A Case-Control Clinical Trial on a Deep Learning-Based Classification System for Diagnosis of Amyloid-Positive Alzheimer’s Disease

• 발행기관: 대한신경정신의학회
• 저자명: 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
• 간행물 정보: 『Psychiatry Investigation』제20권 제12호, 1195~1203쪽, 전체 9쪽
• 주제분류: 의약학 > 정신과학
• 파일형태: PDF
• 발행일자: 2023.12.31

국문 초록

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, specific-ity, 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.

목차

INTRODUCTION

METHODS

RESULTS

DISCUSSION

키워드

Alzheimer disease Magnetic resonance imaging Clinical trial Deep learning