Xing Zhang1, Feng Qin2, Zelin Chen3, Leyan Gao4, Guoxin Qiu2, Shuo Lu4. 1. College of Foreign Studies, Jinan University, Jinan, China. 2. Department of Neurosurgery, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, China. 3. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China. 4. Department of Chinese Language and Literature, Sun Yat-sen University, Guangzhou, China.
Abstract
BACKGROUND: Developmental language disorders (DLDs) are the most common developmental disorders in children. For screening DLDs, speech ability (SA) is one of the most important indicators. METHODS: In this paper, we propose a solution for the fast screening of children's DLDs based on a comprehensive SA evaluation and a deep framework of machine learning. Fast screening is crucial for promoting the prevalence and practicality of DLD screening which in turn is important for the treatment of DLDs and related social and behavioral abnormalities (e.g., dyslexia and autism). Our solution is focused on addressing the drawbacks existing in the previous DLD screening methods which include test failure due to text-based inducing material design and illiteracy of most young children, incomplete language evaluation indicators, and professional-reliant evaluation procedures. First, to avoid test failure, a novel comprehensive inducing procedure (CIP) with non-text (i.e., audio-visual) stimulus materials was designed that could cover a large range of modalities to adequately explore the comprehensive SA of the subjects. Second, to address incomplete language evaluation, a set of comprehensive evaluation indicators with full consideration of the characteristics of the children's language acquisition is proposed; furthermore, to break the professional-reliant limitation, we specifically designed a deep framework for fast and accurate screening. RESULTS: Experimental results showed that the proposed deep framework is effective and professional with a 92.6% accuracy on DLD screening. Additionally, to provide a benchmark for the novel problem, we provide a CIP dataset with about 2,200 responses from over 200 children, which may also be useful for further DLD studies and insightful for the fast screening design of other behavioral abnormalities. CONCLUSIONS: Fast screening of children's DLDs can be achieved at accuracy up to 92.6% by our proposed deep learning framework. For successful fast screening, an elaborated CIP with corresponding comprehensive evaluating indicators is necessary to be designed for children suspected to have DLDs. 2020 Annals of Translational Medicine. All rights reserved.
BACKGROUND: Developmental language disorders (DLDs) are the most common developmental disorders in children. For screening DLDs, speech ability (SA) is one of the most important indicators. METHODS: In this paper, we propose a solution for the fast screening of children's DLDs based on a comprehensive SA evaluation and a deep framework of machine learning. Fast screening is crucial for promoting the prevalence and practicality of DLD screening which in turn is important for the treatment of DLDs and related social and behavioral abnormalities (e.g., dyslexia and autism). Our solution is focused on addressing the drawbacks existing in the previous DLD screening methods which include test failure due to text-based inducing material design and illiteracy of most young children, incomplete language evaluation indicators, and professional-reliant evaluation procedures. First, to avoid test failure, a novel comprehensive inducing procedure (CIP) with non-text (i.e., audio-visual) stimulus materials was designed that could cover a large range of modalities to adequately explore the comprehensive SA of the subjects. Second, to address incomplete language evaluation, a set of comprehensive evaluation indicators with full consideration of the characteristics of the children's language acquisition is proposed; furthermore, to break the professional-reliant limitation, we specifically designed a deep framework for fast and accurate screening. RESULTS: Experimental results showed that the proposed deep framework is effective and professional with a 92.6% accuracy on DLD screening. Additionally, to provide a benchmark for the novel problem, we provide a CIP dataset with about 2,200 responses from over 200 children, which may also be useful for further DLD studies and insightful for the fast screening design of other behavioral abnormalities. CONCLUSIONS: Fast screening of children's DLDs can be achieved at accuracy up to 92.6% by our proposed deep learning framework. For successful fast screening, an elaborated CIP with corresponding comprehensive evaluating indicators is necessary to be designed for children suspected to have DLDs. 2020 Annals of Translational Medicine. All rights reserved.
Entities:
Keywords:
Developmental language disorders (DLDs); developmental language disorder indicators (DLD indicators); fast screening