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Abstract #4349

Diagnosis of Frontotemporal Dementia on Brain MR Images by Using Automated Brain Volumetry

Seung Hyun Lee1, Wooseok Jung1, Hyeonwoo Cho2, Dong-Hee Kim1, and Mina Park3
1Vuno Inc., Seoul, Korea, Republic of, 2OSSTEM IMPLANT, Seoul, Korea, Republic of, 3Radiology, Yonsei University, College of Medicine, Seoul, Korea, Republic of

Synopsis

Keywords: Dementia, Dementia

Motivation: Clinical diagnosis of frontotemporal dementia (FTD) can be challenging due to its distinct regional atrophy patterns on MRI.

Goal(s): This study aimed to evaluate the effectiveness of a deep learning-based automated brain volumetry tool for the diagnosis of FTD using MRI.

Approach: The study included 759 subjects, with brain volumetry performed using VUNO Med DeepBrain software. Key volumetric features, including the frontal lobes, insula, cingulate cortex, and subcortical gray matter, were identified.

Results: linear SVM classifier using volumetric features, age and MMSE scores, achieved 89.3% accuracy in training, 92.0% in internal validation, and 84.6% in external validation, demonstrating strong diagnostic performance.

Impact: Our automated brain volumetry model demonstrated promising diagnostic accuracy for FTD, offering a potential tool for differentiating FTD from AD and normal in clinical settings.

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Keywords