Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication and repetitive behaviors. With the rapid development of computer vision, visual behavior analysis aided diagnosis of ASD has got more and more attention. This paper reviews the research on visual behavior analysis aided diagnosis of ASD. First, the core symptoms and clinical diagnostic criteria of ASD are introduced briefly. Secondly, according to clinical diagnostic criteria, the interaction scenes are classified and introduced. Then, the existing relevant datasets are discussed. Finally, we analyze and compare the advantages and disadvantages of visual behavior analysis aided diagnosis methods for ASD in different interactive scenarios. The challenges in this research field are summarized and the prospects of related research are presented to promote the clinical application of visual behavior analysis in ASD diagnosis.
Humans ; Autism Spectrum Disorder/diagnosis* ; Vision, Ocular ; Behavior

Social (pragmatic) communication disorder (SCD) is a new diagnosis included under communication disorders in the neurodevelopmental disorders section of Diagnostic and Statistical Manual of Mental Disorders-5. SCD is defined as a primary deficit in the social use of nonverbal and verbal communication. SCD has very much in common with pragmatic language impairment, which is characterized by difficulties in understanding and using language in context and following the social rules of language, despite relative strengths in word knowledge and grammar. SCD and Autism Spectrum Disorder (ASD) are similar in that they both involve deficits in social communication skills, however individuals with SCD do not demonstrate restricted interests, repetitive behaviors, insistence on sameness, or sensory abnormalities. It is essential to rule out a diagnosis of ASD by verifying the lack of these additional symptoms, current or past. The criteria for SCD are qualitatively different from those of ASD and are not equivalent to those of mild ASD. It is clinically important that SCD should be differentiated from high-functioning ASD (such as Asperger syndrome) and nonverbal learning disabilities. The ultimate goals are the refinement of the conceptualization, development and validation of assessment tools and interventions, and obtaining a comprehensive understanding of the shared and unique etiologic factors for SCD in relation to those of other neurodevelopmental disorders.
Autism Spectrum Disorder ; Communication Disorders ; Diagnosis ; Learning Disorders ; Neurodevelopmental Disorders ; Social Communication Disorder*

OBJECTIVES: We examined the agreement between the Autism Diagnostic Observation Schedule (ADOS) and the Childhood Autism Rating Scale (CARS) in the diagnosis of autism spectrum disorder. METHODS: The ADOS and CARS scores of 78 children were retrospectively collected from a chart review. A correlation analysis was performed to examine the concurrent validity between the two measures. Using the receiver operating characteristic (ROC) curve, we determined the optimal cut-off score of the CARS for identifying autism spectrum disorder. RESULTS: The CARS score was significantly correlated with the ADOS score (r=0.808, p < 0.001). Taking ADOS as the ideal standard, the optimal cut-off scores of CARS for identifying autism and autism spectrum were 30 and 24.5, respectively. CONCLUSION: We determined the optimal cut-off scores of CARS for screening and diagnosing autism spectrum disorder.
Appointments and Schedules* ; Autism Spectrum Disorder* ; Autistic Disorder* ; Child ; Diagnosis* ; Humans ; Mass Screening ; Retrospective Studies ; ROC Curve

