Yi Zheng, Grey Leonard, Herbert Zeh, Ann Majewicz Fey
{"title":"利用空间注意力确定表征外科手术任务中应力的显著运动特征。","authors":"Yi Zheng, Grey Leonard, Herbert Zeh, Ann Majewicz Fey","doi":"10.1142/s2424905x22410069","DOIUrl":null,"url":null,"abstract":"<p><p>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 (<i>p</i> <= 0.0001); Velocity (<i>p</i> <= 0.015) and jerk (<i>p</i> <= 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 (<i>p</i> = 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.</p>","PeriodicalId":73821,"journal":{"name":"Journal of medical robotics research","volume":"7 2-3","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10289589/pdf/nihms-1903565.pdf","citationCount":"0","resultStr":"{\"title\":\"Determining the Significant Kinematic Features for Characterizing Stress during Surgical Tasks Using Spatial Attention.\",\"authors\":\"Yi Zheng, Grey Leonard, Herbert Zeh, Ann Majewicz Fey\",\"doi\":\"10.1142/s2424905x22410069\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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 (<i>p</i> <= 0.0001); Velocity (<i>p</i> <= 0.015) and jerk (<i>p</i> <= 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 (<i>p</i> = 0.0000). 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引用次数: 0
摘要
研究表明,在腹腔镜手术中,术中压力会对外科医生的手术技能产生负面影响。对于新手外科医生来说,压力条件会导致手术器械尖端的速度、加速度和急动明显更高,从而导致更快但不太平稳的运动。然而,目前尚不清楚这些运动学特征(速度、加速度或急动)中的哪一个是识别正常和应力条件的最佳标志。因此,为了找到受术中应激影响的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。在之前IRB批准的一项实验中,我们收集了执行扩展peg转移任务的医学生的数据,他们被随机分为对照组和在外部心理压力下执行该任务的组。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”的正常或应力运动。在这项研究中,空间注意力机制被用来描述每个运动学特征对法向/应力运动分类的贡献。我们在Leave One User Out(LOOO)交叉验证下测试了我们的分类器,该分类器在使用运动学特征作为输入对“代表性”正常和受力运动进行分类时,总体准确率达到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有更高的关注度(p p p=0.0000)。总的来说,我们发现非优势手侧的急动可以更有效地用于表征新手外科医生的受力运动。
Determining the Significant Kinematic Features for Characterizing Stress during Surgical Tasks Using Spatial Attention.
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 (p <= 0.0001); Velocity (p <= 0.015) and jerk (p <= 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 (p = 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.