Leveraging Artificial Intelligence in Neuroimaging for Enhanced Brain Health Diagnosis

Abstract

Neuroimaging plays a pivotal role in diagnosing and monitoring brain health, offering detailed insights into the structural and functional aspects of the brain. However, traditional analysis methods are often limited by their reliance on manual interpretation and inability to manage high-dimensional datasets effectively. Artificial intelligence (AI), with its machine learning (ML) and deep learning (DL) capabilities, is revolutionizing the field by enabling automated, accurate, and rapid analysis of neuroimaging data. This study explores the integration of AI in neuroimaging, focusing on its application in diagnosing neurological disorders such as Alzheimer’s disease, Parkinson’s disease, and stroke. By leveraging advanced algorithms, AI models can detect subtle patterns and anomalies in imaging data that are imperceptible to the human eye, facilitating early diagnosis and personalized treatment planning. Key findings include the enhanced sensitivity and specificity of AI-driven models compared to traditional methods. Convolutional neural networks (CNNs), recurrent neural networks (RNNs), and hybrid architectures have demonstrated exceptional performance in identifying pathological changes, achieving accuracies exceeding 95% in several cases. Furthermore, multimodal approaches combining imaging data with genetic and clinical information offer improved diagnostic precision, enabling comprehensive assessments of brain health. Despite these advancements, challenges remain, including data standardization, ethical considerations, and the need for explainable AI models to ensure clinical adoption. This paper highlights the potential of AI to transform neuroimaging from a diagnostic tool into a predictive and preventive instrument. By addressing current limitations and fostering interdisciplinary collaboration, the integration of AI in neuroimaging holds promise for enhancing diagnostic accuracy, reducing healthcare costs, and improving patient outcomes.