用眼动和图像内容数据，用深度学习区分早期痴呆高危人群。
Deep Learning in Mild Cognitive Impairment Diagnosis using Eye Movements and Image Content in Visual Memory Tasks
- 融合眼动轨迹、热力图与图像内容，构建多模态深度模型
- 在44人小样本上实现68%敏感度、76%特异度
- 适用于早期筛查，尤其适合资源有限的临床场景
阿尔茨海默病全球患病率预计到2050年翻倍，亟需可扩展的诊断工具。本研究利用带有眼动追踪的数字认知任务，结合记忆过程相关数据，区分健康对照组（HC）与轻度认知障碍（MCI）患者。基于VTNet的深度学习模型，使用44名参与者（24例MCI，20例HC）在视觉记忆任务中的眼动数据进行训练。模型整合了时间序列与空间信息，引入扫描路径、热力图及图像内容特征，并测试了图像分辨率与任务表现对模型的影响。最佳模型采用700×700px分辨率热力图，达到68%敏感度和76%特异度。尽管面临数据量小、任务时长短等挑战，性能仍与同类阿尔茨海默病研究相当（70%敏感度，73%特异度）。研究为自动化MCI诊断工具的发展提供支持，未来应聚焦模型优化与标准化长期视觉记忆任务的应用。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。