OBJECTIVE This study aimed to compare the ability of a deep-learning platform (the MACSSwin-T model) with healthcare professionals in detecting cerebral aneurysms from operative videos. Secondly, we aimed to compare the neurosurgical team's ability to detect cerebral aneurysms with and without AI-assistance. BACKGROUND Modern microscopic surgery enables the capture of operative video data on an unforeseen scale. Advances in computer vision, a branch of artificial intelligence (AI), have enabled automated analysis of operative video. These advances are likely to benefit clinicians, healthcare systems, and patients alike, yet such benefits are yet to be realised. METHODS In a cross-sectional comparative study, neurosurgeons, anaesthetists, and operating room (OR) nurses, all at varying stages of training and experience, reviewed still frames of aneurysm clipping operations and labelled frames as "aneurysm not in frame" or "aneurysm in frame". Frames then underwent analysis by the AI platform. A second round of data collection was performed whereby the neurosurgical team had AI-assistance. Accuracy of aneurysm detection was calculated for human only, AI only, and AI-assisted human groups. RESULTS 5,154 individual frame reviews were collated from 338 healthcare professionals. Healthcare professionals correctly labelled 70% of frames without AI assistance, compared to 78% with AI-assistance (OR 1.77, P<0.001). Neurosurgical Attendings showed the greatest improvement, from 77% to 92% correct predictions with AI-assistance (OR 4.24, P=0.003). CONCLUSION AI-assisted human performance surpassed both human and AI alone. Notably, across healthcare professionals surveyed, frame accuracy improved across all subspecialties and experience levels, particularly among the most experienced healthcare professionals. These results challenge the prevailing notion that AI primarily benefits junior clinicians, highlighting its crucial role throughout the surgical hierarchy as an essential component of modern surgical practice.

中文翻译：

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人工智能辅助手术场景识别：医疗保健专业人员之间的比较研究。


目的 本研究旨在比较深度学习平台 （MACSSwin-T 模型） 与医疗保健专业人员从手术视频中检测脑动脉瘤的能力。其次，我们旨在比较神经外科团队在有和没有 AI 辅助的情况下检测脑动脉瘤的能力。背景 现代显微手术能够以不可预见的规模捕获手术视频数据。计算机视觉是人工智能 （AI） 的一个分支，其进步使手术视频的自动分析成为可能。这些进步可能会使临床医生、医疗保健系统和患者都受益，但这些好处尚未实现。方法 在一项横断面比较研究中，神经外科医生、麻醉师和手术室 （OR） 护士都处于不同的培训和经验阶段，回顾了动脉瘤夹闭手术的静态框架，并将框架标记为“动脉瘤不在框架中”或“框架内动脉瘤”。然后，AI 平台对帧进行了分析。进行了第二轮数据收集，神经外科团队获得了 AI 辅助。计算仅人类、仅 AI 和 AI 辅助人类组的动脉瘤检测准确性。结果 整理了来自 338 名医疗保健专业人员的 5,154 个单独的框架评论。医疗保健专业人员在没有 AI 协助的情况下正确标记了 70% 的帧，而使用 AI 辅助时，这一比例为 78% （OR 1.77，P<0.001）。神经外科就诊者显示出最大的改善，在 AI 辅助下从 77% 到 92% 的正确预测 （OR 4.24，P=0.003）。结论 AI 辅助人类的表现仅超过了人类和 AI。 值得注意的是，在接受调查的医疗保健专业人员中，所有亚专业和经验水平的帧准确性都有所提高，尤其是在最有经验的医疗保健专业人员中。这些结果挑战了人工智能主要使初级临床医生受益的流行观念，突出了它作为现代外科实践的重要组成部分在整个外科层次结构中的关键作用。