Application and research progress of machine learning in the diagnosis and treatment of neurodevelopmental disorders in children

Abstract

The prevalence of neurodevelopment disorders (NDDs) among children has been on the rise. This has affected the health and social life of children. This condition has also imposed a huge economic burden on families and health care systems. Currently, it is difficult to perform early diagnosis of NDDs, which results in delayed intervention. For this reason, patients with NDDs have a prognosis. In recent years, machine learning (ML) technology, which integrates artificial intelligence technology and medicine, has been applied in the early detection and prediction of diseases based on data mining. This paper reviews the progress made in the application of ML in the diagnosis and treatment of NDDs in children based on supervised and unsupervised learning tools. The data reviewed here provide new perspectives on early diagnosis and treatment of NDDs.

A social robot connected with chatGPT to improve cognitive functioning in ASD subjects

“…The application of machine learning (ML) in the diagnosis and treatment of neurodevelopmental disorders (NDDs) in children is reported in Song et al ( 2022 ). The authors highlight the increasing prevalence of NDDs among children and the challenges associated with early diagnosis and intervention.…”

Innovative Approaches to Detecting Neurodevelopmental Disorders

“…Machine learning techniques, including artificial intelligence algorithms, offer the potential to analyze vast amounts of data and identify patterns that may be indicative of specific neurodevelopmental conditions [8]. For brain imaging, machine learning algorithms can be employed to process imaging data and extract meaningful information about brain activity [74,75].…”

“…Biomarkers, such as genetic markers or specific biochemical indicators, offer valuable insights into the underlying biological processes associated with neurodevelopmental disorders [7,14]. Machine learning techniques, with the ability to analyze complex datasets and identify patterns, contribute to improved diagnostic accuracy and personalized treatment strategies [8,9]. Functional Magnetic Resonance Imaging (fMRI), Electroencephalography (EEG), and Magnetic Resonance Imaging (MRI) provide non-invasive tools to examine brain structure, connectivity, and activity, offering valuable information about neural correlates and abnormalities in individuals with neurodevelopmental disorders [10,11,12].…”

Machine Learning for Predicting Neurodevelopmental Disorders in Children

“…Numerous studies have been conducted on various types of NDs, using a wide range of ML techniques for diverse sorts of data for diagnostic and prediction processes, employing efficient and sophisticated standards to attain accuracy and cost-effectiveness [6,15,25,26]. These ML techniques mainly employ supervised (like regression, support vector machines (SVMs), decision trees, artificial neural networks (ANNs), and Bayesian logic), and unsupervised learning methods (like clustering, association rules, and dimensionality reduction) [27]. Semi-supervised learning and reinforcement learning techniques are used less frequently [27].…”

“…These ML techniques mainly employ supervised (like regression, support vector machines (SVMs), decision trees, artificial neural networks (ANNs), and Bayesian logic), and unsupervised learning methods (like clustering, association rules, and dimensionality reduction) [27]. Semi-supervised learning and reinforcement learning techniques are used less frequently [27].…”