Guest Editors
Dr. Roberta Simeoli
Email: roberta.simeoli@unina.it
Affiliation: Department of Humanistic Studies, University of Naples "Federico II", Naples, 80133, Italy
Homepage:
Research Interests: autism; movement analysis; artificial intelligence; embodied cognition; neuropsychology
Dr. Monica Casella
Email: monica.casella@unina.it
Affiliation: Department of Humanistic Studies, University of Naples "Federico II", Naples, 80133, Italy
Homepage:
Research Interests: machine learning; psychometrics; biosignal analysis; measure development
Dr. Maria Luongo
Email: maria.luongo@unina.it
Affiliation: Department of Humanistic Studies, University of Naples "Federico II", Naples, 80133, Italy
Homepage:
Research Interests: autism; movement analysis; large language models; agent-based models
Prof. Davide Marocco
Email: davide.marocco@unina.it
Affiliation: Natural and Artificial Cognition Laboratory, University of Naples Federico II, Naples, 80133, Italy
Homepage:
Research Interests: artificial intelligence in psychology; psychometric analysis; large language models; multimodal AI-based assessment
Summary
The diagnosis of neurodevelopmental and neurodegenerative disorders presents significant challenges due to the complexity and heterogeneity of these conditions. Traditional diagnostic approaches rely heavily on behavioral assessments, clinical interviews, and neuropsychological evaluations. While effective, these methods can be time-intensive, subject to observer bias, and prone to diagnostic delays, which may compromise the effectiveness of early interventions and treatment strategies.
In recent years, the integration of machine learning (ML) algorithms and advanced technological screening tools has emerged as a transformative approach to enhance diagnostic accuracy, predictive modeling, and early detection of conditions such as Autism Spectrum Disorder (ASD), Attention-Deficit/Hyperactivity Disorder (ADHD), intellectual disabilities, Alzheimer's disease, Parkinson's disease, and other neurocognitive disorders. ML techniquesincluding artificial neural networks (ANN), support vector machines (SVM), decision trees (DT), and deep learning modelshave demonstrated great potential in analyzing large-scale behavioral, neuroimaging, genetic, and biomarker datasets to identify patterns that may be imperceptible to traditional diagnostic methods.
At the same time, novel biomarker discoveries, wearable technologies, and multimodal data integration are further revolutionizing the field, offering unprecedented opportunities to reduce diagnostic uncertainty, enable personalized treatment approaches, and enhance patient outcomes. These advancements not only improve our understanding of disease mechanisms but also pave the way for precision medicine approaches that could transform clinical practice.
This Special Issue aims to bring together leading researchers, clinicians, and data scientists from around the world who are working at the intersection of artificial intelligence, neuroscience, and medical diagnostics. By fostering an interdisciplinary exchange of knowledge, we seek to advance the scientific landscape, encourage collaboration, and accelerate progress in the development of innovative, technology-driven solutions for both neurodevelopmental and neurodegenerative disorders.
We invite contributions from experts in machine learning, technology, psychology, neurology, biomedical engineering, and computational medicine to share their latest findings, methodologies, and perspectives.
Join us in shaping the future of diagnostics and ensuring that cutting-edge technologies translate into meaningful clinical impact.
We invite original research articles, reviews, case studies, and perspectives that address, but are not limited to, the following topics:
· Machine Learning Applications in neuropsychological Diagnosis
· Technological Innovations in neuropsychological Screening
· Behavioral and Biomarker-Based Approaches
· Early Detection and Preventive Interventions
· Challenges and Ethical Considerations
Keywords
machine learning in neurology, early diagnosis of neurodevelopmental disorders, AI for neurodegenerative diseases, biomarkers and computational screening, technology-driven precision medicine
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