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

Yuhe Chen; Simeng Ma; Xiaoyu Yang; Dujuan Liu; Jun Yang

doi:10.3390/brainsci13010155

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

Brain Sci. 2023 Jan 16;13(1):155. doi: 10.3390/brainsci13010155.

Authors

Yuhe Chen¹, Simeng Ma², Xiaoyu Yang^{3

4}, Dujuan Liu², Jun Yang^{5

6}

Affiliations

¹ School of Foreign Languages, Huazhong University of Science and Technology, Wuhan 430074, China.
² Department of Psychiatry, Renmin Hospital of Wuhan University, Wuhan 430060, China.
³ Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.
⁴ Hubei Province Clinical Research Center for Precision Medicine for Critical Illness, Wuhan 430030, China.
⁵ School of Computer Science & Technology, Huazhong University of Science and Technology, Wuhan 430074, China.
⁶ School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China.

Abstract

Background: 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.

Methods: 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.

Results: 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%.

Conclusions: 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.

Keywords: craniofacial variability index; facial features; intellectual disability; machine learning; phonetic features.

Grants and funding

This work was supported by grants from the National Natural Science Foundation of China (grant number: 62002269) and the National Key R&D Program of China (grant number: 2018YFC1314600).