Applying Neural Networks on Biometric Datasets for Screening Speech and Language Deficiencies in Child Communication

Abstract

Screening and evaluation of developmental disorders include complex and challenging procedures, exhibit uncertainties in the diagnostic fit, and require high clinical expertise. Although typically, clinicians’ evaluations rely on diagnostic instrumentation, child observations, and parents’ reports, these may occasionally result in subjective evaluation outcomes. Current advances in artificial intelligence offer new opportunities for decision making, classification, and clinical assessment. This study explores the performance of different neural network optimizers in biometric datasets for screening typically and non-typically developed children for speech and language communication deficiencies. The primary motivation was to give clinicians a robust tool to help them identify speech disorders automatically using artificial intelligence methodologies. For this reason, in this study, we use a new dataset from an innovative, recently developed serious game collecting various data on children’s speech and language responses. Specifically, we employed different neural network approaches such as Artificial Neural Networks (ANNs), K-Nearest Neighbor (KNN), Support Vector Machines (SVM), along with state-of-the-art Optimizers, namely the Adam, the Broyden–Fletcher–Goldfarb–Shanno (BFGS), Genetic algorithm (GAs), and Particle Swarm Optimization algorithm (PSO). The results were promising, while Integer-bounded Neural Network proved to be the best competitor, opening new inquiries for future work towards automated classification supporting clinicians’ decisions on neurodevelopmental disorders.

Machine Learning for Predicting Neurodevelopmental Disorders in Children

“…The game dataset included variables that were child responses quantified from two sources: hand movements on the touch screen (such as solving puzzles, manipulating items on the touchscreen, or identifying images and forms) and verbal responses to questions or executing commands (such as recalling names/events, recognizing emotions, or answering with vocal replies). It is important to note that this study only focuses on the dataset gathered from the SG activities, excluding biometric measurements [19,43]. The children participated in a range of activities that were presented in an engaging and visually appealing manner.…”

“…Particularly, more than one-third of individuals with ASD exhibit symptoms that match criteria for various disorders, resulting in numerous possible diagnostic combinations. Further, traditional diagnostic methods often rely on subjective observations and lengthy assessments, leading to delayed or inaccurate diagnoses [3,[19][20][21]. Early detection is crucial as the developing brain is adaptable, allowing for the creation of compensation mechanisms.…”

“…Consequently, there is a growing need for automated diagnostic tools to help experts accurately and efficiently identify NDs in children. SmartSpeech (Ioannina, Greece) is an innovative system that uses a serious game and a machine learning model to assess a child's developmental profile [19,43]. The aim of this study is to improve the model's ability to capture complex patterns in the game dataset that are difficult for humans to comprehend, enhancing its accuracy.…”

“…More studies reveal the ML efficiency in SLD [10,21,39,40], and CD [41]. A common observation in real-world scenarios is that multiple disorders can be further explored in research, examining their co-occurrence within a person [6,15,19,25,42,43].…”

Utilizing Constructed Neural Networks for Autism Screening

“…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].…”