Brain CT Volumetric Analysis for Early Dementia Prediction

The patent substitutes expensive, complex imaging modalities (MRI, PET) with a cheaper, more accessible modality (CT scanning). CT scanners are more widely available and less costly than MRI or PET systems, while still providing sufficient diagnostic information when combined with the specialized AI analysis pipeline for detecting dementia-related brain atrophy patterns.

The patent transforms the diagnostic approach by changing the analysis parameters - instead of relying on the inherent superiority of MRI/PET hardware, it enhances CT scanning through advanced image processing and AI-based volumetric analysis. The system extracts and analyzes specific brain region volumes (hippocampus, entorhinal cortex, etc.) using machine learning models, converting a limited modality into a powerful diagnostic tool through parameter optimization.

The patent applies segmentation by dividing the brain into specific regions of interest (hippocampus, entorhinal cortex, amygdala, etc.) and analyzing each region's volume separately. This segmentation allows for precise measurement of atrophy in dementia-vulnerable areas, enabling early detection before global brain changes become apparent. The system uses AI models to automatically segment and measure these regions from CT images with high precision.

The patent replaces traditional visual inspection and manual measurement methods with automated AI-based image analysis. Machine learning models automatically detect, segment, and measure brain region volumes, substituting human expertise with computational algorithms that provide consistent, reproducible, and more precise measurements. This substitution enables detection of subtle atrophy patterns that would be difficult to identify through conventional methods.

The patent implements self-service through automated AI analysis that performs all diagnostic measurements without human intervention. The system automatically segments brain regions, calculates volumes, compares them to normative databases, and generates diagnostic reports. This eliminates the need for manual tracing and measurement by radiologists, significantly reducing analysis time while maintaining or improving measurement accuracy and eliminating inter-observer variability.

The patent substitutes manual analytical methods with automated computational algorithms. Instead of radiologists manually outlining brain structures and calculating volumes, machine learning models automatically perform these tasks in minutes. This substitution dramatically increases productivity while reducing the time investment required for each diagnostic case and eliminating human subjectivity in measurements.