A Game-Based Smart System Identifying Developmental Speech and Language Disorders in Child Communication: A Protocol Towards Digital Clinical Diagnostic Procedures

Toki, Eugenia I.; Zakopoulou, Victoria; Tatsis, Giorgos; Plachouras, Konstantinos; Siafaka, Vassiliki; Kosma, Evangelia I.; Chronopoulos, Spyridon K.; Filippidis, Despina Elisabeth; Nikopoulos, Georgios; Pange, Jenny; Manos, Anastasios

doi:10.1007/978-3-030-96296-8_50

Cited by 9 publications

(3 citation statements)

References 30 publications

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“…The study gathered responses from children who participated in playing the Serious Game on a mobile device. The game involved different activities related to screening and assessing individuals for ASD [87]. During the gameplay, the children complete several activities related to their developmental skills.…”

Section: Methodsmentioning

confidence: 99%

Utilizing Constructed Neural Networks for Autism Screening

Toki,

Pange,

Tatsis

et al. 2024

Applied Sciences

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Autism Spectrum Disorder is known to cause difficulties in social interaction and communication, as well as repetitive patterns of behavior, interests, or hobbies. These challenges can significantly affect the individual’s daily life. Therefore, it is crucial to identify and assess children with Autism Spectrum Disorder early to significantly benefit the long-term health of children. Unfortunately, many children are not diagnosed or are misdiagnosed, which means they miss out on the necessary interventions. Clinicians and other experts face various challenges during the diagnostic process. Digital tools can facilitate early diagnosis effectively. This study aimed to explore the use of machine learning techniques on a dataset collected from a serious game designed for children with autism to investigate how these techniques can assist in classification and make the clinical process more efficient. The responses were gathered from children who participated in interactive games deployed on mobile devices, and the data were analyzed using various types of neural networks, such as multilayer perceptrons and constructed neural networks. The performance metrics of these models, including error rate, precision, and recall, were reported, and the comparative experiments revealed that the constructed neural network using the integer rule-based neural networks approach was superior. Based on the evaluation metrics, this method showed the lowest error rate of 11.77%, a high accuracy of 0.75, and a good recall of 0.66. Thus, it can be an effective way to classify both typically developed children and children with Autism Spectrum Disorder. Additionally, it can be used for automatic screening procedures in an intelligent system. The results indicate that clinicians could use these techniques to enhance conventional screening methods and contribute to providing better care for individuals with autism.

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Section: Methodsmentioning

confidence: 99%

Utilizing Constructed Neural Networks for Autism Screening

Toki,

Pange,

Tatsis

et al. 2024

Applied Sciences

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“…This study is part of the ongoing research project "Smart Computing Models, Sensors, and Early diagnostic speech and language deficiencies indicators in Child Communication" (also known as SmartSpeech), funded by the Region of Epirus in Greece and the European Regional Development Fund (ERDF). Specifically designed activities based on ND assessment procedures were used to create a serious game for the SmartSpeech project [71]. This serious game collects players' responses.…”

Section: The Smartspeech Projectmentioning

confidence: 99%

Employing Classification Techniques on SmartSpeech Biometric Data towards Identification of Neurodevelopmental Disorders

Toki

Tatsis

et al. 2023

Signals

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Early detection and evaluation of children at risk of neurodevelopmental disorders and/or communication deficits is critical. While the current literature indicates a high prevalence of neurodevelopmental disorders, many children remain undiagnosed, resulting in missed opportunities for effective interventions that could have had a greater impact if administered earlier. Clinicians face a variety of complications during neurodevelopmental disorders’ evaluation procedures and must elevate their use of digital tools to aid in early detection efficiently. Artificial intelligence enables novelty in taking decisions, classification, and diagnosis. The current research investigates the efficacy of various machine learning approaches on the biometric SmartSpeech datasets. These datasets come from a new innovative system that includes a serious game which gathers children’s responses to specifically designed speech and language activities and their manifestations, intending to assist during the clinical evaluation of neurodevelopmental disorders. The machine learning approaches were used by utilizing the algorithms Radial Basis Function, Neural Network, Deep Learning Neural Networks, and a variation of Grammatical Evolution (GenClass). The most significant results show improved accuracy (%) when using the eye tracking dataset; more specifically: (i) for the class Disorder with GenClass (92.83%), (ii) for the class Autism Spectrum Disorders with Deep Learning Neural Networks layer 4 (86.33%), (iii) for the class Attention Deficit Hyperactivity Disorder with Deep Learning Neural Networks layer 4 (87.44%), (iv) for the class Intellectual Disability with GenClass (86.93%), (v) for the class Specific Learning Disorder with GenClass (88.88%), and (vi) for the class Communication Disorders with GenClass (88.70%). Overall, the results indicated GenClass to be nearly the top competitor, opening up additional probes for future studies toward automatically classifying and assisting clinical assessments for children with neurodevelopmental disorders.

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“…SmartSpeech is an ongoing project intended to support and enhance screening and early detection procedures of NDs, utilizing smart computing models, sensors, and early diagnostic speech and language deficiency indicators [45]. The ultimate goal of this smart project is to improve children's communication abilities in the context of digital healthcare, thus leading to a positive economic outcome in line with current digital trends.…”

Section: Introductionmentioning

confidence: 99%

Constructing Features for Screening Neurodevelopmental Disorders Using Grammatical Evolution

Toki,

Tatsis,

Pange

et al. 2023

Applied Sciences

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Developmental domains refer to different areas of a child’s growth and maturation, including physical, language, cognitive, and social–emotional skills. Understanding these domains helps parents, caregivers, and professionals track a child’s progress and identify potential areas of concern. Nevertheless, due to the high level of heterogeneity and overlap, neurodevelopmental disorders may go undiagnosed in children for a crucial period. Detecting neurodevelopmental disorders at an early stage is fundamental. Digital tools like artificial intelligence can help clinicians with the early detection process. To achieve this, a new method has been proposed that creates artificial features from the original ones derived from the SmartSpeech project, using a feature construction procedure guided by the Grammatical Evolution technique. The new features from a machine learning model are used to predict neurodevelopmental disorders. Comparative experiments demonstrated that using the feature creation method outperformed other machine learning methods for predicting neurodevelopmental disorders. In many cases, the reduction in the test error reaches up to 65% to the next better one.

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A Game-Based Smart System Identifying Developmental Speech and Language Disorders in Child Communication: A Protocol Towards Digital Clinical Diagnostic Procedures

Cited by 9 publications

References 30 publications

Utilizing Constructed Neural Networks for Autism Screening

Utilizing Constructed Neural Networks for Autism Screening

Employing Classification Techniques on SmartSpeech Biometric Data towards Identification of Neurodevelopmental Disorders

Constructing Features for Screening Neurodevelopmental Disorders Using Grammatical Evolution

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