• Therapeutic Science for Rehabilitation
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• v.13 no.2
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• pp.9-25
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• 2024

Objective : Children with autism spectrum disorder (ASD) commonly suffer from feeding disorders. Major feeding problems include mealtime behavior problems, picky eating, and a lack of food variety can lead to nutritional problems, developmental and social limitations, and stress for the caregivers. A review of the latest literature was conducted to gain an in-depth understanding of assessment tools for feeding disorders in children with ASD. Method : This study analyzed assessments to identify feeding problems in ASD based on previous studies searched through keywords such as ASD, ASD feeding problem, and ASD feeding evaluation. Results : The ASD feeding disorder assessment was divided into direct and indirect assessments. Indirect assessment, in which caregivers measure a child's situation using questionnaires, is mainly used. The assessment of feeding disorders in children with ASD was divided into 1) mealtime behavior, 2) sensory processing, 3) food consumption, and 4) others. Conclusion : As the main feeding disorder characteristics of children with ASD are very diverse, a comprehensive evaluation is necessary but is still limited. Swallowing rehabilitation experts, such as occupational therapists, should apply comprehensive assessment tools based on a basic understanding of the feeding problems, behaviors, and sensations in ASD.

• Clinical and Experimental Pediatrics
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• v.58 no.1
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• pp.8-14
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• 2015

In patients with a language developmental delay, it is necessary to make a differential diagnosis for autism spectrum disorders (ASDs), specific language impairment, and mental retardation. It is important that pediatricians recognize the signs and symptoms of ASDs, as many patients with language developmental delays are ultimately diagnosed with ASDs. Pediatricians play an important role in the early recognition of ASDs, because they are usually the first point of contact for children with ASDs. A revision of the diagnostic criteria of ASDs was proposed in the Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM-5) that was released in May 2013. The autism spectrum describes a range of conditions classified as neurodevelopmental disorders in the fifth edition of the DSM. The new diagnostic criteria encompasses previous elements from the diagnosis of autistic disorder, Asperger disorder, childhood disintegrative disorder, and pervasive developmental disorder-not otherwise specified. An additional change to the DSM includes synthesizing the section on social and communication deficits into one domain. In ASD patients, the appropriate behavioral therapies and rehabilitation treatments significantly affect the prognosis. Therefore, this makes early diagnosis and treatment very important. In conclusion, pediatricians need to be able to recognize the signs and symptoms of ASDs and be attentive to them in order to make an early diagnosis and provide treatment.

• Journal of Gifted/Talented Education
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• v.21 no.3
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• pp.595-610
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• 2011

It is hard to understand the coexistence of giftedness and disorder in an individual, but the twice-exceptional is widely recognized now. Gifted autistic spectrum disorder is one of its subtypes in which giftedness exists with autistic spectrum disorder (ASD) simultaneously. Like other constructs including giftedness, the nature of gifted ASD has not been understood in a fundamental and wholistic manner. This paper suggests a cognitive mechanism of gifted ASD based on the integrated model of human abilities(Song, 2009; Song & Porath, 2005), which explains how giftedness coexists with ASD and interacts with each other, producing the characteristics of gifted individuals with ASD. According to the suggested mechanism, the excessive growth of mental spaces in the brain may cause ASD. The over-grown mental spaces result in excessively strong short-term sensory memory and better facility of processing, promoting internal cognitive activities on one hand, but relative lack of cognitive activities in the real world space results in ASD symptoms on the other hand. The cognitive structure of gifted ASD students also contributes to the presentation of giftedness in specific domains. This study suggests that gifted individuals with ASD need to be discouraged from fully engaging in domains they are interested in or the most confident of, rather to be encouraged to invest their giftedness to overcome their ASD symptoms. This study also provides new perspectives on theoretical and educational approaches for gifted ASD.

• Industrial Engineering and Management Systems
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• v.11 no.2
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• pp.142-147
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• 2012

Individuals with autistic spectrum disorders (ASD) have difficulty inferring other people's feelings from their facial expressions and/or from situational cues, and therefore, they are less able to respond with prosocial behavior. We developed a computer-based training program to help teach the connection between facial-based or situation-based emotions and prosocial behavioral responses. An 8-year-old male school child with ASD participated in the study. In this program, he was trained to identify persons in need of help and appropriate prosocial responses using novel photo-based scenarios. When he misidentified emotions from photographs of another's face, the program highlighted those parts of the face which effectively communicate emotion. To increase the likelihood that he would learn a generalized repertoire of emotional understanding, multiple examples of emotional expressions and situations were provided. When he misidentified persons expressing a need for help, or failed to identify appropriate helping behaviors, role playing was used to help him appreciate the state of mind of a person in need of help. The results of the training indicated increases in prosocial behaviors during a laboratory task that required collaborative work. His homeroom teacher, using a behavioral rating scale, reported that he now understood another's emotion or situation better than before training. These findings indicate the effects of the training are not limited to the artificial experiment situation, but also carried over to his school life.

