autism spectrum disorder based on cortical thickness measures in agglomerative data

2016 ◽  
Author(s):  
Elaheh Moradi ◽  
Budhachandra Khundrakpam ◽  
John D. Lewis ◽  
Alan C. Evans ◽  
Jussi Tohka
Keyword(s):  
Machine Learning ◽  
Autism Spectrum Disorder ◽  
Cortical Thickness ◽  
Symptom Severity ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Support Vector ◽  
Imaging Data ◽  
Learning Stage ◽  
Severity Prediction

AbstractMachine learning approaches have been widely used for the identification of neuropathology from neuroimaging data. However, these approaches require large samples and suffer from the challenges associated with multi-site, multi-protocol data. We propose a novel approach to address these challenges, and demonstrate its usefulness with the Autism Brain Imaging Data Exchange (ABIDE) database. We predict symptom severity based on cortical thickness measurements from 156 individuals with autism spectrum disorder (ASD) from four different sites. The proposed approach consists of two main stages: a domain adaptation stage using partial least squares regression to maximize the consistency of imaging data across sites; and a learning stage combining support vector regression for regional prediction of severity with elastic-net penalized linear regression for integrating regional predictions into a whole-brain severity prediction. The proposed method performed markedly better than simpler alternatives, better with multi-site than single-site data, and resulted in a considerably higher cross-validated correlation score than has previously been reported in the literature for multi-site data. This demonstration of the utility of the proposed approach for detecting structural brain abnormalities in ASD from the multi-site, multi-protocol ABIDE dataset indicates the potential of designing machine learning methods to meet the challenges of agglomerative data.

scholarly journals Multidimensional Neuroanatomical Subtyping of Autism Spectrum Disorder

2017 ◽  
Vol 28 (10) ◽  
pp. 3578-3588 ◽  
Author(s):  
Seok-Jun Hong ◽  
Sofie L Valk ◽  
Adriana Di Martino ◽  
Michael P Milham ◽  
Boris C Bernhardt
Keyword(s):  
Autism Spectrum Disorder ◽  
Surface Area ◽  
Geodesic Distance ◽  
Autism Diagnostic Observation Schedule ◽  
Analytical Framework ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Learning Approaches ◽  
Bootstrap Analysis ◽  
Severity Prediction

Abstract Autism spectrum disorder (ASD) is a group of neurodevelopmental disorders with multiple biological etiologies and highly variable symptoms. Using a novel analytical framework that integrates cortex-wide MRI markers of vertical (i.e., thickness, tissue contrast) and horizontal (i.e., surface area, geodesic distance) cortical organization, we could show that a large multi-centric cohort of individuals with ASD falls into 3 distinctive anatomical subtypes (ASD-I: cortical thickening, increased surface area, tissue blurring; ASD-II: cortical thinning, decreased distance; ASD-III: increased distance). Bootstrap analysis indicated a high consistency of these biotypes across thousands of simulations, while analysis of behavioral phenotypes and resting-state fMRI showed differential symptom load (i.e., Autism Diagnostic Observation Schedule; ADOS) and instrinsic connectivity anomalies in communication and social-cognition networks. Notably, subtyping improved supervised learning approaches predicting ADOS score in single subjects, with significantly increased performance compared to a subtype-blind approach. The existence of different subtypes may reconcile previous results so far not converging on a consistent pattern of anatomical anomalies in autism, and possibly relate the presence of diverging corticogenic and maturational anomalies. The high accuracy for symptom severity prediction indicates benefits of MRI biotyping for personalized diagnostics and may guide the development of targeted therapeutic strategies.

