• 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 the Korean Academy of Child and Adolescent Psychiatry
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• v.29 no.4
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• pp.178-184
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• 2018

Objectives: The objective of this study was to investigate the concordance of Diagnostic and Statistical Manual of Mental Disorders (DSM-IV and DSM-5) diagnostic criteria for autism spectrum disorder (ASD). Methods: We retrospectively reviewed the medical records of 170 subjects (age range: 3-23, 140 boys) with developmental delay or social deficit from January 2011 to July 2016 at the Department of Psychiatry of Asan Medical Center. The Autism Diagnostic Interview-Revised (ADI-R), the Autism Diagnostic Observation Schedule (ADOS), and intelligence tests were performed for each subject. Diagnosis was reviewed and confirmed for each subject with DSM-IV Pervasive Developmental Disorder (PDD) and DSM-5 ASD criteria, respectively. Results: Fifty-eight of 145 subjects (34.1%) who were previously diagnosed as having PDD in DSM-IV did not meet DSM-5 ASD criteria. Among them, 28 (48.3%) had Asperger's disorder based on DSM-IV. Most algorithm scores on ADOS and all algorithm scores on ADI-R were highest in subjects who met both DSM-IV PDD criteria and DSM-5 ASD criteria (the Convergent group), followed by subjects with a DSM-IV PDD diagnosis who did not have a DSM-5 ASD diagnosis (the Divergent group), and subjects who did not meet either DSM-IV PDD or DSM-5 ASD criteria (the non-PDD group). Intelligence quotient was lower in the Convergent group than in the Divergent group. Conclusion: The results of our study suggest that ASD prevalence estimates could be lower under DSM-5 than DSM-IV diagnostic criteria. Further prospective study on the impact of new DSM-5 ASD diagnoses in Koreans with ASD is needed.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.22 no.1
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• pp.3-9
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• 2011

Autism and autistic spectrum disorder are chronic neuro-developmental disorders characterized by social and language impairments and stereotyped, repetitive patterns of behavior. The etiology of autism remains unknown; however, a strong genetic component has been detected and environmental factors may also be involved in their etiologies. In the current study, we reviewed evidence for the presence of prenatal and perinatal factors, gastrointestinal factors, food allergies, metabolic and heavy metal factors, and other nutritional factors that may represent risk factors for the development of autism and autistic spectrum disorder.

• Therapeutic Science for Rehabilitation
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• v.10 no.4
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• pp.7-24
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• 2021

Objective : This study examined the characteristics of the literature involving a single-subject research design among positive behavior support intervention studies to improve problem behavior in children with autism spectrum disorders and assess the quality level. Methods : This is a literature study, and the targets of analysis were nine single-subject research papers published between 2011 and 2020. The subject papers were analyzed by dividing them into general characteristics and the qualitative levels according to the content of the research method. Results : Analysis of analyzing the contents of the study showed that the subjects were preschool and elementary school-age children at the same ratio. Furthermore, the study design involved mostly the middle and multiple baseline designs among the behaviors. All papers presented social validity, intervention fidelity, and observer reliability. Problem behaviors included self-injury and aggression behaviors, disturbing behaviors, and seat break-away behaviors, while the most dependent variables were measured through partial interval recording. As a result of confirming the intervention effect, the effect was confirmed in intervention, maintenance, and generalization. All analysis studies showed high-quality levels. Conclusion : This study confirmed the content and qualitative level of the thesis that applied the single-subject research design among positive behavior support intervention studies for problem behaviors of children with autism spectrum disorders. Positive behavior support intervention, an evidence-based intervention for children with autism spectrum disorders, was confirmed an effective intervention for autism spectrum 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.

