Artificial Intelligence Assisted Surgical Scene Recognition: A Comparative Study Amongst Healthcare Professionals.

IF 7.5 1区 医学 Q1 SURGERY Annals of surgery Pub Date : 2024-10-30 DOI:10.1097/SLA.0000000000006577
Simon C Williams, Jinfan Zhou, William R Muirhead, Danyal Z Khan, Chan Hee Koh, Razna Ahmed, Jonathan P Funnell, John G Hanrahan, Alshaymaa Mortada Ali, Shankhaneel Ghosh, Tarık Sarıdoğan, Alexandra Valetopoulou, Patrick Grover, Danail Stoyanov, Mary Murphy, Evangelos B Mazomenos, Hani J Marcus
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Abstract

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.

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人工智能辅助手术场景识别:医护人员之间的比较研究。
研究目的本研究旨在比较深度学习平台(MACSSwin-T 模型)与医护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人工智能辅助和没有人工智能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前所未有的规模采集手术视频数据。作为人工智能(AI)的一个分支,计算机视觉技术的进步实现了手术视频的自动分析。这些进步可能会给临床医生、医疗系统和患者带来益处,但这些益处尚未实现: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(均处于不同的培训和经验阶段)查看了动脉瘤剪切手术的静态帧,并将帧标记为 "动脉瘤不在帧中 "或 "动脉瘤在帧中"。然后由人工智能平台对帧进行分析。神经外科团队在人工智能的协助下进行了第二轮数据收集。分别计算了人类组、人工智能组和人工智能辅助人类组的动脉瘤检测准确率:结果:从 338 名医护人员处收集整理了 5,154 份个人框架审查报告。在没有人工智能辅助的情况下,医护人员对 70% 的病例进行了正确标注,而在有人工智能辅助的情况下,这一比例为 78%(OR 1.77,PC 结论:人工智能辅助下的人类表现与人工智能辅助下的人类表现相差甚远:人工智能辅助下的人类表现超过了人类和人工智能本身。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框架准确性都有所提高,尤其是经验最丰富的医疗保健专业人员。这些结果挑战了 "人工智能主要有利于初级临床医生 "的普遍观点,强调了人工智能作为现代外科实践的重要组成部分,在整个外科等级制度中的关键作用。
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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来源期刊
Annals of surgery
Annals of surgery 医学-外科
CiteScore
14.40
自引率
4.40%
发文量
687
审稿时长
4 months
期刊介绍: The Annals of Surgery is a renowned surgery journal, recognized globally for its extensive scholarly references. It serves as a valuable resource for the international medical community by disseminating knowledge regarding important developments in surgical science and practice. Surgeons regularly turn to the Annals of Surgery to stay updated on innovative practices and techniques. The journal also offers special editorial features such as "Advances in Surgical Technique," offering timely coverage of ongoing clinical issues. Additionally, the journal publishes monthly review articles that address the latest concerns in surgical practice.
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