Digital tools for direct assessment of autism risk during early childhood: A systematic review

Brain network alterations in fragile X syndrome

Fragile X syndrome (FXS), caused by FMR1 gene mutations, leads to widespread brain alterations significantly impacting cognition and behaviour. Recent advances have provided a deeper understanding of the neural substrates of FXS. This review provides a comprehensive overview of the current knowledge of neuronal network alterations in FXS. We highlight imaging studies that demonstrate network-level disruptions within resting-state networks, including the default mode network, frontoparietal network, salience network, and basal ganglia network, linked to cognitive, emotional and motor deficits in FXS. Next, we link dysregulated network activity in FXS to molecular studies showing neurometabolic imbalances, particularly in GABAergic and glutamatergic systems. Additionally, gene-brain-behavior correlations are explored with gene expression maps to illustrate regional FMR1 expression patterns tied to clinical symptoms. A graph analysis and meta-analytic mapping further link these dysfunctional networks to the specific symptoms of FXS. We conclude by highlighting gaps in the literature, including the need for greater global collaboration, inclusion of underrepresented populations, and consideration of transdiagnostic effects in future research to advance neuroimaging and therapeutic approaches for FXS.

Accelerating autism spectrum disorder care: A rapid review of data science applications in diagnosis and intervention

Integrating data science techniques, including machine learning, natural language processing, and big data analytics, has revolutionized the diagnosis and intervention landscape for Autism Spectrum Disorder (ASD). This rapid review examines these approaches' current applications, benefits, limitations, and ethical considerations while identifying key research gaps and future directions. Data-driven methodologies offer significant advantages, such as enhanced diagnostic accuracy, personalized interventions, and increased accessibility, particularly in resource-limited settings. However, challenges like data quality, algorithmic bias, and interpretability hinder widespread implementation. Additionally, ethical concerns regarding privacy, consent, and equity necessitate careful navigation. Despite these advancements, substantial research gaps remain, including the lack of diverse datasets, limited longitudinal studies, and insufficient generalizability across populations. Future studies must prioritize addressing these gaps by fostering collaboration, ensuring ethical transparency, and developing inclusive, scalable solutions to improve patient outcomes. This review underscores the transformative potential of data science in accelerating ASD care while emphasizing the need for continued innovation and responsible application.

Virtual environments as a novel and promising approach in (neuro)diagnosis and (neuro)therapy: a perspective on the example of autism spectrum disorder

Over the last three decades, dynamically evolving research using novel technologies, including virtual environments (VEs), has presented promising solutions for neuroscience and neuropsychology. This article explores the known and potential benefits and drawbacks of employing modern technologies for diagnosing and treating developmental disorders, exemplified by autism spectrum disorder (ASD). ASD's complex nature is ideal for illustrating the advantages and disadvantages of the digital world. While VEs' possibilities remain under-explored, they offer enhanced diagnostics and treatment options for ASD, augmenting traditional approaches. Unlike real-world obstacles primarily rooted in social challenges and overwhelming environments, these novel technologies provide unique compensatory opportunities for ASD-related deficits. From our perspective in addition to other recent work, digital technologies should be adapted to suit the specific needs of individuals with ASD.

Leveraging AI for the diagnosis and treatment of autism spectrum disorder: Current trends and future prospects

The integration of artificial intelligence (AI) into the diagnosis and treatment of autism spectrum disorder (ASD) represents a promising frontier in healthcare. This review explores the current landscape and future prospects of AI technologies in ASD diagnostics and interventions. AI enables early detection and personalized assessment of ASD through the analysis of diverse data sources such as behavioural patterns, neuroimaging, genetics, and electronic health records. Machine learning algorithms exhibit high accuracy in distinguishing ASD from neurotypical development and other developmental disorders, facilitating timely interventions. Furthermore, AI-driven therapeutic interventions, including augmentative communication systems, virtual reality-based training, and robot-assisted therapies, show potential in improving social interactions and communication skills in individuals with ASD. Despite challenges such as data privacy and interpretability, the future of AI in ASD holds promise for refining diagnostic accuracy, deploying telehealth platforms, and tailoring treatment plans. By harnessing AI, clinicians can enhance ASD care delivery, empower patients, and advance our understanding of this complex condition.