• Genomics & Informatics
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• v.15 no.3
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• pp.82-86
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• 2017

Chromosomal microarray (CMA) is a high-resolution, high-throughput method of identifying submicroscopic genomic copy number variations (CNVs). CMA has been established as the first-line diagnostic test for individuals with developmental delay (DD), intellectual disability (ID), autism spectrum disorders (ASDs), and multiple congenital anomalies (MCAs). CMA analysis was performed in 42 Korean patients who had been diagnosed with unexplained DD, ID, ASDs, and MCAs. Clinically relevant CNVs were discovered in 28 patients. Variants of unknown significance were detected in 13 patients. The diagnostic yield was high (66.7%). CMA is a superior diagnostic tool compared with conventional karyotyping and fluorescent in situ hybridization.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.30 no.1
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• pp.9-16
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• 2019

Objectives: The purpose of this study was to analyze the discriminant validity and the clinical cut off scores of the Child Behavior Checklist 1.5-5 (CBCL 1.5-5) in the diagnosis of autism spectrum disorder (ASD) and non-ASD. Methods: In total, 104 ASD and 441 non-ASD infants were included in the study. T-test, discriminant analysis, receiver operating characteristic (ROC) curve analysis, and odds ratio analysis were performed on the data. Results: The discriminant validity was confirmed by mean differences and discriminant analysis on the subscales of Emotionally reactive, Somatic complaints, Withdrawn, Sleep problems, Attention problems, Aggressive behavior, Internalizing problems, Externalizing problems, and Total problems, along with the Diagnostic and Statistical Manual of Mental Disorders (DSM)-oriented scales between the two groups. ROC analysis showed that the following subscales significantly separated ASD from normal infants: Emotionally reactive, Somatic complaints, Withdrawn, Sleep problems, Attention problems, Aggressive behavior, Internalizing problems, Externalizing problems, Total problems, and DSM pervasive developmental problems. Moreover, the clinical cut off score criteria adopted in the Korean-CBCL 1.5-5 were shown to be valid for the subscales Withdrawn, Internalizing problems, Externalizing problems, Total problems, and DSM pervasive developmental problems. Conclusion: The subscales of Withdrawn, Internalizing problems, Externalizing problems, Total problems, and DSM pervasive developmental problems significantly discriminated infants with ASD.

• Journal of Digital Contents Society
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• v.18 no.1
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• pp.35-46
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• 2017

The purpose of the study was to review the recent literature on applying augmented reality based educational contents for students with autism spectrum disorders and to identify research trends and implications. The search procedures through the Web-database system were implemented to find the proper research and a total of 12 studies were included in this review. The results indicated that most of subjects were elementary school-age children, also single subject design was mostly implemented. Mobile devices were used mostly for augmented reality, and most of data collection methods was behavioral observation. Results founded several contents types: objects manipulation, manipulation for self-modeling, on-site problem solving program, and location-based learning guide. Additionally, the results indicated that the educational effectiveness was the improvements of social behaviors, play and imitation behaviors, and emotion recognition. Furthermore, considerations to develop and apply augmented reality based educational contents for students with autism spectrum disorders were suggested.

• Journal of the Korean Academy of Child and Adolescent Psychiatry
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• v.34 no.2
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• pp.117-124
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• 2023

Savant syndrome was described before autism. However, they soon became closely associated, as many of their symptoms (intellectual disability, repetitive behaviors, alterations in social communication, and islets of abilities) overlap. Only a few women with autism have been diagnosed with savant syndrome. The theories or hypotheses that attempt to explain savant syndrome, which are common in autism, present differential treatment according to sex. We postulate that savant syndrome associated with autism as well as autism in general is underdiagnosed in women.

• Sleep Medicine and Psychophysiology
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• v.20 no.2
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• pp.53-58
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• 2013

Autism Spectrum Disorder (ASD) is characterized by persistent deficits in social communication and restricted, repetitive patterns of behavior and interest. Sleep problems are not uncommon in children with autism spectrum disorders. Symptoms of insomnia are the most frequent sleep problems in individuals with ASD. Sleep problems can cause significant difficulties in the daily life of children with ASD and their families. Genetic factor, deregulations of melatonin synthesis, extraneous environmental stimuli and psychiatric and medical conditions may cause sleep problems. The first line treatment of sleep problems in ASD includes managements for potential contributing factors and parent education about sleep hygiene care for child and behavioral therapy. Supplementation with melatonin may be effective before considering other medications, such as risperidone, clonidine, and mirtazapine.