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Development and evaluation of the second version of scrub nurse robot (SNR) for endoscopic and laparoscopic surgery

机译:用于内窥镜和腹腔镜手术的磨砂护士机器人(SNR)的第二个版本的开发和评估

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The shortage of nurses in large hospitals of developed countries has become a major problem. Especially, the shortage of scrub nurses, who assist operating surgeons exchange surgical instruments, has been chronically severe. To compensate for this shortage, we have been proposing the Scrub Nurse Robot (SNR) system that is capable of functioning as a skilled human scrub nurse in endoscopic and laparoscopic surgery. We developed the 2nd version of SNR, and achieved smooth and wide movement of its arms each with 4 D.O.F.. The 2nd SNR is able to speak several sentences and recognize some words as well as the names of surgical instruments, and is also capable of recognizing a surgeon’s intraoperative actions by its real-time visual recognition system (RTVRS). The RTVRS is basically composed of both a commercially-available 3D position tracking system and the algorithm that we developed to recognize surgeons’ actions during exchange of instruments from the above-mentioned positional data. In this paper, we evaluated how quickly and timely the RTVRS-driven SNR helped surgeon’s stand-ins exchange instruments in a laboratory, in comparison with human scrub nurses in real surgical operations. We found two problems about the current RTVRS-driven SNR: one is its response time and the other is chiefly related to the design and mechanism of the part storing the surgical instruments. 1) Concerning the first problem, the RTVRS-driven SNR took 2.11 sec until it finished holding out its hand with an instrument after it had detected a surgeon’s stand-in’s motions observed during extraction of a surgical instrument. However, a skilled real surgeon took 1.90 sec until he got the requested instrument in the clinical cases although he had to wait for as long as 1.24 sec until receiving it. Therefore, we must speed up the SNR’s performance at least by 0.2 sec to assist the real surgeon as human scrub nurses did. Especially, since 0.68 sec out- of the 2.11 sec was spent in data processing within the current RTVRS, we conclude that the performance of the RTVRS must be improved rather than speed-up of its arm movement. 2) The other problem was highlighted by measurement of the time during which the stand-ins and the real surgeon had to take their eyes from the monitor displaying the operative field within the abdomen. We termed this period of time ‘eyes-off’ time. The existence of ‘eyes-off’ time observed during his actions of returning an instrument after use and of waiting for the next instrument was regarded as unfavorable. The ‘eyes-off’ time was 2.34 sec in the laboratory whereas it was 0.19 sec in the clinical cases. The much longer ‘eyes-off’ time in the laboratory was partly due to inexperienced stand-ins’ performances, but mainly because of the design and mechanism of the part storing the instruments (a tool changer). To overcome these two problems, we are now developing the next version of SNR.
机译:发达国家大型医院的护士短缺已成为一个主要问题。特别是,擦洗护士的短缺,他们协助操作外科医生交换手术器械,一直严重。为了弥补这种短缺,我们一直在提出能够用作内窥镜和腹腔镜手术的熟练人磨碎的护士的磨砂护士机器人(SNR)系统。我们开发了SNR的第二版,并实现了臂的平稳宽阔,每间臂都有4个DOF。第二个SNR能够讲述几个句子并识别一些单词以及外科仪器的名称,也能够认识到外科医生通过实时视觉识别系统(RTVR)的术中行动。 RTVRS基本上由商业上可用的3D位置跟踪系统和算法组成,我们开发用于在从上述位置数据交换仪器期间识别外科医生的动作。在本文中,我们评估了RTVRS驱动的SNR在实验室中帮助外科医生的立式交换仪器,与真正的手术操作中的人磨损的人进行了评估。我们发现了关于当前RTVRS驱动的SNR的两个问题:一个是其响应时间,另一个主要与存储外科仪器的部件的设计和机制有关。 1)关于第一个问题,RTVRS驱动的SNR服用2.11秒,直到在检测到外科医生在提取手术器械期间观察到外科医生的待机运动后,它用仪器完成了手。然而,熟练的真实外科医生在临床案件中获得了1.90秒,虽然他不得不等到1.24秒,直至收到它。因此,我们必须至少加快SNR的表现,至少乘0.2秒,以协助真正的外科医生,因为人磨损的护士做了。特别是,由于2.11秒的0.68秒在当前RTVRS内的数据处理中度过,我们得出结论,RTVRS的性能必须得到改善而不是加速其手臂运动。 2)另一个问题是通过测量立式和实际外科医生从显示腹部内的操作场的监视器中的时间来突出显示的时间。我们在这段时间里被称为“睁大眼睛”。在使用后返回仪器的行动期间观察到“斜关”的存在,并等待下一个仪器被视为不利。在实验室中,“休息”时间是2.34秒,而临床病例则为0.19秒。实验室中的时间更长的时间是部分原因是由于缺乏经验的立式性能,而且主要是因为储存仪器的部件(工具更换器)的设计和机制。为了克服这两个问题,我们现在正在开发下一个版本的SNR。

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