Autism Spectrum Disorders: Diagnosis and Treatment

Lordan, Ronan; Storni, Cristiano; Benedictis, Chiara A. De

doi:10.36255/exonpublications.autismspectrumdisorders.2021.diagnosis

Cited by 10 publications

(18 citation statements)

References 106 publications

(128 reference statements)

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“…It is an atypical antipsychotic, aripiprazole. Secretory hormone, vitamin B6, and magnesium are all helpful for ASD [12].…”

Section: Pharmacological Interventionsmentioning

confidence: 99%

Influencing factors and intervention therapy of the Autism Spectrum Disorder (ASD)

Huang¹

2022

HSET

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Autism spectrum disorder (ASD) is a kind of the disorder which is characterized by continuous social interaction and communication disorders, narrow interests and repetitive stereotyped behaviors are the main core symptoms of neurodevelopmental disorders. At the same time, the incidence of the disease increases rapidly and is extremely harmful, which brings huge economic burden to families and society. Therefore, early identification and early intervention are urgently needed. At present, the pathogenesis of ASD is not clear, and there is no perfect diagnostic tools and methods. So far, scholars are still exploring the etiology and pathogenesis of ASD. In the past, researchers have paid attention to the study of twins and found that the prevalence of monozygotic twins is higher than that of dizygotic twins, so as to try to explain the ASD from a genetic perspective.Academic circles generally believe that ASD is caused by the joint action of genetics and environment. In order to find the objective indicators of early recognition of ASD individuals, researchers have actively carried out research on ASD infants, such as twins, the overall processing of isolated faces,event-related potentials (ERP) and other monitoring methods. Traditional ASD psychotherapy mostly focuses on behavioral rehabilitation training, supplemented by some drug treatment.Advanced spatial information technology has certain advantages and potentials for the treatment of autism spectrum disorders.However, at present, the research in this field is relatively scattered and cannot be popularized.

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“…It is an atypical antipsychotic, aripiprazole. Secretory hormone, vitamin B6, and magnesium are all helpful for ASD [12].…”

Section: Pharmacological Interventionsmentioning

confidence: 99%

Influencing factors and intervention therapy of the Autism Spectrum Disorder (ASD)

Huang¹

2022

HSET

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“…Gold-standard examinations such as ADOS and the Autism Diagnostic Interview-Revised can still be biased by examiner-child interactions and caregiver memory. Intrinsic constraints in clinical instruments, such as a lower age limit for diagnosis, limit early detection of ASD symptoms [4]. The DSM-5 ignores critical ASD behaviours such as di culties in play, social anxiety, language delays, and behavioural issues, potentially resulting in missed or incorrect diagnoses and reducing diagnostic accuracy by failing to capture nuances such as poor imitation skills, leading to misdiagnosis or delayed recognition of ASD [5].…”

Section: Introductionmentioning

confidence: 99%

A Dual Track Feature Fusion Network for ASD Detection using Swin Transformers and Convolutional Neural Network

Radhakrishnan,

Ramamurthy,

Shanmugam

et al. 2024

Preprint

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Early diagnosis of Autism Spectrum Disorder (ASD) plays a crucial role in enriching a child's development, particularly in improving social communication, language development, and addressing behavioural challenges. Early signs of autism may be observable in childhood, but a formal diagnosis often occurs later in life. Behavioural-based assessments, such as the Autism Diagnostic Interview-Revised (ADI-R) and Autism Diagnostic Observation Schedule-Revised (ADOS-R), are currently used for diagnosing ASD. These methods of diagnosis are time-consuming and require trained professionals. Due to these disadvantages of the traditional method of diagnosis, deep learning is used, where feature extraction is done automatically from Magnetic Resonance Imaging (MRI) data, eliminating the reliance on subjective pre-defined features. This advancement not only captures subtle information that may be missed by human-defined features but also enhances accuracy significantly. The dataset comprises of axial view of MRI images from ABIDE-I dataset from Autism Brain Imaging Data Exchange (ABIDE) database. This study proposes a dual-track feature fusion network architecture comprising Swin Transformer and customised Convolutional Neural Network (CNN) for precise classification. Swin Transformers excel in capturing long-range dependencies within images, facilitating a deeper understanding of interrelations among different image components. Concurrently, CNNs are adept at extracting local features, thus contributing to improved classification performance by considering both local and global features. The experimental outcomes highlight the efficacy of the proposed feature fusion network, showcasing an accuracy rate of 98.7%, precision of 98.12%, recall of 98.77%, and an F1-score of 98.65% upon evaluation using the ABIDE dataset.

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“…Currently, it is estimated that over 75 million people worldwide are living with this disorder, with 1 in every 36 children at the age of 8 years affected by it in the US [2]. Traditionally, ASD has been diagnosed with evaluations conducted by trained specialists, based on guidelines such as the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5), and the Modified Checklist for Autism in Toddlers (M-CHAT) [3]. However, this approach is not without issues and despite numerous attempts to diagnose autism utilizing chemical and biological means, no reliable biomarkers exist that could enable earlier diagnosis [4] as there is much to elucidate regarding ASD's etiology and pathogenesis.…”

Section: Introductionmentioning

confidence: 99%

Behavioral Classification and Characterization of Autism Spectrum Disorder in Naturalistic Settings using Classical Machine Learning

Aguiar-Pulido,

Huang,

Mojica

et al. 2023

LatinX in AI at Neural Information Processing Systems Conference 2023

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Autism Spectrum Disorder (ASD) is a group of complex neurodevelopmental disorders that affects about 1% of the world’s population, impacting the quality of life of not only the diagnosed individuals but also their communities. Early detection and intervention are paramount to limit its effect on a child’s development, however overlap with other disorders and medical comorbidities make these tasks challenging. The present study explores the use of a novel multimodal, interpretable approach to characterize ASD children’s behavior in a naturalistic environment. Spatial (real-time location tracking), audio and demographic data from children in a classroom setting are integrated and analyzed to identify traits potentially connected to ASD. Our findings point to the use of this type of approach as a potential tool for screening individuals in a naturalistic setting, allowing for further evaluation and, ultimately, earlier diagnosis by a clinician. Results show good classification performance and suggest vocalization, speech, proximity and certain movement-related features to be impacted in ASD.

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Autism Spectrum Disorders: Diagnosis and Treatment

Cited by 10 publications

References 106 publications

Influencing factors and intervention therapy of the Autism Spectrum Disorder (ASD)

Influencing factors and intervention therapy of the Autism Spectrum Disorder (ASD)

A Dual Track Feature Fusion Network for ASD Detection using Swin Transformers and Convolutional Neural Network

Behavioral Classification and Characterization of Autism Spectrum Disorder in Naturalistic Settings using Classical Machine Learning

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