Problem behavior in autism spectrum disorders: A paradigmatic self-organized perspective of network structures

2019 ◽  
Vol 42 ◽  
Author(s):  
Lucio Tonello ◽  
Luca Giacobbi ◽  
Alberto Pettenon ◽  
Alessandro Scuotto ◽  
Massimo Cocchi ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Problem Behaviors ◽  
Autism Spectrum ◽  
The Self ◽  
Network Structures ◽  
Self Organized ◽  
Critical Equilibrium ◽  
Self Organized Criticality ◽  
Acute Agitation ◽  
Spectrum Disorders

AbstractAutism spectrum disorder (ASD) subjects can present temporary behaviors of acute agitation and aggressiveness, named problem behaviors. They have been shown to be consistent with the self-organized criticality (SOC), a model wherein occasionally occurring “catastrophic events” are necessary in order to maintain a self-organized “critical equilibrium.” The SOC can represent the psychopathology network structures and additionally suggests that they can be considered as self-organized systems.

Development of Neural Structure and Function in Autism Spectrum Disorder: Potential Implications for Learning Language

2020 ◽  
Vol 29 (4) ◽  
pp. 1783-1797
Author(s):  
Kelly L. Coburn ◽  
Diane L. Williams
Keyword(s):  
Autism Spectrum Disorder ◽  
Language Development ◽  
Diffusion Tensor ◽  
Autism Spectrum ◽  
Language Intervention ◽  
Spectrum Disorder ◽  
Autistic Children ◽  
Neural Structure ◽  
Complex Information ◽  
And Function

Purpose Neurodevelopmental processes that begin during gestation and continue throughout childhood typically support language development. Understanding these processes can help us to understand the disruptions to language that occur in neurodevelopmental conditions, such as autism spectrum disorder (ASD). Method For this tutorial, we conducted a focused literature review on typical postnatal brain development and structural and functional magnetic resonance imaging, diffusion tensor imaging, magnetoencephalography, and electroencephalography studies of the neurodevelopmental differences that occur in ASD. We then integrated this knowledge with the literature on evidence-based speech-language intervention practices for autistic children. Results In ASD, structural differences include altered patterns of cortical growth and myelination. Functional differences occur at all brain levels, from lateralization of cortical functions to the rhythmic activations of single neurons. Neuronal oscillations, in particular, could help explain disrupted language development by elucidating the timing differences that contribute to altered functional connectivity, complex information processing, and speech parsing. Findings related to implicit statistical learning, explicit task learning, multisensory integration, and reinforcement in ASD are also discussed. Conclusions Consideration of the neural differences in autistic children provides additional scientific support for current recommended language intervention practices. Recommendations consistent with these neurological findings include the use of short, simple utterances; repetition of syntactic structures using varied vocabulary; pause time; visual supports; and individualized sensory modifications.

Parent Education in Studies With Nonverbal and Minimally Verbal Participants With Autism Spectrum Disorder: A Systematic Review

2020 ◽  
Vol 29 (2) ◽  
pp. 890-902
Author(s):  
Lynn Kern Koegel ◽  
Katherine M. Bryan ◽  
Pumpki Lei Su ◽  
Mohini Vaidya ◽  
Stephen Camarata
Keyword(s):  
Systematic Review ◽  
Autism Spectrum Disorder ◽  
Parent Education ◽  
Autism Spectrum ◽  
Verbal Communication ◽  
Fidelity Of Implementation ◽  
Spectrum Disorder ◽  
Education Programs ◽  
Minimally Verbal ◽  
Parent Education Programs

Purpose The purpose of this systematic review was to identify parent education procedures implemented in intervention studies focused on expressive verbal communication for nonverbal (NV) or minimally verbal (MV) children with autism spectrum disorder (ASD). Parent education has been shown to be an essential component in the habilitation of individuals with ASD. Parents of individuals with ASD who are NV or MV may particularly benefit from parent education in order to provide opportunities for communication and to support their children across the life span. Method ProQuest databases were searched between the years of 1960 and 2018 to identify articles that targeted verbal communication in MV and NV individuals with ASD. A total of 1,231 were evaluated to assess whether parent education was implemented. We found 36 studies that included a parent education component. These were reviewed with regard to (a) the number of participants and participants' ages, (b) the parent education program provided, (c) the format of the parent education, (d) the duration of the parent education, (e) the measurement of parent education, and (f) the parent fidelity of implementation scores. Results The results of this analysis showed that very few studies have included a parent education component, descriptions of the parent education programs are unclear in most studies, and few studies have scored the parents' implementation of the intervention. Conclusions Currently, there is great variability in parent education programs in regard to participant age, hours provided, fidelity of implementation, format of parent education, and type of treatment used. Suggestions are made to provide both a more comprehensive description and consistent measurement of parent education programs.

