Impaired social functioning is a hallmark feature of autism spectrum disorder (ASD), often requiring treatment throughout the life span. PEERS(®) (Program for the Education and Enrichment of Relational Skills) is a parent-assisted social skills training for teens with ASD. Although PEERS(®) has an established evidence base in improving the social skills of adolescents and young adults with ASD in North America, the efficacy of this treatment has yet to be established in cross-cultural validation trials. The objective of this study is to examine the feasibility and treatment efficacy of a Korean version of PEERS(®) for enhancing social skills through a randomized controlled trial (RCT).The English version of the PEERS(®) Treatment Manual (Laugeson & Frankel, 2010) was translated into Korean and reviewed by 21 child mental health professionals. Items identified as culturally sensitive were surveyed by 447 middle school students, and material was modified accordingly. Participants included 47 teens between 12 and 18 years of age with a diagnosis of ASD and a verbal intelligence quotient (IQ) ≥ 65. Eligible teens were randomly assigned to a treatment group (TG) or delayed treatment control group (CG). Primary outcome measures included questionnaires and direct observations quantifying social ability and problems directly related to ASD. Secondary outcome measures included scales for depressive symptoms, anxiety, and other behavioral problems. Rating scales for parental depressive symptoms and anxiety were examined to detect changes in parental psychosocial functioning throughout the PEERS(®) treatment. Independent samples t-tests revealed no significant differences at baseline across the TG and CG conditions with regard to age (14.04 ± 1.64 and 13.54 ± 1.50 years), IQ (99.39 ± 18.09 & 100.67 ± 16.97), parental education, socioeconomic status, or ASD symptoms (p < 0.05), respectively. Results for treatment outcome suggest that the TG showed significant improvement in communication and social interaction domain scores on the Autism Diagnostic Observation Schedule, interpersonal relationship and play/leisure time on the subdomain scores of the Korean version of the Vineland Adaptive Behavior Scale (p's < 0.01), social skills knowledge total scores on the Test of Adolescent Social Skills Knowledge-Revised (p < 0.01), and decreased depressive symptoms on the Child Depression Inventory following treatment (p < 0.05). Analyses of parental outcome reveal a significant decrease in maternal state anxiety in the TG after controlling for potential confounding variables (p < 0.05). Despite cultural and linguistic differences, the PEERS(®) social skills intervention appears to be efficacious for teens with ASD in Korea with modest cultural adjustment. In an RCT, participants receiving the PEERS(®) treatment showed significant improvement in social skills knowledge, interpersonal skills, and play/leisure skills, as well as a decrease in depressive symptoms and ASD symptoms. This study represents one of only a few cross-cultural val...

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.

Autism spectrum disorder (ASD) is a developmental disorder with a life-span disability. While diagnostic instruments have been developed and qualified based on the accuracy of the discrimination of children with ASD from typical development (TD) children, the stability of such procedures can be disrupted by limitations pertaining to time expenses and the subjectivity of clinicians. Consequently, automated diagnostic methods have been developed for acquiring objective measures of autism, and in various fields of research, vocal characteristics have not only been reported as distinctive characteristics by clinicians, but have also shown promising performance in several studies utilizing deep learning models based on the automated discrimination of children with ASD from children with TD. However, difficulties still exist in terms of the characteristics of the data, the complexity of the analysis, and the lack of arranged data caused by the low accessibility for diagnosis and the need to secure anonymity. In order to address these issues, we introduce a pre-trained feature extraction auto-encoder model and a joint optimization scheme, which can achieve robustness for widely distributed and unrefined data using a deep-learning-based method for the detection of autism that utilizes various models. By adopting this auto-encoder-based feature extraction and joint optimization in the extended version of the Geneva minimalistic acoustic parameter set (eGeMAPS) speech feature data set, we acquire improved performance in the detection of ASD in infants compared to the raw data set.

Although early screening is critical for individuals with autism spectrum disorder (ASD) in order to receive early intervention and improve function later in life, screening is often delayed. Limitations of existing screening instruments, and the need for a culturally appropriate early screening tool in Korean children, led us to develop Behavior Development Screening for Toddlers (BeDevel). The BeDevel assessment consists of two parts: BeDevel‐Interview, a structured interview measure for parents/primary caregivers; and BeDevel‐Play, a play‐based semi‐structured observational measure in children. To examine the feasibility and validity of BeDevel, 155 children (N = 75 ASD, N = 55 typical development, N = 25 developmentally delayed) aged 18–42 months (M = 31.54 months, SD = 7.60) were examined through parent‐reported screening questionnaires, BeDevel, and standard diagnostic assessments. When BeDevel items were analyzed using Cohen's kappa statistics, most items in BeDevel‐Interview and all items in BeDevel‐Play were reasonably consistent with diagnoses. We identified primary items, which were significantly interacted with actual diagnosis in the chi‐squared test (P < 0.05, range = 0.000–0.032). Using cutoff numbers of items determined using the receiver operating characteristics curve, BeDevel showed satisfactory levels of sensitivity (83.33%–100%), specificity (81.25%–100%), positive predictive values (80.65%–100%), and negative predictive values (83.87%–100%), as well as high internal consistency (Cronbach's α = 0.866–959). The agreement between BeDevel and most other screening/diagnostic instruments was moderate (k = 0.419–1.000). These results suggest that BeDevel can be a useful instrument for early screening of ASD. Autism Res 2019, 12: 1112–1128. © 2019 International Society for Autism Research, Wiley Periodicals, Inc. Lay Summary Although early screening is critical for individuals with autism spectrum disorder (ASD) in order to receive early intervention and improve function later in life, screening is often delayed. Limitations of existing screening instruments and the need for a culturally appropriate early screening tool in Korean children led us to develop Behavior Development Screening for Toddlers (BeDevel). The BeDevel assessment consists of two parts: BeDevel‐Interview, a structured interview measure for parents/primary caregivers; and BeDevel‐Play, a play‐based, semi‐structured observational measure in children. In order to test the feasibility and validity of BeDevel, we analyzed preliminary data of total 155 children aged 18–42 months, examined through parent‐reported screening questionnaires, BeDevel, and standard diagnostic assessments. When individual items were analyzed, responses of all BeDevel‐Interview items and of most BeDevel‐Play items well matched actual diagnoses, and we identified primary items, which were particularly useful in differentiating between the ASD group and the non‐ASD group. With the optimal screening criteria determined, the BeDevel was able to identify individuals...