INTRODUCTION Automated skills assessment can provide surgical trainees with objective, personalized feedback during training. Here, we measure the efficacy of artificial intelligence (AI)-based feedback on a robotic suturing task. MATERIALS AND METHODS 42 participants with no robotic surgical experience were randomized to a control or feedback group and video-recorded while completing two rounds (R1 and R2) of suturing tasks on a daVinci surgical robot. Participants were assessed on needle handling and needle driving, and feedback was provided via a visual interface after R1. For feedback group, participants were informed of their AI-based skill assessment and presented with specific video clips from R1. For control group, participants were presented with randomly-selected video clips from R1 as a placebo. Participants from each group were further labeled as under-performers or innate-performers based on a median split of their technical skill scores from R1. RESULTS Demographic features were similar between control (n=20) and feedback group (n=22) (p>0.05). Observing the improvement from R1 to R2, feedback group had a significantly larger improvement in needle handling score (0.30 vs -0.02, p=0.018) when compared to control group, though the improvement of needle driving score was not significant when compared to control group (0.17 vs.-0.40, p=0.074). All innate-performers exhibited similar improvements across rounds, regardless of feedback (p>0.05). In contrast, under-performers in feedback group improved more than control group in needle handling (p=0.02). CONCLUSION AI-based feedback facilitates surgical trainees' acquisition of robotic technical skills, especially underperformers. Future research will extend AI-based feedback to additional suturing skills, surgical tasks, and experience groups.

中文翻译：

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基于人工智能的视频反馈可提高机器人缝合技能新手的表现 - 一项试点研究。

简介 自动化技能评估可以在培训期间为外科学员提供客观、个性化的反馈。在这里，我们测量了基于人工智能 (AI) 的反馈对机器人缝合任务的有效性。材料和方法 42 名没有机器人手术经验的参与者被随机分配到对照组或反馈组，并在达芬奇手术机器人上完成两轮（R1 和 R2）缝合任务时进行视频记录。对参与者的针处理和针驱动进行评估，并在 R1 后通过可视界面提供反馈。对于反馈组，参与者被告知他们基于人工智能的技能评估，并观看 R1 的特定视频剪辑。对于对照组，参与者随机选择 R1 中的视频片段作为安慰剂。根据 R1 中技术技能得分的中位数，每组参与者被进一步标记为表现不佳者或先天表现者。结果 对照组 (n=20) 和反馈组 (n=22) 之间的人口统计学特征相似 (p>0.05)。观察从R1到R2的改善，与对照组相比，反馈组在针处理评分方面有显着更大的改善（0.30 vs -0.02，p=0.018），但与对照组相比，针驱动评分的改善并不显着。 （0.17 与-0.40，p=0.074）。无论反馈如何，所有先天表现者在各轮中都表现出类似的进步（p>0.05）。相比之下，反馈组中表现不佳的人在针处理方面比对照组有更多改善（p=0.02）。结论 基于人工智能的反馈有助于外科学员（尤其是表现不佳的学员）获得机器人技术技能。未来的研究将把基于人工智能的反馈扩展到额外的缝合技能、手术任务和经验小组。