Early detection of brain pathologies through deep learning and advanced neuroimaging approaches

Recent advances in the field of medical imaging and computational neuroscience have transformed the landscape of brain pathology detection. The application of deep learning and artificial intelligence (AI) in the analysis of neuroimaging data has shown remarkable progress in identifying subtle biomarkers associated with early onset neurological and neurodegenerative disorders. Traditional diagnostic processes for conditions such as Alzheimer’s disease, Parkinson’s disease, brain tumors, and epilepsy are often time-consuming and reliant on clinical expertise, leading to delayed interventions. While significant studies have demonstrated the potential of AI to achieve high diagnostic accuracy, challenges remain in generalizability, interpretability, and integration of these models into clinical practice. The need for multidimensional frameworks combining diverse neuroimaging modalities such as MRI (sMRI and fMRI), CT, and DTI with robust machine learning (ML) pipelines underscores a major research frontier in computational neurodiagnostics.This Research Topic aims to consolidate interdisciplinary efforts toward early identification of brain abnormalities through the convergence of AI and advanced neuroimaging. The goal is to deepen understanding of how ML architectures—including Support Vector Machines (SVMs), Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs), and Transformers—can be optimized for medical imaging tasks. Furthermore, it seeks to explore data pre-processing strategies, augmentation techniques, and generative modeling approaches that can mitigate dataset imbalance, enhance model robustness, and facilitate real-time analysis. By establishing methodological benchmarks and frameworks for model interpretability, this Research Topic strives to advance the clinical readiness of AI-powered diagnostic solutions.To gather further insights in the domain of AI-enabled neurodiagnostics, we welcome articles addressing, but not limited to, the following themes:o Multimodal neuroimaging integration for early disease detectiono AI-Enabled image restoration and reconstruction methods for neuroscience imagingo Deep learning and transfer learning architectures for neurological disorder diagnosiso Pre-processing pipelines and data augmentation methods for medical imageso Generative approaches (e.g., GANs) for synthetic data creation and model trainingo Lightweight and low-complexity AI models for real-time clinical implementationo Model interpretability and explainability in medical decision-makingo Validation and benchmarking of AI systems on in vivo and clinical datasets Topic editors: Marco Leo, Abdelhamid Helali Source: https://www.frontiersin.org/research-topics/82794/early-detection-of-brain-pathologies-through-deep-learning-and-advanced-neuroimaging-approaches