PUBLICATIONS

Abstract: Artificial Intelligence (AI) has become integral to the research of neurological diseases due to the rapid expansion of neuroimaging, clinical, physiological, and wearable data. However, the concise synthesis of recent machine learning (ML) and deep learning (DL) remains limited. This systematic review analyzes studies published between January 2021 and March 2026 on five major conditions- Alzheimer’s disease, stroke, Parkinson’s disease, brain tumors, and traumatic brain injury (TBI)-following the PRISMA 2020 guidelines and a structured search of PubMed, Scopus, and Web of Science, yielding 206 eligible articles. The results show that convolutional and encoder–decoder architectures dominate imaging tasks, whereas hybrid and multimodal approaches increasingly combine imaging with clinical and sensor data. Emerging paradigms, including federated learning, self-supervised learning, and foundation models, address data scarcity, privacy, and cross-institutional variability. Key advances include high-performing transformer-based models for Alzheimer’s diagnosis, real-time stroke detection by CT/MRI, improved Parkinson’s detection by multimodal fusion, hybrid models for brain tumor classification, and outcome prediction in TBI. Despite these gains, challenges in generalizability, interpretability, and clinical translation persist, underscoring the need for more robust and clinically reliable AI systems to address these issues.

Abstract: This work investigates the second-order partitioned time-stepping method for the sophisticated multiphysics parabolic model problem. In this paper, we consider a coupled system of heat equations through two adjacent materials which are coupled across their shared and rigid interface with two interface conditions. We perform the variational formulation of the heat-heat coupled fluid flow model and report the well-posedness of the model. On the other hand, efficient second-order Crank-Nicolson and second-order implicit-explicit finite element discretized algorithm is proposed to solve the parabolic two domain problems numerically. We conduct several numerical tests to achieve optimal convergence order. We also designed several conceptual model problems to demonstrate the validity, accuracy, and efficiency of the heat-heat multiphysics model problem. The applicability and complicated flow characteristics are shown by illustrating the heat flux, conduction of the heat and contour plots in the conjugate computational domain.