Autism screening: The perfect vs the good

In the past two decades, several screening instruments were developed to detect toddlers who may be autistic both in clinical and unselected samples. Among others, the Quantitative CHecklist for Autism in Toddlers (Q-CHAT) is a quantitative and normally distributed measure of autistic traits that demonstrates good psychometric properties in different settings and cultures. Recently, machine learning (ML) has been applied to behavioral science to improve the classification performance of autism screening and diagnostic tools, but mainly in children, adolescents, and adults. In this study, we used ML to investigate the accuracy and reliability of the Q-CHAT in discriminating young autistic children from those without. Five different ML algorithms (random forest (RF), naïve Bayes (NB), support vector machine (SVM), logistic regression (LR), and K-nearest neighbors (KNN)) were applied to investigate the complete set of Q-CHAT items. Our results showed that ML achieved an overall accuracy of 90%, and the SVM was the most effective, being able to classify autism with 95% accuracy. Furthermore, using the SVM–recursive feature elimination (RFE) approach, we selected a subset of 14 items ensuring 91% accuracy, while 83% accuracy was obtained from the 3 best discriminating items in common to ours and the previously reported Q-CHAT-10. This evidence confirms the high performance and cross-cultural validity of the Q-CHAT, and supports the application of ML to create shorter and faster versions of the instrument, maintaining high classification accuracy, to be used as a quick, easy, and high-performance tool in primary-care settings.

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The Caregiver Reported Autistic Symptoms in Preschool Children: Findings of Chandigarh Autism Screening Instrument (CASI) Linked Screening from North India

Journal of Neurosciences in Rural Practice ◽

10.1055/s-0040-1721167 ◽

2020 ◽

Author(s):

Jaison Joseph ◽

Komal Hooda ◽

Indu Chauhan ◽

Komal Dhull

Keyword(s):

Socioeconomic Status ◽

Preschool Children ◽

Neurodevelopmental Disorder ◽

Communication Disorders ◽

Screening Instrument ◽

Screening Tools ◽

North India ◽

Cutoff Score ◽

Autism Screening ◽

Autistic Symptoms

Abstract Background Autism is a neurodevelopmental disorder and can be early detected with the aid of screening tools. Chandigarh autism screening instrument (CASI) is a newly developed tool to screen autistic symptoms among children aged between 1.5 to 10 years in the north Indian Hindi speaking population. Objective In this study, we evaluated the caregiver report of autistic symptoms in preschool children (3–6 years) attending selected schools of Rohtak. Methods The index study was conducted among 225 caregivers of school-going children aged between 3 to 6 years. Social and communication disorders checklist (SCDC-Hindi) and CASI was used to measure autistic symptoms. The modified Kuppuswamy scale was used for assessing the socioeconomic status of the caregivers. Results The autistic symptoms varied from 2.2 to 18.7%, depending upon the CASI (cutoff score of 10) and SCDC (cutoff score of 9) measurements. The items in the shorter four-item version (CASI Bref) of CASI were found to be the predictors of autistic symptoms in this population. Children’s gender, age, and socioeconomic status were not found to have any association with autistic symptoms in this setting. Conclusion The study provides preliminary evidence in relation to the CASI-linked screening for autistic symptoms among preschool children. The shorter version of CASI (CASI Bref) can be an efficient quick screener for autistic traits, but the full version of CASI needs to be validated as per age-appropriate autism screening tools.

