AI-assisted synthetic medical report generation using radiomic feature correlation-based PCA scores of multimodal data for dementia progression

Alzheimer’s disease (AD), a common form of dementia, is a progressive neurodegenerative condition that necessitates early and accurate diagnosis to minimize the risk of misdiagnosis, treatment delays, or inappropriate interventions as it advances from the cognitively normal (CN) stage to mild cognitive impairment (MCI) and ultimately to AD. Interpreting diverse multimodal data for reliable diagnosis and progression monitoring typically depends on expert evaluation, which can be subjective. Moreover, existing automated methods often rely on rigid, discrete classifications that fail to account for uncertainty and transitional dynamics between stages. To address these limitations, the proposed method provides a comprehensive and structured input from multimodal data to generate a synthetic medical report as a supportive document with AI tool guidance in the absence of detailed analysis and handwritten reports. MRI and PET data are used to extract radiomic features and generate correlation maps at both whole-brain and sub-brain levels. This allows for the simultaneous assessment of structural atrophy and metabolic changes. Diagnostic classification into CN, MCI, and AD is performed using multiple machine learning models, with explicit identification of possible inter-stage and cross-modality conflicts. Additionally, principal component analysis (PCA) is applied to these correlation maps to detect longitudinal progression patterns, ranging from stable to aggressive trajectories, and to pinpoint the most affected brain regions. The resulting system not only enhances stage classification accuracy but also provides insight into disease evolution by leveraging the structural and functional relationships captured through radiomic correlations. These outputs, encompassing diagnostic insights, imaging-based findings, cognitive score trajectories, and conflict analysis, are structured into input data suitable for AI-guided synthetic ground-truth report generation due to the absence of handwritten data. The collection of the structured input and ground truth reports is used to fine-tune LLMs to obtain optimal and meaningful results. This approach enables the automatic production of detailed, personalized diagnostic reports. Ultimately, the integration of radiomic correlation mapping with PCA-driven progression analysis offers a robust, non-invasive, and automated decision-support tool for comprehensive AD evaluation and reporting.