Multi-modal approaches to Alzheimer's disease diagnosis: Combining cognitive assessments with biomarkers and imaging

BackgroundAlzheimer's disease (AD) is a progressive neurodegenerative disorder where early diagnosis is essential for effective care.ObjectiveThis paper is set to compare the diagnostic performance of cognitive tests (Mini-Mental State Examination and Montreal Cognitive Assessment), serum biomarkers, EEG, and MRI separately and in combination with PET-CT results in the early diagnosis of AD.MethodsThe cognitive assessment was made in 384 individuals. blood sampling (biomarker tests), EEG monitoring, MRI, and PET-CT scans. Sensitivity, specificity, positive predictive value, and negative predictive value were used to determine diagnostic performance. The additional rule of probability and the product rule of probability were used to determine combined diagnostic power. ROC curves were plotted to visualize the performance of any modality.ResultsAmong 384 participants, PET-CT confirmed AD in 192 cases (50%). Serum biomarkers showed the highest individual sensitivity (77.60%), followed by MRI (69.79%), EEG (66.67%), and cognitive tests (62.50%). All modalities had a specificity of 84.90%. When combined using the addition rule of probability, diagnostic sensitivity increased to 99.15% and specificity to 99.95%. ROC curve analysis showed serum biomarkers and MRI had the highest diagnostic accuracy. The multi-modal approach significantly improved early diagnostic performance compared to single modalities.ConclusionsSerum biomarkers and MRI showed the best individual performance, though accuracy was only moderate. Combining modalities with the addition rule improved sensitivity and specificity markedly, while the product rule yielded low sensitivity and moderate specificity. Multimodal strategies may enhance early detection of AD but require further validation.