دانلود کتاب پیشرفت‌های هوش مصنوعی در تصویربرداری عصبی

Neuroimaging [1–3] is a rapidly evolving field that involves the use of non-invasive imaging techniques to visualize and study the structure and function of the human brain. This field has experienced transformative progress—as well as significant breakthroughs in terms of the accuracy, speed, and efficiency of identifying various brain disorders—over the past decade, largely driven by technological advancements and computational innovations. Among these, artificial intelligence (AI) has emerged as a pivotal tool, offering researchers and clinicians novel approaches to explore the brain’s structure and function [4–10]. AI models have been widely applied in the analysis and interpretation of neuroimaging data, aiding researchers and clinicians in diagnosing, treating, and monitoring patients with neurological and psychiatric disorders. This Special Issue, titled “Advances of AI in Neuroimaging”, was conceived to provide a platform for cutting-edge research at the intersection of AI and neuroimaging, aiming to revolutionize neuroscience and healthcare. The primary motivation behind this Special Issue was the increasing demand for innovative solutions to address the complexity of neuroimaging data, especially in the context of neurological and psychiatric disorders. AI techniques, such as machine learning (ML) [11,12] and deep learning (DL) [13], offer unparalleled potential for biomarker discovery, disease prediction, and personalized treatment strategies. With the prevalence of brain disorders increasing, the need for accurate and efficient diagnostic tools is more pressing than ever. This Special Issue sought to highlight both technical advancements and their practical implications for patient care and healthcare systems. The contributions span a range of neuroimaging modalities, including magnetic resonance imaging (MRI), positron emission tomography (PET), computed tomography (CT), and electroencephalography (EEG). By addressing challenges such as data complexity, model interpretability, and cost-efficiency, the featured research underscores the indispensable role of AI in advancing neuroimaging and its applications.