Towards a Comprehensive Solution for a Vision-Based Digitized Neurological Examination.
Towards a Comprehensive Solution for a Vision-Based Digitized Neurological Examination.
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DOI：
10.1109/jbhi.2022.3167927
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发表时间：
2022-08
影响因子：
7.7
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中科院分区：
文献类型：
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使用数字记录和量化的神经系统检查信息的能力对于帮助医疗保健系统提供更好的护理，面对面和通过远程医疗非常重要，因为它们弥补了神经科医生日益短缺的问题。然而，目前的神经学数字生物标志物管道被缩小到特定的神经学检查组件或应用于评估特定的条件。在本文中，我们提出了一种可访问的基于视觉的检查和文档解决方案，称为数字化神经检查（DNE），以使用智能手机/平板电脑扩展检查生物标志物记录选项和临床应用。通过我们的DNE软件，临床环境中的医疗保健提供者和在家的人们能够在执行指示的神经系统测试时视频捕获检查，包括手指敲击，手指到手指，前臂滚动以及站立和行走。我们的DNE软件模块化设计支持附加测试的集成。DNE从记录的检查中提取2D/3D人体姿势并量化运动学和时空特征。这些功能与临床相关，允许临床医生记录和观察量化的运动以及这些指标随时间的变化。一个网络服务器和一个用户界面，用于记录查看和功能可视化。DNE进行了评估，对收集的数据集的21名受试者包含正常和模拟受损的运动。DNE的整体准确性通过使用各种机器学习模型对记录的运动进行分类来证明。我们的测试表明，上肢测试的准确性超过90%，站立和行走测试的准确性超过80%。
The ability to use digitally recorded and quantified neurological exam information is important to help healthcare systems deliver better care, in-person and via telehealth, as they compensate for a growing shortage of neurologists. Current neurological digital biomarker pipelines, however, are narrowed down to a specific neurological exam component or applied for assessing specific conditions. In this paper, we propose an accessible vision-based exam and documentation solution called Digitized Neurological Examination (DNE) to expand exam biomarker recording options and clinical applications using a smartphone/tablet. Through our DNE software, healthcare providers in clinical settings and people at home are enabled to video capture an examination while performing instructed neurological tests, including finger tapping, finger to finger, forearm roll, and stand-up and walk. Our modular design of the DNE software supports integrations of additional tests. The DNE extracts from the recorded examinations the 2D/3D human-body pose and quantifies kinematic and spatio-temporal features. The features are clinically relevant and allow clinicians to document and observe the quantified movements and the changes of these metrics over time. A web server and a user interface for recordings viewing and feature visualizations are available. DNE was evaluated on a collected dataset of 21 subjects containing normal and simulated-impaired movements. The overall accuracy of DNE is demonstrated by classifying the recorded movements using various machine learning models. Our tests show an accuracy beyond 90% for upper-limb tests and 80% for the stand-up and walk tests.