AI-powered evaluation of dementia severity based on clinical data and visual scoring systems (MTA, ERICA, GCA) from MRI.

Dementia, particularly Alzheimer's disease (AD), is a growing concern in aging populations, with mild cognitive impairment (MCI) frequently progressing to AD. While existing studies often rely on comprehensive neuropsychological evaluations and assessments by neurologists and neuroradiologists, these approaches are not always feasible in routine or rural clinical practice. Current diagnostic methods rely on clinical assessments and MRI-based visual scoring systems such as MTA, ERICA, and GCA, requiring expert evaluation and leading to delays. This study presents an AI-based diagnostic framework utilizing deep learning models to predict visual scores and classify dementia stages using brain MRI and clinical measures such as TMSE and MoCA. Unlike prior methods that demand full expert oversight, our approach reduces reliance on specialized personnel by enabling AI-generated visual scores to support general radiologists and internists. ResNet18 was trained separately for MTA, ERICA, and GCA scoring, while DenseNet121 was applied for MRI-based dementia classification. Results indicate that models integrating AI-predicted Visual Scores with clinical data achieved up to 75.24% accuracy, outperforming MRI-only models (63.44%). Notably, the inclusion of MoCA unexpectedly reduced classification accuracy, suggesting potential biases in its application. Feature attribution using SHAP revealed that clinical inputs (MMSE, MoCA, age) dominated model decisions, while MRI scores played a greater role in AD classification. Age-stratified confusion matrices further uncovered forward-shifted misclassifications in younger patients, potentially indicating early disease sensitivity. By streamlining the diagnostic process and minimizing the need for licensed specialists, the AI system offers a promising tool for early dementia screening, particularly in areas with limited access to neurologists and radiologists, such as rural Thailand. Future studies will focus on refining model generalizability across diverse populations and improving prediction robustness in real-world clinical settings.