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首页> 外文期刊>Proceedings of the IEEE >CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer-Assisted Interventions
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CAI4CAI: The Rise of Contextual Artificial Intelligence in Computer-Assisted Interventions

机译：CAI4CAI：上下文人工智能在计算机辅助干预中的兴起

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摘要

Data-driven computational approaches have evolved to enable extraction of information from medical images with reliability, accuracy, and speed, which is already transforming their interpretation and exploitation in clinical practice. While similar benefits are longed for in the field of interventional imaging, this ambition is challenged by a much higher heterogeneity. Clinical workflows within interventional suites and operating theaters are extremely complex and typically rely on poorly integrated intraoperative devices, sensors, and support infrastructures. Taking stock of some of the most exciting developments in machine learning and artificial intelligence for computer-assisted interventions, we highlight the crucial need to take the context and human factors into account in order to address these challenges. Contextual artificial intelligence for computer-assisted intervention (CAI4CAI) arises as an emerging opportunity feeding into the broader field of surgical data science. Central challenges being addressed in CAI4CAI include how to integrate the ensemble of prior knowledge and instantaneous sensory information from experts, sensors, and actuators; how to create and communicate a faithful and actionable shared representation of the surgery among a mixed human-AI actor team; and how to design interventional systems and associated cognitive shared control schemes for online uncertainty-aware collaborative decision-making ultimately producing more precise and reliable interventions.
机译：数据驱动的计算方法已经发展为能够以可靠，准确和快速的方式从医学图像中提取信息，这已经在临床实践中改变了它们的解释和开发方法。尽管在介入成像领域渴望获得类似的好处，但这种野心却受到更高异质性的挑战。介入套件和手术室中的临床工作流程非常复杂，并且通常依赖于集成不良的术中设备，传感器和支持基础设施。总结了计算机辅助干预中机器学习和人工智能领域最激动人心的发展，我们强调了应对这些挑战所必需的考虑背景和人为因素。用于计算机辅助干预的情境人工智能（CAI4CAI）涌入了外科手术数据科学领域的新兴机会。 CAI4CAI中要解决的主要挑战包括如何整合专家，传感器和执行器的先验知识和即时感官信息;如何在人与AI混合演员团队之间创建并传达切实可行的手术共享表示;以及如何设计干预系统和相关的认知共享控制方案，以进行在线不确定性感知的协作决策，最终产生更精确，更可靠的干预措施。

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