• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.179-186
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• 2023

For the future prosperity of any society, the sound growth of children is essential. Autism Spectrum Disorder (ASD) is a neurobehavioral disorder which has an impact on social interaction of autistic child and has an undesirable effect on his learning, speaking, and responding skills. These children have over or under sensitivity issues of touching, smelling, and hearing. Its symptoms usually appear in the child of 4- to 11-year-old but parents did not pay attention to it and could not detect it at early stages. The process to diagnose in recent time is clinical sessions that are very time consuming and expensive. To complement the conventional method, machine learning techniques are being used. In this way, it improves the required time and precision for diagnosis. We have applied TFLite model on image based dataset to predict the autism based on facial features of child. Afterwards, various machine learning techniques were trained that includes Logistic Regression, KNN, Gaussian Naïve Bayes, Random Forest and Multi-Layer Perceptron using Autism Spectrum Quotient (AQ) dataset to improve the accuracy of the ASD detection. On image based dataset, TFLite model shows 80% accuracy and based on AQ dataset, we have achieved 100% accuracy from Logistic Regression and MLP models.

• Phonetics and Speech Sciences
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• v.15 no.2
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• pp.53-59
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• 2023

Detection of children with autism spectrum disorder (ASD) based on speech has relied on predefined feature sets due to their ease of use and the capabilities of speech analysis. However, clinical impressions may not be adequately captured due to the broad range and the large number of features included. This paper demonstrates that the knowledge-driven speech features (KDSFs) specifically tailored to the speech traits of ASD are more effective and efficient for detecting speech of ASD children from that of children with typical development (TD) than a predefined feature set, extended Geneva Minimalistic Acoustic Standard Parameter Set (eGeMAPS). The KDSFs encompass various speech characteristics related to frequency, voice quality, speech rate, and spectral features, that have been identified as corresponding to certain of their distinctive attributes of them. The speech dataset used for the experiments consists of 63 ASD children and 9 TD children. To alleviate the imbalance in the number of training utterances, a data augmentation technique was applied to TD children's utterances. The support vector machine (SVM) classifier trained with the KDSFs achieved an accuracy of 91.25%, surpassing the 88.08% obtained using the predefined set. This result underscores the importance of incorporating domain knowledge in the development of speech technologies for individuals with disorders.

• ETRI Journal
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• v.44 no.4
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• pp.613-623
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• 2022

Autism spectrum disorder (ASD) is a developmental disorder associated with cognitive and neurobehavioral disorders. It affects the person's behavior and performance. Autism affects verbal and non-verbal communication in social interactions. Early screening and diagnosis of ASD are essential and helpful for early educational planning and treatment, the provision of family support, and for providing appropriate medical support for the child on time. Thus, developing automated methods for diagnosing ASD is becoming an essential need. Herein, we investigate using various machine learning methods to build predictive models for diagnosing ASD in children using facial images. To achieve this, we used an autistic children dataset containing 2936 facial images of children with autism and typical children. In application, we used classical machine learning methods, such as support vector machine and random forest. In addition to using deep-learning methods, we used a state-of-the-art method, that is, automated machine learning (AutoML). We compared the results obtained from the existing techniques. Consequently, we obtained that AutoML achieved the highest performance of approximately 96% accuracy via the Hyperpot and tree-based pipeline optimization tool optimization. Furthermore, AutoML methods enabled us to easily find the best parameter settings without any human efforts for feature engineering.

• Clinical and Experimental Pediatrics
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• v.64 no.7
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• pp.339-340
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• 2021

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.31 no.3
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• pp.105-120
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• 2020

Although autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by social impairments, patients with ASD frequently manifest atypical sensory behaviors. Recently, atypical sensory perception in ASD has received much attention, yet little is known about its cause or neurobiology. Herein, we review the findings from neuroimaging studies related to visual perception in ASD. Specifically, we examined the neural underpinnings of visual detection, motion perception, and face processing in ASD. Results from neuroimaging studies indicate that atypical visual perception in ASD may be influenced by attention or higher order cognitive mechanisms, and atypical face perception may be affected by disrupted social brain network. However, there is considerable evidence for atypical early visual processing in ASD. It is likely that visual perceptual abnormalities are independent of deficits of social functions or cognition. Importantly, atypical visual perception in ASD may enhance difficulties in dealing with complex and subtle social stimuli, or improve outstanding abilities in certain fields in individuals with Savant syndrome. Thus, future research is required to elucidate the characteristics and neurobiology of autistic visual perception to effectively apply these findings in the interventions of ASD.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.30 no.4
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• pp.145-152
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• 2019

Objectives: The detection of autism spectrum disorder (ASD) is based on behavioral observations. To build a more objective datadriven method for screening and diagnosing ASD, many studies have attempted to incorporate artificial intelligence (AI) technologies. Therefore, the purpose of this literature review is to summarize the studies that used AI in the assessment process and examine whether other behavioral data could potentially be used to distinguish ASD characteristics. Methods: Based on our search and exclusion criteria, we reviewed 13 studies. Results: To improve the accuracy of outcomes, AI algorithms have been used to identify items in assessment instruments that are most predictive of ASD. Creating a smaller subset and therefore reducing the lengthy evaluation process, studies have tested the efficiency of identifying individuals with ASD from those without. Other studies have examined the feasibility of using other behavioral observational features as potential supportive data. Conclusion: While previous studies have shown high accuracy, sensitivity, and specificity in classifying ASD and non-ASD individuals, there remain many challenges regarding feasibility in the real-world that need to be resolved before AI methods can be fully integrated into the healthcare system as clinical decision support systems.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.18 no.2
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• pp.123-129
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• 2007