• Journal of the Korea Convergence Society
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• v.9 no.3
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• pp.153-163
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• 2018

The purpose of this research is to investigate the research trends on the clinical use of robots for children with autistic spectrum disorders. In order to understand research trends in the context of the clinical use of robots for children with ASD, recent studies on the use of robot in the educational and therapeutic intervention were examined. Critical literature review is used as research method. Recent studies of 17 articles are chosen with two filters of 1) publication years since 2009, and 2) two key-words; robot and ASD. Further, literature on research trends is scrutinized and categorized according to the kinds of robots that are used, the types of independent and dependent variables, and research methods. The result of this research indicated that recent years, the clinical use of interactive robots with children with disability has received considerable attention in light of the proven utility of educational and therapeutic intervention. Rapid progress in robotics, especially in the area of ASD, offers tremendous possibilities for innovation in treatment for children with ASD. In conclusion, this study addresses the need of further study on the implementation procedures and protocols of clinical robots that will make the adoption feasible and easy.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.34 no.1
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• pp.51-56
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• 2023

Objectives: Regression, while not a core symptom of autism spectrum disorder (ASD), has been suggested to be a distinct subtype by previous studies. Therefore, this study aimed to explore the prevalence and clinical differences between those with and without regression in children with ASD. Methods: This study includes data from toddlers and young children aged 2-7 years acquired from other projects at Seoul National University Bundang Hospital. The presence and characteristics of regression were explored using question items #11-28 from the Autism Diagnostic Interview-Revised. Chi-square and independent t-tests were used to compare various clinical measurements such as autistic symptoms, adaptative behavior, intelligence, and perinatal factors. Results: Data from 1438 young children (1020 with ASD) were analyzed. The overall prevalence rate of regression, which was mainly related to language-related skills, was 10.2% in the ASD group, with an onset age of 24 months. Regarding clinical characteristics, patients with ASD and regression experienced ASD symptoms, especially restricted and repetitive interests and behaviors, with greater severity than those without regression. Furthermore, there were significant associations between regression and hypertension/placenta previa. Conclusion: In-depth surveillance and proactive interventions targeted at young children with ASD and regression should focus on autistic symptoms and other areas of functioning.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.5 no.1
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• pp.108-117
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• 1994

Twenty nine children with autism and thirty children with mental retardation were examined for association between autism and chromosomal disorders including fragile X. The peripheral blood was cultured in Medium 199 with methotrexate and without methorexate for 70 hours. Thirty metaphase cells in each case were karyotyped in all samples. Chromosomal abnormalities were found in 11 cases(37.9%) of autistic disorder and 10 cases (33.3%) of mental retardation, but in none of fragile(X)(q27.3) from all cases. Chromosomal abnormalities were present on group A, C, D and X in autistic disorder and on group A, B, C, D, E and X in mental retardation. No specific chromosomal region was found in both autistic disorder and mental retardation. Types of chromosomal disorders were only fragile and/or gap but no numerical abnormality was present in all cases. Number of cells which revealed fragile sites were 31 cells(3.6%) out of 870 cells in autistic disorder and 29 cells(3.2%) out of 900 cells in mental retardation Number of cells which revealed gaps were 43 cells(4.9%) out of 870 cells in autistic disorder and 35 cells(3.9%) out of 900 cells in mental retardation. Autistic disorder may not be directly correlated with fragile X but with nonspecific chromosomal breakages from these data.

• Journal of Digital Convergence
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• v.16 no.11
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• pp.411-418
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• 2018

The purpose of this study was to estimate the current status and trends of healthcare utilization among people with ASD. Using National Health Insurance open database, from 2010 to 2017. We analyzed the treatment prevalence for people with ASD, the pattern of healthcare utilization, the difference in medical care utilization according to age, and the type and location of main medical institutions. The main results of the study are as follows: First, the medical utilization has been continuously increasing from 2010 to 2017. The total amount of medical utilization is increased by 50% in 2017 compared to 2010, and the treatment prevalence was estimated to be 79.1% in 2017 and medical uses for the next three years is also increasing. Second, the pattern of medical care utilization varied widely according to age, especially after 20 years of age. Third, the types of medical institutions that were mainly used were 45.6% in the medical clinic and 35.9% in Seoul. The results of this study can be used as a minimum reference point of evaluating the effectiveness of government policy on future autistic disorders. However, further studies are required to increase the prevalence of treatment for autistic patients and to find out the difference in medical use according to age.

• Journal of IKEEE
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• v.23 no.4
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• pp.1440-1447
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• 2019

Currently, the prevalence of autism spectrum disorders in children is reported to be higher and shows various types of disorders. In particular, they are having difficulty in communication due to communication impairment in the area of social communication and need to be improved through training. Thus, this study proposes a method of acquiring voice information through a microphone mounted on a robot designed through preliminary research and using this information to make intelligent motions. An ANN(Artificial Neural Network) was used to classify the speech data into robot motions, and we tried to improve the accuracy by combining the Recurrent Neural Network based on Convolutional Neural Network. The preprocessing of input speech data was analyzed using MFCC(Mel-Frequency Cepstral Coefficient), and the motion of the robot was estimated using various data normalization and neural network optimization techniques. In addition, the designed ANN showed a high accuracy by conducting an experiment comparing the accuracy with the existing architecture and the method of human intervention. In order to design robot motions with higher accuracy in the future and to apply them in the treatment and education environment of children with autism.

• 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.