标题： 使用眼动和视觉记忆任务中的图像内容在轻度认知障碍诊断中的深度学习 显示英文标题

标题： Deep Learning in Mild Cognitive Impairment Diagnosis using Eye Movements and Image Content in Visual Memory Tasks

摘要： 全球痴呆症的患病率预计到2050年将翻一番，突显了对可扩展诊断工具的迫切需求。 本研究利用与记忆过程相关的数字认知任务和眼动追踪数据，以区分健康对照组（HC）和轻度认知障碍（MCI），即痴呆的前兆。 基于VTNet的深度学习模型使用44名参与者（24名MCI，20名HC）在视觉记忆任务中的眼动追踪数据进行训练。 该模型同时利用从眼动追踪中得出的时间序列和空间数据。 它被修改以纳入扫描路径、热图和图像内容。 这些修改还使得可以测试诸如图像分辨率和任务表现等参数，分析它们对模型性能的影响。 最佳模型使用$700\times700px$分辨率的热图，达到了68%的敏感性和76%的特异性。 尽管在更具挑战性的条件下运行（例如，数据集规模较小、任务持续时间较短或任务标准化程度较低），该模型的性能与使用类似方法的阿尔茨海默病研究相当（70%的敏感性和73%的特异性）。 这些发现有助于开发用于MCI的自动化诊断工具。 未来的工作应集中在优化模型和使用标准化的长期视觉记忆任务上。 显示英文摘要

摘要： The global prevalence of dementia is projected to double by 2050, highlighting the urgent need for scalable diagnostic tools. This study utilizes digital cognitive tasks with eye-tracking data correlated with memory processes to distinguish between Healthy Controls (HC) and Mild Cognitive Impairment (MCI), a precursor to dementia. A deep learning model based on VTNet was trained using eye-tracking data from 44 participants (24 MCI, 20 HCs) who performed a visual memory task. The model utilizes both time series and spatial data derived from eye-tracking. It was modified to incorporate scan paths, heat maps, and image content. These modifications also enabled testing parameters such as image resolution and task performance, analyzing their impact on model performance. The best model, utilizing $700\times700px$ resolution heatmaps, achieved 68% sensitivity and 76% specificity. Despite operating under more challenging conditions (e.g., smaller dataset size, shorter task duration, or a less standardized task), the model's performance is comparable to an Alzheimer's study using similar methods (70% sensitivity and 73% specificity). These findings contribute to the development of automated diagnostic tools for MCI. Future work should focus on refining the model and using a standardized long-term visual memory task.