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Erroneous NICE guidance on autism screening

The Lancet Psychiatry ◽

10.1016/s2215-0366(21)00065-1 ◽

2021 ◽

Vol 8 (4) ◽

pp. 276-277

Author(s):

Lucy H Waldren ◽

Rachel A Clutterbuck ◽

Punit Shah

Keyword(s):

Autism Screening ◽

Nice Guidance

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Q-CHAT-NAO: A robotic approach to autism screening in toddlers

Journal of Biomedical Informatics ◽

10.1016/j.jbi.2021.103797 ◽

2021 ◽

pp. 103797

Author(s):

Rubén Romero-García ◽

Rafael Martínez-Tomás ◽

Pilar Pozo ◽

Félix la Paz ◽

Encarnación Sarriá

Keyword(s):

Robotic Approach ◽

Autism Screening

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Epidemiological study on the involvements of environmental factors and allergy in child mental health using the Autism Screening Questionnaire

Research in Autism Spectrum Disorders ◽

10.1016/j.rasd.2012.06.003 ◽

2013 ◽

Vol 7 (1) ◽

pp. 132-140 ◽

Cited By ~ 7

Author(s):

Aki Shibata ◽

Yoshiaki Hitomi ◽

Yasuhiro Kambayashi ◽

Yuri Hibino ◽

Masami Yamazaki ◽

...

Keyword(s):

Mental Health ◽

Environmental Factors ◽

Epidemiological Study ◽

Child Mental Health ◽

Screening Questionnaire ◽

Autism Screening

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Autism Screening and Early Identification: If You See Something, Say Something

Diagnosing and Caring for the Child with Autism Spectrum Disorder ◽

10.1007/978-3-030-26531-1_3 ◽

2019 ◽

pp. 19-31

Author(s):

Tina Iyama-Kurtycz

Keyword(s):

Early Identification ◽

Autism Screening

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Genotype-Based Deep Learning in Autism Spectrum Disorder: Diagnostic Classification and Prognostic Prediction Using Common Genetic Variants (Preprint)

10.2196/preprints.24754 ◽

2020 ◽

Author(s):

Haishuai Wang ◽

Paul Avillach

Keyword(s):

Machine Learning ◽

Autism Spectrum Disorder ◽

Deep Learning ◽

The United States ◽

Autism Spectrum ◽

Repetitive Behaviors ◽

Spectrum Disorder ◽

Screening Tools ◽

Common Variants ◽

Autism Screening

BACKGROUND In the United States, about 3 million people have autism spectrum disorder (ASD), and around 1 out of 59 children are diagnosed with ASD. People with ASD have characteristic social communication deficits and repetitive behaviors. The causes of this disorder remain unknown; however, in up to 25% of cases, a genetic cause can be identified. Detecting ASD as early as possible is desirable because early detection of ASD enables timely interventions in children with ASD. Identification of ASD based on objective pathogenic mutation screening is the major first step toward early intervention and effective treatment of affected children. OBJECTIVE Recent investigation interrogated genomics data for detecting and treating autism disorders, in addition to the conventional clinical interview as a diagnostic test. Since deep neural networks perform better than shallow machine learning models on complex and high-dimensional data, in this study, we sought to apply deep learning to genetic data obtained across thousands of simplex families at risk for ASD to identify contributory mutations and to create an advanced diagnostic classifier for autism screening. METHODS After preprocessing the genomics data from the Simons Simplex Collection, we extracted top ranking common variants that may be protective or pathogenic for autism based on a chi-square test. A convolutional neural network–based diagnostic classifier was then designed using the identified significant common variants to predict autism. The performance was then compared with shallow machine learning–based classifiers and randomly selected common variants. RESULTS The selected contributory common variants were significantly enriched in chromosome X while chromosome Y was also discriminatory in determining the identification of autistic from nonautistic individuals. The ARSD, MAGEB16, and MXRA5 genes had the largest effect in the contributory variants. Thus, screening algorithms were adapted to include these common variants. The deep learning model yielded an area under the receiver operating characteristic curve of 0.955 and an accuracy of 88% for identifying autistic from nonautistic individuals. Our classifier demonstrated a significant improvement over standard autism screening tools by average 13% in terms of classification accuracy. CONCLUSIONS Common variants are informative for autism identification. Our findings also suggest that the deep learning process is a reliable method for distinguishing the diseased group from the control group based on the common variants of autism.

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