Differential diagnosis of autism spectrum disorder and global developmental delay based on machine learning and Children Neuropsychological and Behavioral Scale / 中国当代儿科杂志

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.

Application of the Children Neuropsychological and Behavioral Scale-Revision 2016 in young children with autism spectrum disorder / 中国当代儿科杂志

OBJECTIVE@#To compare the assessment results of the Children Neuropsychological and Behavioral Scale-Revision 2016 (CNBS-R2016) between young children with autism spectrum disorder (ASD) and global developmental delay (GDD, without ASD) and to explore whether CNBS-R2016 could be helpful to early identification of ASD.@*METHODS@#A total of 260 ASD and 371 GDD children aged 18-30 months were enrolled to finish the assessment of CNBS-R2016. The development quotients (DQs) of the five domains of CNBS-R2016 including gross motor, fine motor, adaptability, language and social behavior were compared between the two groups. The receiver operating characteristic (ROC) curve was used to evaluate the value of the autism-predicted domain in identifying ASD and GDD.@*RESULTS@#The DQs of all the five domains in the ASD group were lower than those in the GDD group (P<0.05). The language DQ and total DQ of the ASD group had a negative correlation with the score of the autism-predicted domain (r=-0.566, -0.552 respectively, P<0.01). When the cut-off value of the autism-predicted domain was 10.5, the largest area under the ROC curve was 0.835, and the sensitivity and specificity for the diagnosis of ASD were 0.750 and 0.798 respectively.@*CONCLUSIONS@#The development of ASD children aged 18-30 months is worse than that of GDD children. CNBS-R2016 may be helpful to distinguish ASD from children with developmental delay.