Diagnosis of Autism Spectrum Disorder: A Review of Three Focused Interventions

Sujana, D. Swainson; Augustine, D. Peter

doi:10.1007/s42979-022-01584-1

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…However, there are two reasons that may lead to discourage its use as a tool for assessing improvements in the autistic child. Firstly, to achieve an adequate diagnosis, it is necessary to use various validated questionnaires (Sujana and Agustine, 2023). Thus, there are parameters such as the validity of the questionnaires that can condition the diagnosis made.…”

Section: Discussionmentioning

confidence: 99%

Creation of social interaction environments with robot for autistic children: analysis of assessment instruments and type of activities

Lorenzo,

López-Núñez,

Lorenzo-Lledó

et al. 2023

JET

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PurposeThe aim of the study is to conduct a thematic review of assessment tools and types of activities in the application of robotics to autistic students during the period 1996–2021 using Web of Science and Scopus. The sample consisted of 119 documents.Design/methodology/approachNowadays, emerging technologies have become increasingly prevalent across various fields of knowledge. In this regard, robotics is being increasingly applied in the educational environment. The characteristics of this tool are the ones that best suit the needs of autistic students.FindingsResults reveal that 16.80% of the studies used automatic measurement systems, whilst 15.96% of the studies used user observation and recording techniques. As for the tasks, 37.80% were focussed on imitation tasks. Amongst the practical implications is the need to include tasks that could be developed collaboratively in the regular classroom.Originality/valueWith this research, it is intended to disseminate in the scientific community what are the characteristics that should have the interaction activities between a robot and autistic students. In addition, the type of tool needed to evaluate the improvements in the interaction is proposed.

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Section: Discussionmentioning

confidence: 99%

Creation of social interaction environments with robot for autistic children: analysis of assessment instruments and type of activities

Lorenzo,

López-Núñez,

Lorenzo-Lledó

et al. 2023

JET

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“…Researchers have used wearable devices containing sensors for detection of ASD 42,43 . Application of intelligent approaches present advanced ways to economically detect ASD effected children and adults 44 . Models have been proposed in the literature describing application of different methods and approaches for ASD detection like structural MRI 45 , neural networks 46 , machine learning [47][48][49] , deep learning 50 , transfer learning 51,52 and IoT 53 .…”

Section: Related Workmentioning

confidence: 99%

Detection of autism spectrum disorder (ASD) in children and adults using machine learning

Farooq

Tehseen

Sabir

et al. 2023

Sci Rep

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Autism spectrum disorder (ASD) presents a neurological and developmental disorder that has an impact on the social and cognitive skills of children causing repetitive behaviours, restricted interests, communication problems and difficulty in social interaction. Early diagnosis of ASD can prevent from its severity and prolonged effects. Federated learning (FL) is one of the most recent techniques that can be applied for accurate ASD diagnoses in early stages or prevention of its long-term effects. In this article, FL technique has been uniquely applied for autism detection by training two different ML classifiers including logistic regression and support vector machine locally for classification of ASD factors and detection of ASD in children and adults. Due to FL, results obtained from these classifiers have been transmitted to central server where meta classifier is trained to determine which approach is most accurate in the detection of ASD in children and adults. Four different ASD patient datasets, each containing more than 600 records of effected children and adults have been obtained from different repository for features extraction. The proposed model predicted ASD with 98% accuracy (in children) and 81% accuracy (in adults).

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Machine Learning Methodologies for Predicting Autism Spectrum Disorder across Generations

Keren,

Keziah,

Rubesh Kumar

et al. 2024

2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)

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Diagnosis of Autism Spectrum Disorder: A Review of Three Focused Interventions

Cited by 3 publications

References 34 publications

Creation of social interaction environments with robot for autistic children: analysis of assessment instruments and type of activities

Creation of social interaction environments with robot for autistic children: analysis of assessment instruments and type of activities

Detection of autism spectrum disorder (ASD) in children and adults using machine learning

Machine Learning Methodologies for Predicting Autism Spectrum Disorder across Generations

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