JMIR Publications

ABSTRACT

Background:

Alzheimer’s disease (AD) is a type of dementia that is irreversible and continues to worsen over time; it is the most common form of dementia in the world affecting over 50 million people with projections of this figure tripling in 2050 due to aging populations. It is especially important to diagnose it early because therapeutic approaches are more effective when applied at the preclinical or MCI stages when the neuronal loss may be slowed down or reduced. However, the current diagnostic clinical tools which include clinical examination, MRI and PET are not effective in detecting the disease at early stages when intervention would be most beneficial.

Objective:

Primary objective 1. To build a robust, scalable predictive model capable of detecting early-stage AD with high precision. 2. By using a hybrid architecture that integrates imaging features, fluid biomarkers, genetic profiles, and longitudinal cognitive scores 3. we aim to provide clinically interpretable outputs that could guide personalized intervention strategies.

Methods:

This study is a multi-center, multimodal biomarker research to identify early AD using hybrid deep learning and ensemble-based machine learning techniques. The methodology combines high-level data structures, modern machine learning networks, and stringent statistical testing procedures in order to guarantee the method’s stability, transparency, and applicability to clinical scenarios

Results:

The findings of this study are organized to give a comprehensive account of the assessment of the predictive model, with an emphasis on the accuracy, modality invariance, and clinical interpretability. The findings show that the multimodal integration framework works effectively, is portable across centers, and has clinical applicability in the assessment of early onset Alzheimer’s Disease (AD).

Conclusions:

This work presents a new, multiplex machine learning approach to AD diagnosis at the prodromal stage by incorporating neuroimaging, biofluid markers, genomics, and neuropsychological tests. The model surpasses the results of single-modality and existing multimodal frameworks, showing high predictive accuracy and cross-dataset and cross-site generalization on ADNI, OASIS, and an independent clinic-based sample. CNN, LSTM networks, GNNs, federated learning and GAN based domain adaptation are employed to build a reliable and scalable model which can pave the way to develop a clinically feasible and interpretable biomarker of AD risk. However, the proposed model has some issues like data heterogeneity and limited demographic diversity, and thus the proposed model needs to be improved. The future studies should try to collect data from a broader population and apply new biomarkers for improving the prediction of the outcomes. It will, therefore, be crucial to test the model in clinical trials to determine its efficiency in real life. In summary, this work provides the evidence of the importance of the integrative and multiple approach for the early AD diagnostics and management, and can potentially contribute to better patient prognosis, as well as the development of individualized medicine.