Objective 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.
Key words
Autism spectrum disorder /
Global developmental delay /
Machine learning /
Children Neuropsychological and Behavioral Scale-Revision 2016 /
Child
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
References
1 American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders[M]. 5th ed. Arlington, VA: American Psychiatric Association, 2013: 591-643.
2 张雅如, 邵智. 孤独症儿童语言障碍及其认知神经机制[J]. 中国妇幼保健, 2014, 29(4): 647-650.
3 Bhat AN, Landa RJ, Galloway JC. Current perspectives on motor functioning in infants, children, and adults with autism spectrum disorders[J]. Phys Ther, 2011, 91(7): 1116-1129. PMID: 21546566. DOI: 10.2522/ptj.20100294.
4 郭翠华, 静进. 婴幼儿孤独症谱系障碍的早期筛查工具（综述）[J]. 中国心理卫生杂志, 2017, 31(9): 704-709. DOI: 10.3969/j.issn.1000-6729.2017.09.007.
5 李瑞莉, 金春华, 张丽丽, 等. 《中国儿童发育量表》（4～6岁部分）信度与效度研究[J]. 中国儿童保健杂志, 2015, 23(9): 934-936. DOI: DOI: 10.11852/zgetbjzz2015-23-09-12.
6 张丽丽, 金春华, 李瑞莉, 等. 《中国儿童发育量表》北京地区常模（0~4岁部分）修订与信度分析[J]. 中国儿童保健杂志, 2015, 23(6): 573-576. DOI: 10.11852/zgetbjzz2015-23-06-05.
7 金春华. 儿童神经心理行为检查量表（2016版）[M]. 北京: 北京出版社, 2016.
8 罗美芳, 肖博, 赵晓丽, 等. 儿童神经心理行为检查量表2016版在孤独症谱系障碍幼儿中的临床应用[J]. 中国当代儿科杂志, 2020, 22(5): 494-498. PMID: 32434647. PMCID: PMC7389401. DOI: 10.7499/j.issn.1008-8830.2001035.
9 Ibrahim S, Djemal R, Alsuwailem A. Electroencephalography (EEG) signal processing for epilepsy and autism spectrum disorder diagnosis[J]. Biocybern Biomed Eng, 2018, 38(1): 16-26. DOI: 10.1016/j.bbe.2017.08.006.
10 Liu W, Li M, Yi L. Identifying children with autism spectrum disorder based on their face processing abnormality: a machine learning framework[J]. Autism Res, 2016, 9(8): 888-898. PMID: 27037971. DOI: 10.1002/aur.1615.
11 Duda M, Kosmicki JA, Wall DP. Testing the accuracy of an observation-based classifier for rapid detection of autism risk[J]. Transl Psychiatry, 2014, 4(8): e424. PMID: 25116834. PMCID: PMC4150240. DOI: 10.1038/tp.2014.65.
12 Wall DP, Kosmicki J, Deluca TF, et al. Use of machine learning to shorten observation-based screening and diagnosis of autism[J]. Transl Psychiatry, 2012, 2(4): e100. PMID: 22832900. PMCID: PMC3337074. DOI: 10.1038/tp.2012.10.
13 中华医学会儿科学分会神经学组, 中国医师协会神经内科分会儿童神经疾病专业委员会. 儿童智力障碍或全面发育迟缓病因诊断策略专家共识[J]. 中华儿科杂志, 2018, 56(11): 806-810. PMID: 30392203. DOI: 10.3760/cma.j.issn.0578-1310.2018.11.003.
14 辛志宇, 林莉. 家庭环境因素对儿童语言发育影响的调查研究[J]. 中国医药科学, 2021, 11(18): 168-170. DOI: 10.3969/j.issn.2095-0616.2021.18.043.
15 Chen S, Zhao J, Hu X, et al. Children Neuropsychological and Behavioral Scale-Revision 2016 in the early detection of autism spectrum disorder[J]. Front Psychiatry, 2022, 13: 893226. PMID: 35935438. PMCID: PMC9354041. DOI: 10.3389/fpsyt.2022.893226.
16 田戈. 儿童孤独症发病因素分析[J]. 临床医药文献电子杂志, 2015, 2(10): 1879-1880. DOI: 10.16281/j.cnki.jocml.2015.10.081.
17 Crippa A, Salvatore C, Perego P, et al. Use of machine learning to identify children with autism and their motor abnormalities[J]. J Autism Dev Disord, 2015, 45(7): 2146-2156. PMID: 25652603. DOI: 10.1007/s10803-015-2379-8.
18 Li B, Sharma A, Meng J, et al. Applying machine learning to identify autistic adults using imitation: an exploratory study[J]. PLoS One, 2017, 12(8): e0182652. PMID: 28813454. PMCID: PMC5558936. DOI: 10.1371/journal.pone.0182652.
PDF(648 KB)
