<![CDATA[Background: : Neurodegenerative diseases such as dementia are characterized by progressive atrophy of the hippocampal structure. Accurate evaluation of brain morphometry is crucial for early diagnosis and monitoring disease progression. Currently, atrophy assessment is performed using the manual Medial Temporal Atrophy (MTA) Score, but this is subjective and depends on the assessor's experience. Advances in artificial intelligence (AI) technology, such as the AI-Rad Companion (AIRC) Brain MR, enable more objective, automated volumetric measurements. This study to analyze the level of correlation between the brain atrophy measurements using MTA Score method and the results of automatic volumetric measurements using the AIRC Brain MR program, as well as to assess the validity of the use of AI in brain morphometry in patients with dementia. Methods: This research was quasi-experimental. The sample was 32 dementia patients who underwent T1-W MPRAGE brain MRI examination at RSPON Dr. Mahardjono. MRI images were assessed visually using the MTA Score (0–4) and measured automatically with AIRC Brain MR. Data were analyzed using the Spearman Rank correlation test to determine the relationship between the MTA score and AI-derived hippocampal volume. Results: The results showed an average MTA score of 3 on both sides, indicating moderate to severe atrophy. The average hippocampal volume measured by AIRC was 3.0 ml (left) and 2.8 ml (right). The Spearman Rank test showed a very strong negative correlation between the MTA score and hippocampal volume (r = −0.898; p-value = 0.000). This means that the more severe the brain atrophy according to visual assessment, the smaller the hippocampal volume calculated automatically by AI. Conclusions: There was a very strong negative correlation between the manual MTA Score method and the automated volumetric measurements of AI-Rad Companion Brain MR in detecting brain atrophy in dementia patients. These results suggest that AIRC can be used as an objective validation tool in the evaluation of brain morphometry in neurodegenerative diseases. The use of AI technology is expected to improve the efficiency, accuracy, and consistency of brain atrophy diagnosis in MRI examinations.]]>