Using mobile health technology to assess childhood autism in low-resource community settings in India: An innovation to address the detection gap

LAY ABSTRACT

Autism is diagnosed by highly trained professionals- but most autistic people live in parts of the world that harbour few or no such autism specialists and little autism awareness. So many autistic people go undiagnosed, misdiagnosed, and misunderstood. We designed an app (START) to identify autism and related conditions in such places, in an attempt to address this global gap in access to specialists. START uses computerised games and activities for children and a questionnaire for parents to measure social, sensory, and motor skills. To check whether START can flag undiagnosed children likely to have neurodevelopmental conditions, we tested START with children whose diagnoses already were known: Non-specialist health workers with just a high-school education took START to family homes in poor neighbourhoods of Delhi, India to work with 131 two-to-seven-year-olds. Differences between typically and atypically developing children were highlighted in all three types of skills that START assesses: children with neurodevelopmental conditions preferred looking at geometric patterns rather than social scenes, were fascinated by predictable, repetitive sensory stimuli, and had more trouble with precise hand movements. Parents' responses to surveys further distinguished autistic from non-autistic children. An artificial-intelligence technique combining all these measures demonstrated that START can fairly accurately flag atypically developing children. Health workers and families endorsed START as attractive to most children, understandable to health workers, and adaptable within sometimes chaotic home and family environments. This study provides a proof of principle for START in digital screening of autism and related conditions in community settings.

Extended Reality-Based Mobile App Solutions for the Therapy of Children With Autism Spectrum Disorders: Systematic Literature Review

BACKGROUND

The increasing prevalence of autism spectrum disorder (ASD) has driven research interest on the therapy of individuals with autism, especially children, as early diagnosis and appropriate treatment can lead to improvement in the condition. With the widespread availability of virtual reality, augmented reality (AR), and mixed reality technologies to the public and the increasing popularity of mobile devices, the interest in the use of applications and technologies to provide support for the therapy of children with autism is growing.

OBJECTIVE

This study aims to describe the literature on the potential of virtual reality, AR, and mixed reality technologies in the context of therapy for children with ASD. We propose to investigate and analyze the temporal distribution of relevant papers, identify the target audience for studies related to extended reality apps in ASD therapy, examine the technologies used in the development of these apps, assess the skills targeted for improvement in primary studies, explore the purposes of the proposed solutions, and summarize the results obtained from their application.

METHODS

For the systematic literature review, 6 research questions were defined in the first phase, after which 5 international databases (Web of Science, Scopus, ScienceDirect, IEEE Xplore Digital Library, and ACM Digital Library) were searched using specific search strings. Results were centralized, filtered, and processed applying eligibility criteria and using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The results were refined using a technical and IT-oriented approach. The quality criteria assessed whether the research addressed ASDs, focused on children's therapy, involved targeted technologies, deployed solutions on mobile devices, and produced results relevant to our study.

RESULTS

In the first step, 179 publications were identified in Zotero reference manager software (Corporation for Digital Scholarship). After excluding articles that did not meet the eligibility or quality assessment criteria, 28 publications were finalized. The analysis revealed an increase in publications related to apps for children with autism starting in 2015 and peaking in 2019. Most studies (22/28, 79%) focused on mobile AR solutions for Android devices, which were developed using the Unity 3D platform and the Vuforia engine. Although 68% (19/28) of these apps were tested with children, 32% (9/28) were tested exclusively by developers. More than half (15/28, 54%) of the studies used interviews as an evaluation method, yielding mostly favorable although preliminary results, indicating the need for more extensive testing.

CONCLUSIONS

The findings reported in the studies highlight the fact that these technologies are appropriate for the therapy of children with ASD. Several studies showed a distinct trend toward the use of AR technology as an educational tool for people with ASD. This trend entails multidisciplinary cooperation and an integrated research approach, with an emphasis on comprehensive empirical evaluations and technology ethics.