A case-control clinical trial on a deep learning-based classification system for diagnosis of amyloid-positive alzheimer’s disease

Jong Bin Bae, Subin Lee, Hyunwoo Oh, Jinkyeong Sung, Dongsoo Lee, Ji Won Han, Jun Sung Kim, Jae Hyoung Kim, Sang Eun Kim, Ki Woong Kim

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)1195-1203
Number of pages9
JournalPsychiatry Investigation
Volume20
Issue number12
DOIs
StatePublished - Dec 2023

Bibliographical note

Publisher Copyright:
© 2023 Korean Neuropsychiatric Association.

Keywords

  • Alzheimer disease
  • Clinical trial
  • Deep learning
  • Magnetic resonance imaging

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