Deep learning-based diagnosis of Alzheimer’s disease using brain magnetic resonance images: an empirical study

Jun Sung Kim, Ji Won Han, Jong Bin Bae, Dong Gyu Moon, Jin Shin, Juhee Eliana Kong, Hyungji Lee, Hee Won Yang, Eunji Lim, Jun Yup Kim, Leonard Sunwoo, Se Jin Cho, Dongsoo Lee, Injoong Kim, Sang Won Ha, Min Ju Kang, Chong Hyun Suh, Woo Hyun Shim, Sang Joon Kim, Ki Woong Kim

Research output: Contribution to journalArticlepeer-review

Abstract

The limited accessibility of medical specialists for Alzheimer’s disease (AD) can make obtaining an accurate diagnosis in a timely manner challenging and may influence prognosis. We investigated whether VUNO Med-DeepBrain AD (DBAD) using a deep learning algorithm can be employed as a decision support service for the diagnosis of AD. This study included 98 elderly participants aged 60 years or older who visited the Seoul Asan Medical Center and the Korea Veterans Health Service. We administered a standard diagnostic assessment for diagnosing AD. DBAD and three panels of medical experts (ME) diagnosed participants with normal cognition (NC) or AD using T1-weighted magnetic resonance imaging. The accuracy (87.1% for DBAD and 84.3% for ME), sensitivity (93.3% for DBAD and 80.0% for ME), and specificity (85.5% for DBAD and 85.5% for ME) of both DBAD and ME for diagnosing AD were comparable; however, DBAD showed a higher trend in every analysis than ME diagnosis. DBAD may support the clinical decisions of physicians who are not specialized in AD; this may enhance the accessibility of AD diagnosis and treatment.

Original languageEnglish
Article number18007
JournalScientific Reports
Volume12
Issue number1
DOIs
StatePublished - Dec 2022

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