Extraction and analysis of electroencephalogram (EEG) signal characteristics of patients with autism spectrum disorder (ASD) is of great significance for the diagnosis and treatment of the disease. Based on recurrence quantitative analysis (RQA)method, this study explored the differences in the nonlinear characteristics of EEG signals between ASD children and children with typical development (TD). In the experiment, RQA method was used to extract nonlinear features such as recurrence rate (RR), determinism (DET) and length of average diagonal line (LADL) of EEG signals in different brain regions of subjects, and support vector machine was combined to classify children with ASD and TD. The research results show that for the whole brain area (including parietal lobe, frontal lobe, occipital lobe and temporal lobe), when the three feature combinations of RR, DET and LADL are selected, the maximum classification accuracy rate is 84%, the sensitivity is 76%, the specificity is 92%, and the corresponding area under the curve (AUC) value is 0.875. For parietal lobe and frontal lobe (including parietal lobe, frontal lobe), when the three features of RR, DET and LADL are combined, the maximum classification accuracy rate is 82%, the sensitivity is 72%, and the specificity is 92%, which corresponds to an AUC value of 0.781. The research in this paper shows that the nonlinear characteristics of EEG signals extracted based on RQA method can become an objective indicator to distinguish children with ASD and TD, and combined with machine learning methods, the method can provide auxiliary evaluation indicators for clinical diagnosis. At the same time, the difference in the nonlinear characteristics of EEG signals between ASD children and TD children is statistically significant in the parietal-frontal lobe. This study analyzes the clinical characteristics of children with ASD based on the functions of the brain regions, and provides help for future diagnosis and treatment.
Autism Spectrum Disorder/diagnosis* ; Autistic Disorder ; Brain ; Child ; Electroencephalography ; Humans ; Recurrence

OBJECTIVE: To evaluate the value of autism screening checklists in the early identification of autism spectrum disorder (ASD). METHODS: A total of 2 571 children who attended the Children's Hospital of Chongqing Medical University and completed autism screening and diagnostic test were enrolled as subjects, among whom 2 074 were diagnosed with ASD, 261 were diagnosed with global developmental delay (GDD), 206 were diagnosed with developmental language disorder (DLD), and 30 had normal development. The sensitivity, specificity, and optimal threshold value of the Modified Checklist for Autism in Toddlers (M-CHAT) and the Autism Behavior Checklist (ABC) for the early identification of ASD were evaluated by the receiver operating characteristic (ROC) curve. RESULTS: The M-CHAT had a high sensitivity of 88.3% but a low specificity of 36.0% for the identification of ASD. Its sensitivity decreased with age, and was maintained above 80% for children aged 16 to < 48 months. The ABC had a high specificity of 87.3% but a low sensitivity of 27.2%, with an optimal cut-off value of 47.5 based on the ROC curve analysis. The multivariate linear regression model based on a combination of the M-CHAT and ABC for screening of ASD showed a specificity of 85.8% and a sensitivity of 56.6%. CONCLUSIONS The M-CHAT has a high sensitivity and a low specificity in the identification of ASD, with a better effect in children aged 16 to < 48 months. The ABC has a high specificity and a low sensitivity. The multiple linear regression model method based on the combined M-CHAT and ABC to screen ASD appears to be effective.
Adolescent ; Autism Spectrum Disorder/diagnosis* ; Autistic Disorder ; Checklist ; Humans ; Infant ; Mass Screening ; ROC Curve

The early diagnosis of children with autism spectrum disorders (ASD) is essential. Electroencephalography (EEG) is one of most commonly used neuroimaging techniques as the most accessible and informative method. In this study, approximate entropy (ApEn), sample entropy (SaEn), permutation entropy (PeEn) and wavelet entropy (WaEn) were extracted from EEGs of ASD child and a control group, and Student's -test was used to analyze between-group differences. Support vector machine (SVM) algorithm was utilized to build classification models for each entropy measure derived from different regions. Permutation test was applied in search for optimize subset of features, with which the SVM model achieved best performance. The results showed that the complexity of EEGs in children with autism was lower than that of the normal control group. Among all four entropies, WaEn got a better classification performance than others. Classification results vary in different regions, and the frontal lobe showed the best performance. After feature selection, six features were filtered out and the accuracy rate was increased to 84.55%, which can be convincing for assisting early diagnosis of autism.
Algorithms ; Autism Spectrum Disorder ; classification ; diagnosis ; Child ; Electroencephalography ; Entropy ; Humans ; Support Vector Machine

