Concordance between a U.S. Educational Autism Classification and the Autism Diagnostic Observation Schedule

Maddox, Brenna B.; Rump, Keiran; Stahmer, Aubyn C.; Suhrheinrich, Jessica; Rieth, Sarah R.; Nahmias, Allison S.; Nuske, Heather J.; Reisinger, Erica M.; Crabbe, Samantha; Bronstein, Briana; Mandell, David S.

doi:10.1080/15374416.2019.1567345

Cited by 12 publications

(7 citation statements)

References 22 publications

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“…The best way to detect and treat ASD is by effective diagnosis. Currently, the Diagnostic and Statistical Manual (DSM-5) [8], [9], Autism Diagnostic Observation Schedule (ADOS) [10], [11] and the Autism Diagnostic Interview (ADI-R) [12], [13] are used to conduct initial screening. Manual diagnoses are subjective, prone to errors and biases due to disparities in expertise [14], [15].…”

Section: Introductionmentioning

confidence: 99%

Outlier-Based Autism Detection Using Longitudinal Structural MRI

et al. 2022

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Diagnosis of Autism Spectrum Disorder (ASD) using clinical evaluation (cognitive tests) is challenging due to wide variations amongst individuals. Since no effective treatment exists, prompt and reliable ASD diagnosis can enable the effective preparation of treatment regimens. This paper proposes structural Magnetic Resonance Imaging (sMRI)-based ASD diagnosis via an outlier detection approach. To learn spatio-temporal patterns in structural brain connectivity, a Generative Adversarial Network (GAN) is trained exclusively with sMRI scans of healthy subjects. Given a stack of three adjacent slices as input, the GAN generator reconstructs the next three adjacent slices; the GAN discriminator then identifies ASD sMRI scan reconstructions as outliers. This model is compared against two other baselines-a simpler UNet and a sophisticated Self-Attention GAN. Axial, Coronal and Sagittal sMRI slices from the multi-site ABIDE II dataset are used for evaluation. Extensive experiments reveal that our ASD detection framework performs comparably with the state-of-the-art with far fewer training data. Furthermore, longitudinal data (two scans per subject over time) achieve 17-28% higher accuracy than cross-sectional data (one scan per subject). Among other findings, metrics employed for model training as well as reconstruction loss computation impact detection performance, and the coronal modality is found to best encode structural information for ASD detection.

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Section: Introductionmentioning

confidence: 99%

Outlier-Based Autism Detection Using Longitudinal Structural MRI

et al. 2022

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“…In addition, colocation with autism services does not necessarily mean that those services are being used; other barriers, such as the quality of the service, affordability and insurance coverage, and wait times to access services could impact use, even when services are in close proximity. 42 …”

Section: Discussionmentioning

confidence: 99%

“…41 Overall, 90% of children receiving autism services in school exceeded the threshold for autism based on criteria from the Autism Diagnostic Observation Schedule (first and second editions). 42 An estimated 85.1% to 93.6% of children with an autism diagnosis are receiving autism services in school. 43 Unlike the medical system, nearly all children in the country interact with schools regularly, regardless of their race and ethnicity.…”

Section: Methodsmentioning

confidence: 99%

Racial and Ethnic Disparities in Geographic Access to Autism Resources Across the US

