scholarly journals What are we optimizing for in autism screening? Examination of algorithmic changes in the M‐CHAT

2021 ◽  
Author(s):  
Synnve Schjølberg ◽  
Frederick Shic ◽  
Fred R. Volkmar ◽  
Anders Nordahl‐Hansen ◽  
Nina Stenberg ◽  
...  
Keyword(s):  
Autism Screening ◽  
Screening Examination

scholarly journals Use of Machine Learning to Investigate the Quantitative Checklist for Autism in Toddlers (Q-CHAT) towards Early Autism Screening

Diagnostics ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 574
Author(s):  
Gennaro Tartarisco ◽  
Giovanni Cicceri ◽  
Davide Di Pietro ◽  
Elisa Leonardi ◽  
Stefania Aiello ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Performance ◽  
Behavioral Science ◽  
Autistic Traits ◽  
Classification Performance ◽  
Recursive Feature Elimination ◽  
Diagnostic Tools ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Autism Screening

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.

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

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.

scholarly journals Mass-Screening Examination of Scoliosis in School Children

1981 ◽  
Vol 30 (1) ◽  
pp. 218-221
Author(s):  
Y. Yamaguchi ◽  
S. Hattori ◽  
S. Kawai ◽  
F. Senzoku ◽  
H. Kotani ◽  
...  
Keyword(s):  
Mass Screening ◽  
School Children ◽  
Screening Examination

scholarly journals Erroneous NICE guidance on autism screening

2021 ◽  
Vol 8 (4) ◽  
pp. 276-277
Author(s):  
Lucy H Waldren ◽  
Rachel A Clutterbuck ◽  
Punit Shah
Keyword(s):  
Autism Screening ◽  
Nice Guidance

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

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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