Using a dual-stream attention neural network to characterize mild cognitive impairment based on retinal images

计算机科学 人工智能 痴呆 认知 模式识别(心理学) 卷积神经网络 医学 心理学 神经科学 疾病 病理
作者
He-Bei Gao,Shuai-Ye Zhao,Zheng Gu,Xinmin Wang,Runyi Zhao,Zhigeng Pan,Hong Li,Fan Lü,Meixiao Shen
出处
期刊:Computers in Biology and Medicine [Elsevier BV]
卷期号:166: 107411-107411 被引量:5
标识
DOI:10.1016/j.compbiomed.2023.107411
摘要

Mild cognitive impairment (MCI) is a critical transitional stage between normal cognition and dementia, for which early detection is crucial for timely intervention. Retinal imaging has been shown as a promising potential biomarker for MCI. This study aimed to develop a dual-stream attention neural network to classify individuals with MCI based on multi-modal retinal images. Our approach incorporated a cross-modality fusion technique, a variable scale dense residual model, and a multi-classifier mechanism within the dual-stream network. The model utilized a residual module to extract image features and employed a multi-level feature aggregation method to capture complex context information. Self-attention and cross-attention modules were utilized at each convolutional layer to fuse features from optical coherence tomography (OCT) and fundus modalities, resulting in multiple output losses. The neural network was applied to classify individuals with MCI, Alzheimer's disease, and control participants with normal cognition. Through fine-tuning the pre-trained model, we classified community-dwelling participants into two groups based on cognitive impairment test scores. To identify retinal imaging biomarkers associated with accurate prediction, we used the Gradient-weighted Class Activation Mapping technique. The proposed method achieved high precision rates of 84.96% and 80.90% in classifying MCI and positive test scores for cognitive impairment, respectively. Notably, changes in the optic nerve head on fundus photographs or OCT images among patients with MCI were not used to discriminate patients from the control group. These findings demonstrate the potential of our approach in identifying individuals with MCI and emphasize the significance of retinal imaging for early detection of cognitive impairment.
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