结合自身免疫治疗的混合现实可视化技术在传染病实时跟踪和治疗中的应用研究

Dharun Teja Vujjini, R. M. Salah, A. Alsadoon, P.W.C. Prasa
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引用次数: 1

摘要

医疗保健是生物过程结构的重要组成部分,特别是疾病的语义识别。死亡率的关键状态是通过使用智能手机应用程序确定和激活人类流行病来确定和激活人类流行病。然后,通过图像处理器对自身免疫性疾病进行诊断和治疗。组件系统分为三个属性:数据、预测技术和视图。数据从传感器、比特率、智能手机等多个属性和资源中收集。而预测技术则促进了能量响应、决策树、质量中心算法中的相关性、支持向量机分类器、枚举、误差反向传播和最小二乘缓解。根据几篇文章,使用预测技术可以通过分类和验证标准来治疗自身免疫治疗。基于图像引导手术(IGS)系统的混合现实可视化研究日益深入。然而,在手术室中并没有使用这么多。这可能是由于几个因素的结果,例如系统是从技术角度开发的,很少在现场进行评估。本文介绍了数据、可视化处理、视图(DVV)分类法,该分类法定义了实现混合现实IGS系统所需的每个主要组件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Survey on Real-Time Tracking and Treatment of Infectious Diseases Using Mixed Reality in Visualisation Technique with Autoimmune Therapy
Healthcare is a key part of the biological process structure in particular semantic recognition of diseases. Critical states of death rates are arranged by determining and activating human epidemics by using smartphone applications for determining and activating human epidemics. Then, it is diagnosed and treat people over autoimmune facility visualized by image processors. The components system is classified into three attributes: Data, Prediction technique, and View. Data are collected from several attributes and resources such as sensors, bit rates, smartphones. While, prediction techniques promote energy responses, decision trees, correlation in the algorithm of mass centric, SVM classifiers, enumeration, error backpropagation, and least square reliefs. Based on several articles, using prediction techniques can be benefited the treating autoimmune therapy by classifying groups and validating criteria. Mixed Reality visualizations based on Image Guided Surgery (IGS) systems increasingly study now. Nevertheless, has not been used in the Operating Room ever so much. It is may due to the result of several factors such as the systems are developed from a technical perspective and rarely evaluated in the field. This paper introduces the Data, Visualization processing, View (DVV) taxonomy which defines each of the major components required for implementing a Mixed Reality IGS system.
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