Development and Validation of a Machine Learning Classification Algorithm for Differentiating Frontotemporal Dementia from Alzheimer's Disease Using Automated Brain MRI Volumetry.

Seung Hyun Lee¹, Wooseok Jung¹, Mina Park¹, Hanna Cho¹, Hyeonwoo Cho¹, Hee Sang Oh¹, Bio Joo¹, Sung Jun Ahn¹, Chul Hyoung Lyoo¹, Sang Hyun Suh¹

¹From the VUNO Inc. (S.H.L., W.J., H.C.), Seoul, Korea; UC Berkeley-UCSF Joint Graduate Program in Bioengineering (W.J.); Department of Radiology (M.P., H.S.O., B.J., S.J.A., S.H.S.), Neurology (H.C.C.H.L.), Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea and Osstem Implant Co., Ltd., 3 (H.C.), Magokjungang 12-ro, Gangseo-gu, Seoul, Korea.

PubMed DOI

Abstract

BACKGROUND AND PURPOSE: Frontotemporal dementia (FTD) is characterized by frontal and anterior temporal lobe atrophy and progressive changes in behavior, executive function, or language, and its clinical and imaging presentations may overlap with Alzheimer's disease (AD), complicating differential diagnosis. This study aimed to develop and validate a classification algorithm using automated brain…

Create a free account to keep reading

Free members get 10 full research views every month across publications, clinical trials, FDA clearances, adverse events, and NIH grants. No credit card required.

Create Free Account Sign In

Want unlimited research access? See Pro plans

Data Accuracy Notice: Research intelligence on Health AI Central is aggregated from public sources (PubMed, ClinicalTrials.gov, FDA, NIH, CMS, and others) and refreshed nightly. Classifications and derived metrics are produced by automated methods described in our Methodology. We recommend verifying critical data points against the primary sources before making decisions.