# MRI approach to the patient with suspected dementia: artificial intelligence techniques and semi-quantitative rating scales compared

## TL;DR

This study compares AI-based brain volume analysis and visual rating scales for diagnosing dementia, finding that visual scales are more sensitive while AI is more specific.

## Contribution

The study evaluates and compares the diagnostic performance of AI and semi-quantitative visual scales in dementia diagnosis using MRI.

## Key findings

- Semi-quantitative visual scales showed good inter-observer agreement and higher sensitivity in detecting brain atrophy.
- AI-based Quantib® ND had higher specificity but lower sensitivity compared to visual assessments.
- Lobar microbleeds were more common in patients with neurodegenerative diseases.

## Abstract

To assess the reliability of semi-quantitative and AI-based quantitative brain volume evaluation (Quantib® ND) in predicting clinical diagnosis in patients with suspected neurodegenerative diseases undergoing initial 1.5 T MRI. Additionally, to analyze the frequency of lobar microbleeds (MBs) at diagnosis.

Two neuroradiologists (2 vs. 10 years’ experience), blinded to diagnosis, independently evaluated brain atrophy on 3D-T1 images of 133 subjects using Scheltens, Koedam, and Kipps scales. Automated volumetric analysis was performed using Quantib® ND. SWI images were assessed by one neuroradiologist to classify MBs as cortical, juxtacortical, subcortical, or deep. Inter-observer agreement was measured using intraclass correlation coefficients (ICC); correlation with Quantib® ND was analyzed using Spearman's coefficient. Cohen's Kappa assessed agreement with clinical diagnosis.

Good inter-observer agreement was observed for the MTA scale (ICC 0.86 right, 0.82 left) and Kipps scale (ICC 0.76), with moderate concordance for Koedam (ICC 0.66). Frontal and posterior temporal Kipps subregions had good concordance (ICC 0.77, 0.79), while anterior temporal showed poor agreement (ICC 0.59). Diagnostic accuracy was moderate across observers and Quantib® ND. Observer 1 showed 77% sensitivity, 51% specificity; observer 2 had 79% sensitivity, 62% specificity; Quantib® ND reached 56% sensitivity, 74% specificity. Patients exhibited significantly more lobar MBs than non-dementia patients (χ2
p = 0.04).

Semi-quantitative visual scales proved effective and sensitive for detecting brain atrophy, showing good concordance with automated volumetric data. While AI-based quantification demonstrated higher specificity, visual assessment remained more sensitive. Lobar MBs were more frequent in neurodegenerative cases.