IEEE J Biomed Health Inform. 2019 Sep;23(5):1865-1876. doi: 10.1109/JBHI.2019.2891729. Epub 2019 Jan 9.
New technology enables constant boost to the powers of mobile devices, which in the previous years have transformed from simple mobile phones to smart phones. Computational powers of these electronics enable actions that previously were possible only for computers. By the use of special applications, we may benefit from sensors and multimedia capabilities of operating systems. Therefore, a new era for devoted implementations opens, in which a smart application can take a role of computing system to estimate the symptoms of diseases by evaluating signals coming from a human body.
We propose a model of an application implemented for mobile android systems, which can be used for examination of central nervous system motor disorders occurring in patients suffering from Huntington (HD), Alzheimer, or Parkinson diseases. In particular, the model tracks tremors (involuntary movements), and cognitive (memory loss or dementia) impairments using touch and visual stimulus modalities. The proposed model interprets the symptoms from human bodies that indicate one of the diseases of the nervous system. Pre-processing of collected data for feature extraction is executed on a mobile device by using core functionality and methods provided in android's application programming interface. The information is evaluated by a back-propagation neural network classifier and the result is presented to the end user. The system is able to contact medical supervision and provide an assistance from the clinic.
The system uses a collected dataset of 1928 records, taken from 11 HD patients and 11 healthy persons in Lithuania, to gather statistics about examinations and presents the results as medical evaluation with prediction on the state of health. The accuracy of recognition of early, prodromal symptoms for central nervous system motor disorders is 86.4% (F-measure 0.859). The app (available on Google Play) is easy to use and is efficient tool for decision support in medical examinations.
The use of intelligent apps which can help to evaluate neurodegenerative disorders is an important enhancement to medical diagnosis. The developed smartphone app supports the doctor with additional results that are easy to compare with other examinations. This kind of examination is a nice change from classic stereotypes, especially for younger age patients, who are used to various aspects of information technology.
新技术使移动设备的功能不断增强，这些设备在过去几年中已从简单的手机转变为智能手机。这些电子产品的计算能力使以前只能在计算机上完成的操作成为可能。通过使用特殊的应用程序，我们可以利用操作系统的传感器和多媒体功能。因此，一个新的专门实现的时代已经开启，在这个时代，一个智能应用程序可以作为计算系统，通过评估来自人体的信号来估计疾病的症状。
我们提出了一种为移动安卓系统实现的应用模型，该模型可用于检查亨廷顿病（HD）、阿尔茨海默病或帕金森病患者的中枢神经系统运动障碍。具体来说，该模型通过触摸和视觉刺激模式跟踪震颤（不自主运动）和认知（记忆丧失或痴呆）障碍。所提出的模型解释了来自人体的指示神经系统疾病之一的症状。通过使用安卓应用程序编程接口提供的核心功能和方法，在移动设备上对收集的数据进行预处理，以进行特征提取。通过反向传播神经网络分类器对信息进行评估，并将结果呈现给最终用户。该系统能够联系医疗监督并提供来自诊所的帮助。
该系统使用从立陶宛的 11 名 HD 患者和 11 名健康人收集的 1928 个记录的数据集来收集有关检查的统计信息，并以医学评估的形式呈现结果，并预测健康状况。中枢神经系统运动障碍早期、前驱期症状识别的准确率为 86.4%（F 测度为 0.859）。该应用程序（可在 Google Play 上获得）易于使用，是医学检查中决策支持的有效工具。
使用可以帮助评估神经退行性疾病的智能应用程序是对医学诊断的重要增强。开发的智能手机应用程序为医生提供了易于与其他检查结果进行比较的附加结果。这种检查与经典的刻板印象相比是一个很好的变化，尤其是对于习惯信息技术各个方面的年轻患者。
