TY - JOUR
T1 - A case-control clinical trial on a deep learning-based classification system for diagnosis of amyloid-positive alzheimer’s disease
AU - Bae, Jong Bin
AU - Lee, Subin
AU - Oh, Hyunwoo
AU - Sung, Jinkyeong
AU - Lee, Dongsoo
AU - Han, Ji Won
AU - Kim, Jun Sung
AU - Kim, Jae Hyoung
AU - Kim, Sang Eun
AU - Kim, Ki Woong
N1 - Publisher Copyright:
© 2023 Korean Neuropsychiatric Association.
PY - 2023/12
Y1 - 2023/12
N2 - Objective A deep learning-based classification system (DLCS) which uses structural brain magnetic resonance imaging (MRI) to diagnose Alzheimer’s disease (AD) was developed in a previous recent study. Here, we evaluate its performance by conducting a single-center, case-control clinical trial. Methods We retrospectively collected T1-weighted brain MRI scans of subjects who had an accompanying measure of amyloid-beta (Aβ) positivity based on a 18F-florbetaben positron emission tomography scan. The dataset included 188 Aβ-positive patients with mild cognitive impairment or dementia due to AD, and 162 Aβ-negative controls with normal cognition. We calculated the sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) of the DLCS in the classification of Aβ-positive AD patients from Aβ-negative controls. Results The DLCS showed excellent performance, with sensitivity, specificity, positive predictive value, negative predictive value, and AUC of 85.6% (95% confidence interval [CI], 79.8–90.0), 90.1% (95% CI, 84.5–94.2), 91.0% (95% CI, 86.3–94.1), 84.4% (95% CI, 79.2– 88.5), and 0.937 (95% CI, 0.911–0.963), respectively. Conclusion The DLCS shows promise in clinical settings where it could be routinely applied to MRI scans regardless of original scan purpose to improve the early detection of AD.
AB - Objective A deep learning-based classification system (DLCS) which uses structural brain magnetic resonance imaging (MRI) to diagnose Alzheimer’s disease (AD) was developed in a previous recent study. Here, we evaluate its performance by conducting a single-center, case-control clinical trial. Methods We retrospectively collected T1-weighted brain MRI scans of subjects who had an accompanying measure of amyloid-beta (Aβ) positivity based on a 18F-florbetaben positron emission tomography scan. The dataset included 188 Aβ-positive patients with mild cognitive impairment or dementia due to AD, and 162 Aβ-negative controls with normal cognition. We calculated the sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) of the DLCS in the classification of Aβ-positive AD patients from Aβ-negative controls. Results The DLCS showed excellent performance, with sensitivity, specificity, positive predictive value, negative predictive value, and AUC of 85.6% (95% confidence interval [CI], 79.8–90.0), 90.1% (95% CI, 84.5–94.2), 91.0% (95% CI, 86.3–94.1), 84.4% (95% CI, 79.2– 88.5), and 0.937 (95% CI, 0.911–0.963), respectively. Conclusion The DLCS shows promise in clinical settings where it could be routinely applied to MRI scans regardless of original scan purpose to improve the early detection of AD.
KW - Alzheimer disease
KW - Clinical trial
KW - Deep learning
KW - Magnetic resonance imaging
UR - http://www.scopus.com/inward/record.url?scp=85182157222&partnerID=8YFLogxK
U2 - 10.30773/pi.2023.0052
DO - 10.30773/pi.2023.0052
M3 - Article
AN - SCOPUS:85182157222
SN - 1738-3684
VL - 20
SP - 1195
EP - 1203
JO - Psychiatry Investigation
JF - Psychiatry Investigation
IS - 12
ER -