Nnovative Applications With Artificial Intelligence Methods In Neuroimaging Data Analysis

Innovative applications with artificial intelligence methods in neuroimaging data analysis

Technology and Innovation in Learning, Teaching and Education

The three-volume set CCIS 2479-2481 constitutes the proceedings of the 4th International Conference on Technology and Innovation in Learning, Teaching and Education, TECH-EDU 2024, held in Abu Dhabi, United Arab Emirates, during November 13–15, 2024. The 79 full papers presented in this volume were carefully reviewed and selected from 167 submissions. The papers are organized in the following topical sections: Part I: Artificial Intelligence in Education; Emerging Technologies and Learning Environments. Part II: Open Education, Digital Resources and Online Assessment; Pedagogical and Curricular Innovation. Part III: Technology Integration and Educational Policy.

AI Innovations in Neuroimaging: Transforming Brain Analysis

Neuroscience is undergoing a profound transformation aided by developments in artificial intelligence (AI), particularly in how brain imaging techniques are applied and interpreted. AI algorithms have revamped traditional brain imaging modalities such as MRI and CT scans, enhancing image reconstruction capabilities and enabling quicker, more accurate comparisons of complex data. Furthermore, advances in deep learning now permit the extraction of intricate patterns from large volumes of imaging data, offering new insights into brain anomalies, functioning, and connectivity. This Research Topic aims to explore and document the latest AI innovations in neuroscience imaging that are setting new benchmarks for analysing and understanding the brain. The focus is primarily on how these technologies can improve diagnostics, expand our understanding of neurological disorders, and offer advanced solutions for patient care. To gain further insights in this rapidly evolving field, we welcome articles addressing, but not limited to, the following themes: • Applications of AI in enhancing MRI, CT, and other traditional brain imaging techniques. • Deep learning approaches for pattern recognition in neuroimaging data. • Challenges and solutions in integrating AI with large-scale neuroimaging datasets. • Case studies on the use of AI in detecting and monitoring neurological disorders. • Future directions and potential innovations in AI-driven neuroscience imaging. • These submissions may cover original research, review articles, case studies, and methodological advances in the field.