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

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

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

OBJECTIVE: To assess the value of dry blood spot tandem mass spectrometry for the diagnosis of autism spectrum disorder (ASD). METHODS: Peripheral blood samples of 277 autistic children were collected. Their amino acid and carnitine profiles were detected by liquid chromatography tandem mass spectrometry. Urine samples of suspected patients were collected for verification by gas chromatography mass spectrometry. Blood samples were also taken for genetic testing. RESULTS: Of the 277 children with ASD, 19 (6.9%) were suspected to be with inborn error of metabolism (IEM), which included 6 cases with amino acidemia, 9 with organic acidemia and 4 with fatty acidemia. Three cases of phenylketonuria, one case of homocysteinemia, one case of propionemia, one case of methylmalonic acidemia, one case of glutaric acidemia, one case of isovaleric acidemia, one case of argininemia, one case of citrullinemia I and four cases of primary carnitine deficiency were confirmed by genetic testing, which yielded an overall diagnostic rate of 5.1% (14/277). CONCLUSION Our result has provided further evidence for the co-occurrence of ASD and IEM. Tandem mass spectrometry has a great value for the diagnosis and treatment of ASD in childhood.
Amino Acid Metabolism, Inborn Errors ; complications ; diagnosis ; Autism Spectrum Disorder ; complications ; diagnosis ; Child ; Dried Blood Spot Testing ; Gas Chromatography-Mass Spectrometry ; Humans ; Metabolism, Inborn Errors ; complications ; diagnosis ; Tandem Mass Spectrometry

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

OBJECTIVE: To evaluate the clinical usefulness of the Korean Developmental Screening Test (K-DST) via comparison with Korean Ages and Stages Questionnaire (K-ASQ) for the diagnosis of developmental delay in pediatric patients. METHODS: The K-DST and K-ASQ were used to screen pediatric patients who visited the hospital for evaluation and diagnosis of delayed development. Korean Bayley Scales of Infant Development-II (K-BSID-II) or Korean Wechsler Preschool and Primary Scale of Intelligence III (K-WPPSI-III) were used for the standardized assessment. Moreover, the final clinical diagnosis was confirmed by three expert physicians (rehabilitation doctor, psychiatrist, and neurologist). The sensitivity and specificity of each screening tool for the final diagnosis were investigated and correlated with standardized assessments. RESULTS: A total of 145 pediatric consultations were conducted, which included 123 developmental disorders (40 autism spectrum disorders, 46 global developmental delay/intellectual disability, and 37 developmental language disorders) and another 22 that were not associated with any such disorders. The sensitivity and specificity of K-DST based on the final clinical diagnosis were 82.9% and 90.9%, respectively, which were not significantly different from that of K-ASQ (83.7% and 77.3%). Both K-DST and K-ASQ showed good correlation with K-BSID-II and K-WPPSI-III. No significant difference was found between the K-DST and K-ASQ measures. CONCLUSION: K-DST is an excellent screening tool and is expected to replace K-ASQ with high validity.
Autism Spectrum Disorder ; Communication Disorders ; Developmental Disabilities ; Diagnosis ; Humans ; Infant ; Intellectual Disability ; Intelligence ; Mass Screening ; Motor Skills Disorders ; Psychiatry ; Referral and Consultation ; Sensitivity and Specificity ; Weights and Measures

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.
Autism Spectrum Disorder ; Autistic Disorder ; Checklist ; Child ; Child Behavior ; Child ; Diagnosis ; Diagnostic and Statistical Manual of Mental Disorders ; Humans ; Infant ; Odds Ratio ; ROC Curve ; Weights and Measures

OBJECTIVES: The detection of autism spectrum disorder (ASD) is based on behavioral observations. To build a more objective data-driven 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.
Artificial Intelligence ; Autism Spectrum Disorder ; Autistic Disorder ; Behavior Observation Techniques ; Decision Support Systems, Clinical ; Delivery of Health Care ; Diagnosis ; Mass Screening ; Methods ; Sensitivity and Specificity

Autism spectrum disorder can be differentiated into three subtypes (aloof, passive, and active-but-odd) based on social behaviors according to the Wing Subgroups Questionnaire (WSQ). However, the correlations between the scores on some individual items and the total score are poor. In the present study, we translated the WSQ into Chinese, modified it, validated it in autistic and typically-developing Chinese children, and renamed it the Beijing Autism Subtyping Questionnaire (BASQ). Our results demonstrated that the BASQ had improved validity and reliability, and differentiated autistic children into these three subtypes more precisely. We noted that the autistic symptoms tended to be severe in the aloof, moderate in the passive, and mild in the active-but-odd subtypes. The modified questionnaire may facilitate etiological studies and the selection of therapeutic regimes.
Autism Spectrum Disorder ; diagnosis ; Child, Preschool ; Factor Analysis, Statistical ; Female ; Humans ; Male ; Reproducibility of Results ; Social Behavior ; Surveys and Questionnaires ; Translating