Accelerating Autism Spectrum Disorder Care: A Rapid Review of Data Science Applications in Diagnosis and Intervention

Asian Journal of Psychiatry, Journal Year: 2025, Volume and Issue: unknown, P. 104498 - 104498

Published: April 1, 2025

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

Language: Английский

---

Emerging biophysical techniques for probing synaptic transmission in neurodegenerative disorders

Neuroscience, Journal Year: 2024, Volume and Issue: 565, P. 63 - 79

Published: Nov. 26, 2024

Language: Английский

---

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

Frontiers in Neuroscience, Journal Year: 2025, Volume and Issue: 18

Published: Jan. 15, 2025

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 known potential benefits drawbacks of employing modern technologies diagnosing treating developmental disorders, exemplified by autism spectrum disorder (ASD). ASD's complex nature is ideal illustrating advantages disadvantages digital world. While VEs' possibilities remain under-explored, they offer enhanced diagnostics treatment options ASD, augmenting traditional approaches. Unlike real-world obstacles primarily rooted in social challenges overwhelming environments, these provide unique compensatory opportunities ASD-related deficits. From our perspective addition to other recent work, should be adapted suit specific needs individuals with ASD.

Language: Английский

---

Persistence of Autism Spectrum Disorder:insights from a longitudinal follow-up study in China

Asian Journal of Psychiatry, Journal Year: 2025, Volume and Issue: 105, P. 104385 - 104385

Published: Feb. 4, 2025

Language: Английский

---

Harnessing Artificial Intelligence in Early Detection and Diagnosis of Alzheimer's Disease: Current and Future Applications

Indus journal of bioscience research., Journal Year: 2025, Volume and Issue: 3(2), P. 199 - 212

Published: Feb. 25, 2025

Alzheimer's Disease (AD) is a neurodegenerative disorder requiring early detection. This study compares AI models—Convolutional Neural Networks (CNN), Support Vector Machines (SVM), and Random Forest (RF)—in analyzing neuroimaging data (MRI, PET) to enhance AD prediction improve diagnosis using machine learning techniques. Through the application of multi-modal in form genetic, clinical, data, also investigates effectiveness combining different types predictability models for diagnosis. Feature importance analysis was performed methods like SHAP (SHAP (Shapley Additive Explanations) values determine most important variables model predictions, e.g., certain brain regions or genetic components. The generalizability real-world applicability by training on an independent dataset representing diverse clinical settings. performance each assessed variety statistical measures accuracy, precision, recall, F1-score, Area Under Curve (AUC). findings showed that CNN better compared SVM RF all metrics with highest accuracy (92%), precision (93%), recall (91%), AUC (0.95). suggest effectively detects subtle patterns, making it strong tool While well, superior accuracy. Cross-validation confirmed its generalizability, crucial use. Implementing models, especially CNN, may enable earlier detection, timely interventions, improved patient outcomes Alzheimer’s care. References

Language: Английский

---

Advances in Autism Spectrum Disorder (ASD) Diagnostics: From Theoretical Frameworks to AI-Driven Innovations

Electronics, Journal Year: 2025, Volume and Issue: 14(5), P. 951 - 951

Published: Feb. 27, 2025

This study provides a comprehensive analysis of the evolution Autism Spectrum Disorder (ASD) diagnostics, tracing its progression from psychoanalytic origins to integration advanced artificial intelligence (AI) technologies. The explores, through scientific data bases like Pub Med, Scopus, and Google Scholar, how theoretical frameworks, including psychoanalysis, behavioral psychology, cognitive development, neurobiological paradigms, have shaped diagnostic methodologies over time. Each paradigm’s associated assessment tools, such as Diagnostic Observation Schedule (ADOS) Vineland Adaptive Behavior Scales, are discussed in relation their advancements limitations. Emerging technologies, particularly AI, highlighted for transformative impact on ASD diagnostics. application AI areas video analysis, Natural Language Processing (NLP), biodata demonstrates significant progress precision, accessibility, inclusivity. Ethical considerations, algorithmic transparency, security, inclusivity underrepresented populations, critically examined alongside challenges scalability equitable implementation. Additionally, neurodiversity- informed approaches emphasized role reframing autism natural variation human cognition behavior, advocating strength-based, inclusive frameworks. synthesis underscores interplay between evolving models, technological advancements, growing focus compassionate, practices. It concludes by continued innovation, interdisciplinary collaboration, ethical oversight further refine diagnostics improve outcomes individuals across spectrum.

Language: Английский

---

An Empirical Evaluation of Neural Network Architectures for 3D Spheroid Segmentation

Computers, Journal Year: 2025, Volume and Issue: 14(3), P. 86 - 86

Published: Feb. 28, 2025

The accurate segmentation of 3D spheroids is crucial in advancing biomedical research, particularly understanding tumor development and testing therapeutic responses. As emulate vivo conditions more closely than traditional 2D cultures, efficient methods are essential for precise analysis. This study evaluates three prominent neural network architectures—U-Net, HRNet, DeepLabV3+—for the spheroids, a critical challenge image Through empirical analysis across comprehensive Tumour Spheroid dataset, HRNet DeepLabV3+ emerged as top performers, achieving high accuracy, with 99.72% validation Dice coefficient 96.70%, Jaccard 93.62%. U-Net, although widely used medical imaging, struggled to match performance other models. also examines impact optimizers, Adam optimizer frequently causing overfitting, especially U-Net Despite improvements SGD Adagrad, these optimizers did not surpass DeepLabV3+. highlights importance selecting right model–optimizer combination optimal segmentation.

Language: Английский

---

Impact of social knowledge and skills training based on UCLA PEERS® on social communication and interaction skills of adolescents or young adults with autism: a systematic review and meta-analysis

Asian Journal of Psychiatry, Journal Year: 2025, Volume and Issue: 106, P. 104422 - 104422

Published: March 1, 2025

Language: Английский

---

Neuroglial Dysregulation in Autism Spectrum Disorder: Pathogenetic Insights, Genetic Threads, and Therapeutic Horizons

Neuroglia, Journal Year: 2025, Volume and Issue: 6(1), P. 11 - 11

Published: March 1, 2025

Background/Objectives: Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition marked by challenges in social communication, restricted interests, and repetitive behaviors. Recent studies highlight the crucial roles of neuroglial cells—astrocytes, microglia, oligodendrocytes—in synaptic function, neural connectivity, neuroinflammation. These findings offer fresh perspective on ASD pathophysiology. This review synthesizes current knowledge dysfunction ASD, emphasizing its role pathophysiological mechanisms, genetic influences, potential therapeutic strategies. Methods: We conducted comprehensive literature review, integrating insights from neuroscience, molecular biology, clinical studies. Special focus was given to glial-mediated neuroinflammatory plasticity regulation, impact mutations signaling homeostasis. Results: Neuroglial evident abnormal pruning impaired astrocytic glutamate defective oligodendrocyte-driven myelination, which collectively disrupt neuronal architecture. Emerging therapies targeting these pathways, including anti-inflammatory drugs, microglial modulators, cell-based approaches, show promise alleviating key symptoms. Additionally, advanced interventions such as gene editing glial progenitor therapy present opportunities correct underlying dysfunction. Conclusions: establishes framework for understanding contributions ASD. By diverse disciplines, it enhances our pathophysiology paves way novel strategies pathways.

Language: Английский

---

Initial study of verbal and nonverbal communication training through the collaborative operation of a humanoid robot for individuals with Autism Spectrum Disorder

Asian Journal of Psychiatry, Journal Year: 2025, Volume and Issue: 106, P. 104423 - 104423

Published: March 4, 2025

Language: Английский

---