et al. 2023

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ImportanceWhile research has identified racial and ethnic disparities in access to autism services, the size, extent, and specific locations of these access gaps have not yet been characterized on a national scale. Mapping comprehensive national listings of autism health care services together with the prevalence of autistic children of various races and ethnicities and evaluating geographic regions defined by localized commuting patterns may help to identify areas within the US where families who belong to minoritized racial and ethnic groups have disproportionally lower access to services.ObjectiveTo evaluate differences in access to autism health care services among autistic children of various races and ethnicities within precisely defined geographic regions encompassing all serviceable areas within the US.Design, Setting, and ParticipantsThis population-based cross-sectional study was conducted from October 5, 2021, to June 3, 2022, and involved 530 965 autistic children in kindergarten through grade 12. Core-based statistical areas (CBSAs; defined as areas containing a city and its surrounding commuter region), the Civil Rights Data Collection (CRDC) data set, and 51 071 autism resources (collected from October 1, 2015, to December 18, 2022) geographically distributed into 912 CBSAs were combined and analyzed to understand variation in access to autism health care services among autistic children of different races and ethnicities. Six racial and ethnic categories (American Indian or Alaska Native, Asian, Black or African American, Hispanic or Latino, Native Hawaiian or other Pacific Islander, and White) assigned by the US Department of Education were included in the analysis.Main Outcomes and MeasuresA regularized least-squares regression analysis was used to measure differences in nationwide resource allocation between racial and ethnic groups. The number of autism resources allocated per autistic child was estimated based on the child’s racial and ethnic group. To evaluate how the CBSA population size may have altered the results, the least-squares regression analysis was run on CBSAs divided into metropolitan (&gt;50 000 inhabitants) and micropolitan (10 000-50 000 inhabitants) groups. A Mann-Whitney U test was used to compare the model estimated ratio of autism resources to autistic children among specific racial and ethnic groups comprising the proportions of autistic children in each CBSA.ResultsAmong 530 965 autistic children aged 5 to 18 years, 83.9% were male and 16.1% were female; 0.7% of children were American Indian or Alaska Native, 5.9% were Asian, 14.3% were Black or African American, 22.9% were Hispanic or Latino, 0.2% were Native Hawaiian or other Pacific Islander, 51.7% were White, and 4.2% were of 2 or more races and/or ethnicities. At a national scale, American Indian or Alaska Native autistic children (β = 0; 95% CI, 0-0; P = .01) and Hispanic autistic children (β = 0.02; 95% CI, 0-0.06; P = .02) had significant disparities in access to autism resources in comparison with White autistic children. When evaluating the proportion of autistic children in each racial and ethnic group, areas in which Black autistic children (&gt;50% of the population: β = 0.05; &lt;50% of the population: β = 0.07; P = .002) or Hispanic autistic children (&gt;50% of the population: β = 0.04; &lt;50% of the population: β = 0.07; P &lt; .001) comprised greater than 50% of the total population of autistic children had significantly fewer resources than areas in which Black or Hispanic autistic children comprised less than 50% of the total population. Comparing metropolitan vs micropolitan CBSAs revealed that in micropolitan CBSAs, Black autistic children (β = 0; 95% CI, 0-0; P &lt; .001) and Hispanic autistic children (β = 0; 95% CI, 0-0.02; P &lt; .001) had the greatest disparities in access to autism resources compared with White autistic children. In metropolitan CBSAs, American Indian or Alaska Native autistic children (β = 0; 95% CI, 0-0; P = .005) and Hispanic autistic children (β = 0.01; 95% CI, 0-0.06; P = .02) had the greatest disparities compared with White autistic children.Conclusions and RelevanceIn this study, autistic children from several minoritized racial and ethnic groups, including Black and Hispanic autistic children, had access to significantly fewer autism resources than White autistic children in the US. This study pinpointed the specific geographic regions with the greatest disparities, where increases in the number and types of treatment options are warranted. These findings suggest that a prioritized response strategy to address these racial and ethnic disparities is needed.

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“…4 The Autism Diagnostic Observation Schedule is widely regarded as the universal standard for autism assessment. 5 The Autistic Diagnostic Interview-Revised (ADI-R)and DSM-5 to gather data to observe the pattern of autism symptoms throughout adolescence and adulthood. 6,7 Parent training, Cognitive-Behavioral Therapy (CBT), Treatment and Education of Autistic and Related Communication-Handicapped Children (TEACCH), and applied behavioral analysis are all treatment options for autism.…”

Section: Introductionmentioning

confidence: 99%

An Improved AlexNet Model and Cepstral Coefficient-Based Classification of Autism Using EEG

et al. 2023

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Autism is a neurodevelopmental disorder that cannot be completely cured, but early intervention during childhood can improve outcomes. Identifying autism spectrum disorder (ASD) has relied on subjective detection methods that involve questionnaires, medical professionals, and therapists and are subject to observer variability. The need for early diagnosis and the limitations of subjective detection methods has led researchers to explore machine learning-based approaches, such as Random Forests, K-Nearest Neighbors, Naive Bayes, and Support Vector Machines, to predict ASD meltdowns. In recent years, deep learning techniques have gained traction for early ASD detection. This study evaluates the performance of various deep learning networks, including AlexNet, VGG16, and ResNet50, using 5 cepstral coefficient features for ASD detection. The main contributions of this study are the utilization of Cepstral Coefficients in the processing stage to construct spectrograms and the modification of the AlexNet architecture for precise classification. Experimental observations indicate that the AlexNet with Linear Frequency Cepstral Coefficients (LFCC) yields the highest accuracy of 85.1%, while a customized AlexNet with LFCC achieves 90% accuracy.

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Concordance between a U.S. Educational Autism Classification and the Autism Diagnostic Observation Schedule

Cited by 12 publications

References 22 publications

Outlier-Based Autism Detection Using Longitudinal Structural MRI

Outlier-Based Autism Detection Using Longitudinal Structural MRI

Racial and Ethnic Disparities in Geographic Access to Autism Resources Across the US

An Improved AlexNet Model and Cepstral Coefficient-Based Classification of Autism Using EEG

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