IEEE Trans Biomed Eng. 2021 Jan;68(1):11-18. doi: 10.1109/TBME.2020.2990734. Epub 2020 Dec 21.
Alzheimer's disease (AD) is a neurodegenerative disorder that initially presents with memory loss in the presence of underlying neurofibrillary tangle and amyloid plaque pathology. Mild cognitive impairment is the initial symptomatic stage, which is an early window for detecting cognitive impairment prior to progressive decline and dementia. We recently developed the Visuospatial Memory Eye-Tracking Test (VisMET), a passive task capable of classifying cognitive impairment in AD in under five minutes. Here we describe the development of a mobile version of VisMET to enable efficient and widespread administration of the task.
We delivered VisMET on iPad devices and used a transfer learning approach to train a deep neural network to track eye gaze. Eye movements were used to extract memory features to assess cognitive status in a population of 250 individuals.
Mild to severe cognitive impairment was identifiable with a test accuracy of 70%. By enforcing a minimal eye tracking calibration error of 2 cm, we achieved an accuracy of 76% which is equivalent to the accuracy obtained using commercial hardware for eye-tracking.
This work demonstrates a mobile version of VisMET capable of estimating the presence of cognitive impairment.
Given the ubiquity of tablet devices, our approach has the potential to scale globally.
阿尔茨海默病（AD）是一种神经退行性疾病，最初表现为记忆丧失，同时伴有神经原纤维缠结和淀粉样斑块病理学。轻度认知障碍是初始症状阶段，是在认知能力进行性下降和痴呆之前检测认知障碍的早期窗口。我们最近开发了视空间记忆眼动追踪测试（VisMET），这是一种能够在不到五分钟内对 AD 患者的认知障碍进行分类的被动任务。在此，我们描述了 VisMET 的移动版本的开发，以实现该任务的高效和广泛应用。
我们在 iPad 设备上提供 VisMET，并使用迁移学习方法训练一个深度神经网络来跟踪眼球运动。通过眼球运动提取记忆特征，我们评估了 250 名个体的认知状态。
轻度至重度认知障碍可识别，测试准确率为 70%。通过强制执行 2 厘米的最小眼动校准误差，我们实现了 76%的准确率，这与使用商业眼动追踪硬件获得的准确率相当。
这项工作展示了一种能够估计认知障碍存在的 VisMET 移动版本。
鉴于平板电脑的普及，我们的方法有可能在全球范围内得到推广。
