Advancements in computer-assisted diagnosis of Alzheimer's disease: A comprehensive survey of neuroimaging methods and AI techniques for early detection

Structural and functional remodeling of neural networks in β-amyloid driven hippocampal hyperactivity

Early detection of Alzheimer's disease (AD) is essential for improving the patients outcomes and advancing our understanding of disease, allowing for timely intervention and treatment. However, accurate biomarkers are still lacking. Recent evidence indicates that hippocampal hyperexcitability precedes the diagnosis of AD decades ago, can predict cognitive decline. Thus, could hippocampal hyperactivity be a robust biomarker for early-AD, and what drives hippocampal hyperactivity in early-AD? these critical questions remain to be answered. Increasing clinical and experimental studies suggest that early hippocampal activation is closely associated with longitudinal β-amyloid (Aβ) accumulation, Aβ aggregates, in turn, enhances hippocampal activity. Therefore, in this narrative review, we discuss the role of Aβ-induced altered intrinsic neuronal properties as well as structural and functional remodeling of glutamatergic, GABAergic, cholinergic, noradrenergic, serotonergic circuits in hippocampal hyperactivity. In addition, we analyze the available therapies and trials that can potentially be used clinically to attenuate hippocampal hyperexcitability in AD. Overall, the present review sheds lights on the mechanism behind Aβ-induced hippocampal hyperactivity, and highlights that hippocampal hyperactivity could be a robust biomarker and therapeutic target in prodromal AD.

Neuroimage analysis using artificial intelligence approaches: a systematic review

In the contemporary era, artificial intelligence (AI) has undergone a transformative evolution, exerting a profound influence on neuroimaging data analysis. This development has significantly elevated our comprehension of intricate brain functions. This study investigates the ramifications of employing AI techniques on neuroimaging data, with a specific objective to improve diagnostic capabilities and contribute to the overall progress of the field. A systematic search was conducted in prominent scientific databases, including PubMed, IEEE Xplore, and Scopus, meticulously curating 456 relevant articles on AI-driven neuroimaging analysis spanning from 2013 to 2023. To maintain rigor and credibility, stringent inclusion criteria, quality assessments, and precise data extraction protocols were consistently enforced throughout this review. Following a rigorous selection process, 104 studies were selected for review, focusing on diverse neuroimaging modalities with an emphasis on mental and neurological disorders. Among these, 19.2% addressed mental illness, and 80.7% focused on neurological disorders. It is found that the prevailing clinical tasks are disease classification (58.7%) and lesion segmentation (28.9%), whereas image reconstruction constituted 7.3%, and image regression and prediction tasks represented 9.6%. AI-driven neuroimaging analysis holds tremendous potential, transforming both research and clinical applications. Machine learning and deep learning algorithms outperform traditional methods, reshaping the field significantly.

MRI advances in the imaging diagnosis of tuberculous meningitis: opportunities and innovations

Tuberculous meningitis (TBM) is not only one of the most fatal forms of tuberculosis, but also a major public health concern worldwide, presenting grave clinical challenges due to its nonspecific symptoms and the urgent need for timely intervention. The severity and the rapid progression of TBM underscore the necessity of early and accurate diagnosis to prevent irreversible neurological deficits and reduce mortality rates. Traditional diagnostic methods, reliant primarily on clinical findings and cerebrospinal fluid analysis, often falter in delivering timely and conclusive results. Moreover, such methods struggle to distinguish TBM from other forms of neuroinfections, making it critical to seek advanced diagnostic solutions. Against this backdrop, magnetic resonance imaging (MRI) has emerged as an indispensable modality in diagnostics, owing to its unique advantages. This review provides an overview of the advancements in MRI technology, specifically emphasizing its crucial applications in the early detection and identification of complex pathological changes in TBM. The integration of artificial intelligence (AI) has further enhanced the transformative impact of MRI on TBM diagnostic imaging. When these cutting-edge technologies synergize with deep learning algorithms, they substantially improve diagnostic precision and efficiency. Currently, the field of TBM imaging diagnosis is undergoing a phase of technological amalgamation. The melding of MRI and AI technologies unquestionably signals new opportunities in this specialized area.