C2A: Crowd consensus analytics for virtual colonoscopy

Ji Hwan Park, S. Nadeem, Seyedkoosha Mirhosseini, A. Kaufman
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引用次数: 11

Abstract

We present a medical crowdsourcing visual analytics platform called C2A to visualize, classify and filter crowdsourced clinical data. More specifically, C2A is used to build consensus on a clinical diagnosis by visualizing crowd responses and filtering out anomalous activity. Crowdsourcing medical applications have recently shown promise where the non-expert users (the crowd) were able to achieve accuracy similar to the medical experts. This has the potential to reduce interpretation/reading time and possibly improve accuracy by building a consensus on the findings beforehand and letting the medical experts make the final diagnosis. In this paper, we focus on a virtual colonoscopy (VC) application with the clinical technicians as our target users, and the radiologists acting as consultants and classifying segments as benign or malignant. In particular, C2A is used to analyze and explore crowd responses on video segments, created from fly-throughs in the virtual colon. C2A provides several interactive visualization components to build crowd consensus on video segments, to detect anomalies in the crowd data and in the VC video segments, and finally, to improve the non-expert user's work quality and performance by A/B testing for the optimal crowdsourcing platform and application-specific parameters. Case studies and domain experts feedback demonstrate the effectiveness of our framework in improving workers' output quality, the potential to reduce the radiologists' interpretation time, and hence, the potential to improve the traditional clinical workflow by marking the majority of the video segments as benign based on the crowd consensus.
C2A:虚拟结肠镜检查的人群共识分析
我们提出了一个名为C2A的医疗众包可视化分析平台,对众包临床数据进行可视化、分类和过滤。更具体地说,C2A用于通过可视化人群反应和过滤异常活动来建立对临床诊断的共识。众包医疗应用程序最近显示出希望,非专业用户(人群)能够达到与医疗专家相似的准确性。这有可能减少解释/阅读时间,并可能通过事先就发现建立共识来提高准确性,并让医学专家做出最终诊断。在本文中,我们着重于一个虚拟结肠镜(VC)应用,临床技术人员作为我们的目标用户,放射科医生作为顾问,并分类为良性或恶性节段。特别是,C2A用于分析和探索视频片段上的人群反应,这些视频片段是由虚拟冒号中的飞行片段创建的。C2A提供了多个交互式可视化组件,用于构建人群对视频片段的共识,检测人群数据和VC视频片段中的异常,最后通过A/B测试,针对最优众包平台和特定应用参数,提高非专业用户的工作质量和性能。案例研究和领域专家的反馈证明了我们的框架在提高工作人员输出质量方面的有效性,减少放射科医生解释时间的潜力,因此,通过根据人群共识将大多数视频片段标记为良性,从而改善传统临床工作流程的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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