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利用空间注意力确定手术任务中表征应力的重要运动学特征。

Determining the Significant Kinematic Features for Characterizing Stress during Surgical Tasks Using Spatial Attention.

作者信息

Zheng Yi, Leonard Grey, Zeh Herbert, Fey Ann Majewicz

机构信息

Department of Mechanical Engineering, the University of Texas at Austin, Address, Austin, TX, USA.

Department of Surgery, the University of Texas Southwestern Medical Center, Address, Dallas, TX, USA.

出版信息

J Med Robot Res. 2022 Jun-Sep;7(2-3). doi: 10.1142/s2424905x22410069. Epub 2022 Aug 22.

Abstract

It has been shown that intraoperative stress can have a negative effect on surgeon surgical skills during laparoscopic procedures. For novice surgeons, stressful conditions can lead to significantly higher velocity, acceleration, and jerk of the surgical instrument tips, resulting in faster but less smooth movements. However, it is still not clear which of these kinematic features (velocity, acceleration, or jerk) is the best marker for identifying the normal and stressed conditions. Therefore, in order to find the most significant kinematic feature that is affected by intraoperative stress, we implemented a spatial attention-based Long-Short-Term-Memory (LSTM) classifier. In a prior IRB approved experiment, we collected data from medical students performing an extended peg transfer task who were randomized into a control group and a group performing the task under external psychological stresses. In our prior work, we obtained "representative" normal or stressed movements from this dataset using kinematic data as the input. In this study, a spatial attention mechanism is used to describe the contribution of each kinematic feature to the classification of normal/stressed movements. We tested our classifier under Leave-One-User-Out (LOUO) cross-validation, and the classifier reached an overall accuracy of 77.11% for classifying "representative" normal and stressed movements using kinematic features as the input. More importantly, we also studied the spatial attention extracted from the proposed classifier. Velocity and acceleration on both sides had significantly higher attention for classifying a normal movement ( <= 0.0001); Velocity ( <= 0.015) and jerk ( <= 0.001) on non-dominant hand had significant higher attention for classifying a stressed movement, and it is worthy noting that the attention of jerk on non-dominant hand side had the largest increment when moving from describing normal movements to stressed movements ( = 0.0000). In general, we found that the jerk on non-dominant hand side can be used for characterizing the stressed movements for novice surgeons more effectively.

摘要

研究表明,术中应激会对腹腔镜手术过程中外科医生的手术技能产生负面影响。对于新手外科医生而言,应激状态会导致手术器械尖端的速度、加速度和加加速度显著提高,从而使动作更快但更不平稳。然而,目前尚不清楚这些运动学特征(速度、加速度或加加速度)中哪一个是识别正常状态和应激状态的最佳指标。因此,为了找到受术中应激影响最显著的运动学特征,我们实现了一种基于空间注意力的长短期记忆(LSTM)分类器。在一项先前经机构审查委员会(IRB)批准的实验中,我们收集了医学生执行扩展栓子转移任务的数据,这些学生被随机分为对照组和在外部心理应激下执行任务的组。在我们之前的工作中,我们使用运动学数据作为输入,从该数据集中获得了“代表性”的正常或应激运动。在本研究中,采用空间注意力机制来描述每个运动学特征对正常/应激运动分类的贡献。我们在留一用户出(LOUO)交叉验证下测试了我们的分类器,该分类器使用运动学特征作为输入对“代表性”正常和应激运动进行分类时,总体准确率达到了77.11%。更重要的是,我们还研究了从所提出的分类器中提取的空间注意力。两侧的速度和加速度在对正常运动进行分类时具有显著更高的注意力(<=0.0001);非优势手的速度(<=0.015)和加加速度(<=0.001)在对应激运动进行分类时具有显著更高的注意力,值得注意的是,当从描述正常运动转变为应激运动时,非优势手一侧加加速度的注意力增量最大(=0.0000)。总体而言,我们发现非优势手一侧的加加速度可以更有效地用于表征新手外科医生的应激运动。

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本文引用的文献

1
Identifying Kinematic Markers Associated with Intraoperative Stress during Surgical Training Tasks.
Int Symp Med Robot. 2021 Nov;2021. doi: 10.1109/ismr48346.2021.9661482. Epub 2022 Jan 3.
2
Frame-wise detection of surgeon stress levels during laparoscopic training using kinematic data.
Int J Comput Assist Radiol Surg. 2022 Apr;17(4):785-794. doi: 10.1007/s11548-022-02568-5. Epub 2022 Feb 12.
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Computer Vision in the Surgical Operating Room.
Visc Med. 2020 Dec;36(6):456-462. doi: 10.1159/000511934. Epub 2020 Oct 15.
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IEEE Trans Pattern Anal Mach Intell. 2020 Jan;42(1):154-163. doi: 10.1109/TPAMI.2018.2876404. Epub 2018 Oct 16.
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