OBJECTIVES: To investigate the efficacy and required indicators of Children Neuropsychological and Behavioral Scale-Revision 2016 (CNBS-R2016) in the differential diagnosis of autism spectrum disorder (ASD) and global developmental delay (GDD). METHODS: A total of 277 children with ASD and 415 children with GDD, aged 18-48 months, were enrolled as subjects. CNBS-R2016 was used to assess the developmental levels of six domains, i.e., gross motor, fine motor, adaptive ability, language, social behavior, and warning behavior, and a total of 13 indicators on intelligence age and developmental quotient (DQ) were obtained as the input features. Five commonly used machine learning classifiers were used for training to calculate the classification accuracy, sensitivity, and specificity of each classifier. RESULTS: DQ of warning behavior was selected as the first feature in all five classifiers, and the use of this indicator alone had a classification accuracy of 78.90%. When the DQ of warning behavior was used in combination with the intelligence age of warning behavior, gross motor, and language, it had the highest classification accuracy of 86.71%. CONCLUSIONS Machine learning combined with CNBS-R2016 can effectively distinguish children with ASD from those with GDD. The DQ of warning behavior plays an important role in machine learning, and its combination with other features can improve classification accuracy, providing a basis for the efficient and accurate differential diagnosis of ASD and GDD in clinical practice.
Child ; Humans ; Autism Spectrum Disorder/psychology* ; Diagnosis, Differential ; Machine Learning ; Social Behavior

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.
Autism Spectrum Disorder* ; Autistic Disorder* ; Demography ; Diagnosis ; Diagnosis, Differential ; Humans ; Infant ; Intellectual Disability* ; Language Development ; National Health Programs

Autism spectrum disorder (ASD) is a neurodevelopmental disorders that is characterized by complex behavioral phenotype and deficits in both social and cognitive functions and has been gradually increasing for the past 20 years. However, practically there are some difficulties in diagnosis and treatment due to a limited number of specialist and considerable cost. Emerging technology, especially socially assistive robotics (SAR), has expanded into the evaluation and intervention for children with ASD. SAR refers to a robot that provides assistance to the user in a social interaction setting. SAR becomes a tool that can teach or demonstrate socially desirable behaviors to help children who have trouble expressing themselves to others owing to their underdeveloped communication and social skills as a result of ASD. This paper reviews the use of SAR to assist in the therapy of children with ASD and the extent to which the robots were successful in helping the children in their social, emotional and communication deficits was investigated. The study investigates the different roles that these robots were observed to play with children with ASD by categorizing and the outcome of studies that have been conducted in Korea. Despite the fact that SAR research is still in its formative stages, if rigorous research plans are developed based on clinical usefulness and effectiveness, and if a clinician with specialized knowledge of ASD participates in or evaluates the results of the research, there is the possibility to create a new paradigm for the treatment of ASD.
Autistic Disorder* ; Child ; Autism Spectrum Disorder* ; Diagnosis ; Humans ; Interpersonal Relations ; Korea ; Mental Health Services ; Phenotype ; Robotics* ; Specialization

OBJECTIVES: Recent studies in autistic brain samples have shown diminished acetylcholine and nicotinic receptor activity. We hypothesized that acetylcholinergic enhancement may pharmacologically improve some autistic characteristics. Donepezil hydrochloride, a cholinesterase inhibitor, was studied in few studies which showed improvement in the expressive and receptive speech of autistic children. We therefore undertook an open label trial to evaluate this effect on speech function in Korean autistic children. METHODS: Twenty-one patients (18 males, 3 females, average age 77.9+/-23.7 months), with a diagnosis of autism spectrum disorder enrolled in a 12-week open label trial of donepezil hydrochloride. Changes were evaluated by PLS (Preschool language scale). Testing was administered at baseline and at 12-week follow-up. RESULTS: Test administered at baseline and at 12-week follow-up showed gains in both expressive and receptive speech functions. CONCLUSION: Donepezil hydrochloride, a cholinesterase inhibitor, appears to improve expressive and receptive speech functions of autistic children.
Acetylcholine ; Autistic Disorder* ; Brain ; Autism Spectrum Disorder* ; Child* ; Cholinesterases ; Diagnosis ; Female ; Follow-Up Studies ; Humans ; Male ; Receptors, Nicotinic