Screening Children's Intellectual Disabilities with Phonetic Features, Facial Phenotype and Craniofacial Variability Index

Intellectual Disability (ID) is a kind of developmental deficiency syndrome caused by congenital diseases or postnatal events. This syndrome could be intervened as soon as possible if its early screening was efficient, which may improve the condition of patients and enhance their self-care ability....

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Bibliographic Details
Published in:Brain sciences 2023-01, Vol.13 (1), p.155
Main Authors: Chen, Yuhe, Ma, Simeng, Yang, Xiaoyu, Liu, Dujuan, Yang, Jun
Format: Article
Language:English
Subjects:
Algorithms
Alzheimer's disease
Artificial intelligence
Children
Cognitive ability
Congenital anomalies
craniofacial variability index
Datasets
facial features
Fetal alcohol syndrome
Genotype & phenotype
Intellectual disabilities
intellectual disability
Intelligence tests
Language disorders
Machine learning
Medical diagnosis
Medical personnel
Patients
People with disabilities
Phenotypes
Phonetic features
Phonetics
Psychiatrists
Rehabilitation
Speech
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Summary:Intellectual Disability (ID) is a kind of developmental deficiency syndrome caused by congenital diseases or postnatal events. This syndrome could be intervened as soon as possible if its early screening was efficient, which may improve the condition of patients and enhance their self-care ability. The early screening of ID is always achieved by clinical interview, which needs in-depth participation of medical professionals and related medical resources. A new method for screening ID has been proposed by analyzing the facial phenotype and phonetic characteristic of young subjects. First, the geometric features of subjects' faces and phonetic features of subjects' voice are extracted from interview videos, then craniofacial variability index (CVI) is calculated with the geometric features and the risk of ID is given with the measure of CVI. Furthermore, machine learning algorithms are utilized to establish a method for further screening ID based on facial features and phonetic features. The proposed method using three feature sets, including geometric features, CVI features and phonetic features was evaluated. The best performance of accuracy was closer to 80%. The results using the three feature sets revealed that the proposed method may be applied in a clinical setting in the future after continuous improvement.
ISSN:2076-3425
2076-3425
DOI:10.3390/brainsci13010155