Pervasive Computing Applications, Research Studios Austria, Vienna, Austria.
Institute for Pervasive Computing, Johannes Kepler University Linz, Linz, Austria.
Med Biol Eng Comput. 2017 Oct;55(10):1719-1734. doi: 10.1007/s11517-017-1670-6. Epub 2017 Jul 10.
With the introduction of operating rooms of the future context awareness has gained importance in the surgical environment. This paper organizes and reviews different approaches for recognition of context in surgery. Major electronic research databases were queried to obtain relevant publications submitted between the years 2010 and 2015. Three different types of context were identified: (i) the surgical workflow context, (ii) surgeon's cognitive and (iii) technical state context. A total of 52 relevant studies were identified and grouped based on the type of context detected and sensors used. Different approaches were summarized to provide recommendations for future research. There is still room for improvement in terms of methods used and evaluations performed. Machine learning should be used more extensively to uncover hidden relationships between different properties of the surgeon's state, particularly when performing cognitive context recognition. Furthermore, validation protocols should be improved by performing more evaluations in situ and with a higher number of unique participants. The paper also provides a structured outline of recent context recognition methods to facilitate development of new generation context-aware surgical support systems.
随着未来手术室环境中语境意识的引入,手术环境中的语境识别变得越来越重要。本文组织并回顾了不同的手术环境中语境识别方法。主要电子研究数据库被查询以获取 2010 年至 2015 年间提交的相关出版物。确定了三种不同类型的语境:(i)手术工作流程语境,(ii)外科医生的认知和(iii)技术状态语境。共确定了 52 项相关研究,并根据检测到的语境类型和使用的传感器进行了分组。总结了不同的方法,为未来的研究提供建议。在使用的方法和进行的评估方面仍有改进的空间。应更广泛地使用机器学习来揭示外科医生状态的不同属性之间隐藏的关系,特别是在进行认知语境识别时。此外,应通过更多的现场评估和更多独特参与者的评估来改进验证协议。本文还提供了最近语境识别方法的结构化概述,以促进新一代语境感知手术支持系统的开发。