The Influence of Language Context on Repetitive Speech Use in Children With Autism Spectrum Disorder

2020 ◽  
Vol 29 (1) ◽  
pp. 327-334 ◽  
Author(s):  
Allison Gladfelter ◽  
Cassidy VanZuiden
Keyword(s):  
Autism Spectrum Disorder ◽  
Autism Spectrum ◽  
Individual Characteristics ◽  
Receptive Vocabulary ◽  
Spectrum Disorder ◽  
School Age Children ◽  
Social Responsiveness ◽  
Contributing Factors ◽  
Children With Asd ◽  
Language Context

Purpose Although repetitive speech is a hallmark characteristic of autism spectrum disorder (ASD), the contributing factors that influence repetitive speech use remain unknown. The purpose of this exploratory study was to determine if the language context impacts the amount and type of repetitive speech produced by children with ASD. Method As part of a broader word-learning study, 11 school-age children with ASD participated in two different language contexts: storytelling and play. Previously collected language samples were transcribed and coded for four types of repetitive speech: immediate echolalia, delayed echolalia, verbal stereotypy, and vocal stereotypy. The rates and proportions of repetitive speech were compared across the two language contexts using Wilcoxon signed-ranks tests. Individual characteristics were further explored using Spearman correlations. Results The children produced lower rates of repetitive speech during the storytelling context than the play-based context. Only immediate echolalia differed between the two contexts based on rate and approached significance based on proportion, with more immediate echolalia produced in the play-based context than in the storytelling context. There were no significant correlations between repetitive speech and measures of social responsiveness, expressive or receptive vocabulary, or nonverbal intelligence. Conclusions The children with ASD produced less immediate echolalia in the storytelling context than in the play-based context. Immediate echolalia use was not related to social skills, vocabulary, or nonverbal IQ scores. These findings offer valuable insights into better understanding repetitive speech use in children with ASD.

Instruction Using Augmentative and Alternative Communication Supports: Description of Current Practices by Speech-Language Pathologists Who Work With Children With Autism Spectrum Disorder

2020 ◽  
Vol 29 (2) ◽  
pp. 586-596 ◽  
Author(s):  
Kaitlyn A. Clarke ◽  
Diane L. Williams
Keyword(s):  
Autism Spectrum Disorder ◽  
Augmentative And Alternative Communication ◽  
Communication Systems ◽  
Diagnostic Category ◽  
Children With Autism ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Alternative Communication ◽  
Speech Language Pathologists ◽  
Processing Differences

Purpose The aim of this research study was to examine common practices of speech-language pathologists (SLPs) who work with children with autism spectrum disorder (ASD) with respect to whether or not SLPs consider processing differences in ASD or the effects of input during their instruction. Method Following a qualitative research method, how SLPs instruct and present augmentative and alternative communication systems to individuals with ASD, their rationale for method selection, and their perception of the efficacy of selected interventions were probed. Semistructured interviews were conducted as part of an in-depth case report with content analysis. Results Based on completed interviews, 4 primary themes were identified: (a) instructional method , (b) input provided , (c) decision-making process , and (d) perceived efficacy of treatment . Additionally, one secondary theme, training and education received , was identified . Conclusions Clinicians reported making decisions based on the needs of the child; however, they also reported making decisions based on the diagnostic category that characterized the child (i.e., ASD). The use of modeling when teaching augmentative and alternative communication to individuals with ASD emerged as a theme, but variations in the method of modeling were noted. SLPs did not report regularly considering processing differences in ASD, nor did they consider the effects of input during instruction.

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