Objectives: Autism is a complex neurodevelopmental spectrum disorder with a strong genetic component. Previous neurochemical and genetic studies have suggested the possible involvement of the serotonin system in autism. Tryptophan 2,3-dioxygenase(TDO2) is the rate-limiting enzyme in the catabolism of tryptophan, which is the precursor of serotonin synthesis. The aim of this study was to investigate the association between the TDO2 gene and autism spectrum disorders(ASD) in a Korean population. Methods: The patients were diagnosed with ASD on the basis of the DSM-IV diagnostic classification outlined in the Korean version of the Autism Diagnostic Interview-Revised and Autism Diagnostic Observation Schedule. The present study included the detection of four single nucleotide polymorphisms(SNPs) in the TDO2 gene(rs2292536, rs6856558, rs6830072, rs6830800) and the family-based association analysis of the single nucleotide polymorphisms in Korean ASD trios using a transmission disequilibrium test(TDT) and haplotype analysis. The family trios of 136 probands were included in analysis. 87.5% were male and 86.0% were diagnosed with autism. The mean age of the probands was $78.5{\pm}35.8$ months(range: 26-264 months). Results: Two SNPs showed no polymorphism, and there was no significant difference in transmission in the other two SNPs. We also could not find any significant transmission in the haplotype analysis(p>.05). Conclusion: We could not find any significant statistical association between the transmission of SNPs in the TDO2 gene and ASD in a Korean population. This result may not support the possible involvement of the TDO2 gene in the development of ASD, and further exploration might be needed to investigate other plausible SNP sites.

• Korean Journal of Biological Psychiatry
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• v.13 no.4
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• pp.289-298
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• 2006

Objectives : Autism is a complex neurodevelopmental spectrum disorder with a strong genetic component. Previous neurochemical and genetic studies suggested the possible involvement of glutamate N-methyl-D-aspartate(NMDA) receptor in autism. The aim of study was to investigate the association between the NMDA2B receptor gene(GRIN2B) and autism spectrum disorders(ASD) in the Korean population. Methods : The patients with ASD were diagnosed with Autism Diagnostic Interview-Revised and Autism Diagnostic Observation Schedule based on DSM-IV diagnostic classification. The present study was conducted with the detection of four single nucleotide polymorphisms(SNPs) in GRIK2 and family-based association analysis of the single nucleotide polymorphisms in Korean ASD trios using transmission disequilibrium test (TDT). Results : One hundred twenty six patients with ASD and their biological parents were analyzed. 86.5% were male and 85.1% were diagnosed as autistic disorder. The mean age was $71.9{\pm}31.6$ months(range : 26-185 months). We found that rs1805247 showed significantly preferential transmission(TDT ${\chi}^2$=12.8, p<0.001) in ASD. Conclusion : One SNP in GRIN2B gene was significantly associated with ASD in the Korean population. This result suggests the possible involvement of glutamate NMDA receptor gene in the development of ASD.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.28 no.2
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• pp.96-105
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• 2017

Objectives: The purpose of this study is to investigate the professional knowledge and perceptions of the early screening of Autism Spectrum Disorder (ASD) in practitioners who have contact with patients with ASD. Methods: A survey was carried out among 674 practitioners in total, where practitioners are defined as those who work at primary medical centers, public institutions, educational institutions and treatment institutions. The survey was carried out both online and offline, and it mainly focused on 1) knowledge about ASD symptoms, 2) knowledge about the early screening of ASD, 3) measures taken after ASD detection, 4) thoughts on the development of early screening tools for ASD, and 5) the current status of ASD treatment. The data collected were analyzed through descriptive statistics, analysis of frequency and cross tabulation analysis using SPSS WIN 22.0. Results: The results of this study suggest that the practitioners were not aware of the exact symptoms of ASD and their professional knowledge and the environment for early screening were insufficient. Furthermore, very few and inappropriate measures were taken after the detection of ASD. In addition, there was a high demand for early ASD screening tools to be used on site and, regarding treatment, the significance of the implementation of evidence based treatments as well as the continuity of relevant research came to the fore. Conclusion: It seems that there is a lack of knowledge and perception of the early screening of ASD and that education and training among practitioners is urgently required. This issue is discussed in more detail in the paper.

• Korean Journal of Psychosomatic Medicine
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• v.24 no.2
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• pp.165-173
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• 2016

Objectives : Since the awareness of autism spectrum disorders(ASD) is growing, as a result, it is increasing numbers of infants and toddlers being referred to specialized clinics for a differential diagnosis and the importance of early autism spectrum disorders detection is emphasized. This study is to know the difference between ASD and intellectual disability(ID) from comparison of the demographics, clinical characters and obstetric complications. Methods : The participants are 816 toddlers who visited the developmental delay clinic(DDC) in National Health Insurance Ilsan hospital. The number of toddlers diagnosed as ASD and ID was 324 and 492. 75 toddlers out of 114 who returned to DDC were diagnosed as ID at the first visit but 7 of them had changed diagnosis to ASD at the second visit. After compared ASD with ID from the first visit, we analyzed characters of toddlers who had the changed diagnosis to ASD at the second visit. Results : As a result, the comparison between ASD and ID at the first visit shows that the boys have higher ratio, lower obstetric complication and lower language assessment score in ASD. The toddlers who had the changed diagnosis at the second visit were all boys and they had more cases of family history of developmental delay and had lower score of receptive language developmental quotient. Conclusions : These findings suggest that sex, language characteristics and obstetric complication could be useful in the early detection of ASD.