Utilizing deep learning to predict Alzheimer's disease and mild cognitive impairment with optical coherence tomography

Abstract

INTRODUCTION: Diagnostic performance of optical coherence tomography (OCT) to detect Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains limited. We aimed to develop a deep-learning algorithm using OCT to detect AD and MCI. METHODS: We performed a cross-sectional study involving 228 Asian participants (173 cases/55 controls) for model development and testing on 68 Asian (52 cases/16 controls) and 85 White (39 cases/46 controls) participants. Features from OCT were used to develop an ensemble trilateral deep-learning model. RESULTS: The trilateral model significantly outperformed single non-deep learning models in Asian (area under the curve [AUC] = 0.91 vs. 0.71–0.72, p = 0.022-0.032) and White (AUC = 0.84 vs. 0.58–0.75, p = 0.056- < 0.001) populations. However, its performance was comparable to that of the trilateral statistical model (AUCs similar, p > 0.05). DISCUSSION: Both multimodal approaches, using deep learning or traditional statistical models, show promise for AD and MCI detection. The choice between these models may depend on computational resources, interpretability preferences, and clinical needs. Highlights: A deep-learning algorithm was developed to detect Alzheimer's disease (AD) and mild cognitive impairment (MCI) using OCT images. The combined model outperformed single OCT parameters in both Asian and White cohorts. The study demonstrates the potential of OCT-based deep-learning algorithms for AD and MCI detection.