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

Abstract

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.

A Comprehensive Survey of Brain-Computer Interface Technology in Healthcare: Research Perspectives

“…Neurodevelopmental monitoring entails the methodical observation and evaluation of a child's neurological and cognitive development over time. This process allows healthcare professionals to track developmental milestones, identify potential delays or disorders, and provide early intervention and support when necessary [126,127]. By monitoring neurological development from infancy through childhood, clinicians can detect and address neurodevelopmental concerns early, promoting optimal outcomes for children's cognitive, social, and emotional well-being [128,129].…”

Utilizing Constructed Neural Networks for Autism Screening

“…The identification and diagnosis of ASD involves various methods like brain imaging (PET, SPECT, fNIRS, EEG, fMRI, etc.) [27][28][29][30], diffusion tensor imaging [31], and biometrics to analyze neurological and behavioral aspects [32]. Also, gesture analysis using motion capture, sensory input assessment, and eye tracking are employed [25,[33][34][35].…”

“…ML is increasingly used to process these data for a more effective diagnosis and treatment of ASD [36]. For instance, ML algorithms, in combination with eye-tracking technology, are increasingly showing significance in the early identification and diagnosis of ASD utilizing various stimuli, tasks, datasets, and algorithms [25,32,33,[37][38][39]. Evidence of ML methods combining physiological data (EEG) with behavioral data (eye fixation and facial expression) has been developed to identify children with ASD [40].…”

“…Moreover, it is documented that children with NDs can enjoy better, anxiety-free activities with SGs [42,43]. By integrating ML into SGs, they may become powerful tools for the automated screening and assessment of ASD [32,44]. Studies show that ML in SGs can aid the classification of ASD and help children with early identification, diagnostic assessment, objective metrics, engagement, motivation, and tailored therapy [35,45].…”

App Characteristics and Accuracy Metrics of Available Digital Biomarkers for Autism: Scoping Review

“…Apps also offer the possibility of providing additional ecological information collected in users’ home environments to clinicians during the assessment phase. Specifically, digital biomarkers, that is, digital tools that collect information about the behavioral characteristics and physiological processes of individuals affected by a condition, have shown promise in identifying the presence of a disorder in several diagnostic domains (eg, cognitive impairment and dementia [ 9 ], depression [ 10 ], and learning disabilities [ 11 ]). Recommendations for developers and the scientific community at large outline the importance of transparent communication of the algorithms underlying digital biomarkers as well as plans for iterative evaluations of such products [ 12 ].…”