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

被引：16

作者：

Kim, Jun Sung ^{[1
,2
]}

Han, Ji Won ^{[2
,3
]}

Bae, Jong Bin ^{[2
]}

Moon, Dong Gyu ^{[2
]}

Shin, Jin ^{[2
]}

Kong, Juhee Eliana ^{[2
]}

Lee, Hyungji ^{[2
]}

Yang, Hee Won ^{[4
]}

Lim, Eunji ^{[5
]}

Kim, Jun Yup ^{[6
]}

Sunwoo, Leonard ^{[7
,8
]}

Cho, Se Jin ^{[7
,8
]}

Lee, Dongsoo ^{[9
]}

Kim, Injoong ^{[10
]}

Ha, Sang Won ^{[11
]}

Kang, Min Ju ^{[11
]}

Suh, Chong Hyun ^{[12
,13
]}

Shim, Woo Hyun ^{[12
,13
]}

Kim, Sang Joon ^{[12
,13
]}

Kim, Ki Woong ^{[1
,2
,3
,14
]}

机构：

[1] Seoul Natl Univ, Med Res Ctr, Inst Human Behav Med, Seoul, South Korea

[2] Seoul Natl Univ, Bundang Hosp, Dept Neuropsychiat, 82,Gumi Ro 173, Seongnam Si 13620, Gyeonggi Do, South Korea

[3] Seoul Natl Univ, Coll Med, Dept Psychiat, Seoul, South Korea

[4] Chungnam Natl Univ Hosp, Dept Psychiat, Daejeon, South Korea

[5] Gyeongsang Natl Univ, Changwon Hosp, Dept Neuropsychiat, Chang Won, South Korea

[6] Seoul Natl Univ, Bundang Hosp, Dept Neurol, Seongnam, South Korea

[7] Seoul Natl Univ, Bundang Hosp, Dept Radiol, Seongnam, South Korea

[8] Seoul Natl Univ, Coll Med, Dept Radiol, Seoul, South Korea

[9] VUNO Inc, Seoul, South Korea

[10] Vet Hlth Serv Med Ctr, Dept Radiol, Seoul, South Korea

[11] Vet Hlth Serv Med Ctr, Dept Neurol, Seoul, South Korea

[12] Univ Ulsan, Asan Med Ctr, Coll Med, Dept Radiol, Seoul, South Korea

[13] Univ Ulsan, Asan Med Ctr, Coll Med, Res Inst Radiol, Seoul, South Korea

[14] Seoul Natl Univ, Coll Nat Sci, Dept Brain & Cognit Sci, Seoul, South Korea

来源：

SCIENTIFIC REPORTS | 2022年 / 12卷 / 01期

关键词：

MILD COGNITIVE IMPAIRMENT; ASSOCIATION WORKGROUPS; NATIONAL INSTITUTE; RECOMMENDATIONS; GUIDELINES; ATROPHY; MRI; DEMENTIA;

D O I：

10.1038/s41598-022-22917-3

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页数：8

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