Multimodal Neuroimaging for Early Alzheimer's detection: A Deep Learning Approach

The timely identification and treatment of Alzheimer's detection (AD) is of paramount importance. The present study introduces a deep learning methodology for the timely detection of Alzheimer's detection through the utilization of multimodal neuroimaging data. The authors integrated sMRI, fMRI, and DTI modalities to generate a comprehensive set of features. Subsequently, a pioneering deep learning architecture was developed to consolidate and evaluate these characteristics with the aim of Alzheimer's detection categorization. The dataset utilized in this study comprised of 1200 participants, with 600 individuals diagnosed with early-stage AD and 600 healthy controls matched for age. The application of data augmentation techniques was employed to mitigate class imbalance and enhance the resilience of our model. We used a three-tiered deep learning architecture, which included a sMRI CNN, an fMRI RNN, and a DTI GCN to analyze MRI data. The final categorization was performed by fusing several previously separate networks into a single multimodal neural network. Our study involved a comparative analysis of the efficacy of our model vis-a-vis conventional machine learning algorithms and pre-existing deep learning techniques. Classification accuracy of 93.5%, sensitivity of 92.3%, and specificity of 94.6% were all attained by our suggested method, which represents state-of-the-art performance. Additionally, the model exhibited enhanced generalizability upon evaluation with an external dataset, suggesting its viability for employment in clinical settings. The utilization of multimodal neuroimaging data through our deep learning approach presents a promising avenue for the timely detection of Alzheimer's detection. The results of our study indicate that the amalgamation of sMRI, fMRI, and DTI information can notably augment the precision of diagnosis, thereby opening up avenues for individualized therapeutic approaches in individuals with Alzheimer's detection. Subsequent research endeavors will prioritize the validation of our model in more extensive, multi-center cohorts and the examination of its efficacy in forecasting the advancement of detection and the effectiveness of therapeutic interventions.