Purpose Leap Motion Controller is a device that can capture hand gestures and reproduce these as data comprising several parametric elements. We analyzed surgical suture motion using this device and investigated the optical methodology for clinical applications. Methods We recruited medical students and residents (novice group) and vascular surgeons (specialist group). The operators applied sutures once on a prosthetic graft, and the captured motion was analyzed. Results Ten novices, who each received procedural instruction for at least 2 h, and 10 specialists were recruited. The hand gesture consisted of four elements (roll, pitch, yaw, and wrist angle). Since “roll” was the only element in this simple suture movement that showed some difference between the two groups, we analyzed three parameters: (1) the suturing time, (2) the difference in the degree between two piercing points, and (3) slope of the roll. We found that the specialist group demonstrated significantly shorter suturing times and a larger degree of the slope. Conclusion Leap Motion Controller analysis with the roll revealed that the novices could use the roll motion after only 2 h of instruction; however, the suturing speed and smoothness were secondary to those of the specialists.
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To compensate for severe shortage of scrub nurses who support surgeons during surgery, Miyawaki et al. have developed a scrub nurse robot (SNR) system [1,2,3,4,5]. One of its current challenges is how to make the SNR recognize surgical procedures which compose a surgical operation and understand/predict surgeons' intentions.Therefore, in this paper, we propose a visual recognition system for surgeons' actions based on convolutional neural network (CNN). We developed a temporal pose feature (TPF) CNN, which is a method to recognize surgical procedures based on the body movements of a surgeon's stand-in during a simulated surgical operation. We used OpenPose to extract the pose feature vectors from every frame of the short videos filmed our simulated surgery. Besides, we used a matrix of which the pose vectors were chronologically ordered as the input of CNN by considering it as the pseudograyscale image.We show that the TPF CNN was more accurate in the objects of this study than the conventional LSTM, which is used to recognize time series data. The TPF CNN shows higher recognition accuracy with fewer training than LSTM. Our results suggest that surgeons' body movements may contain much information to be required for recognizing subtle differences in several types of surgical procedures.
Purpose Surgical procedures are often evaluated subjectively, and an objective evaluation has been considered difficult to make and rarely reported, especially in open surgery, where the range of motion is wide. This study evaluated the effectiveness of surgical suturing training as an educational tool using the Leap Motion Controller (LMC), which can capture hand movements and reproduce them as data comprising parametric elements. Methods We developed an off-the-job training system (Off-JT) in our department, mainly using prosthetic grafts and various anastomotic methodologies with graded difficulty levels. We recruited 50 medical students (novice group) and 6 vascular surgeons (expert group) for the study. We evaluated four parameters for intraoperative skills: suturing time, slope of the roll, smoothness, and rate of excess motion. Results All 4 parameters distinguished the skill of the novice group at 1 and 10 h off-JT. After 10 h of off-JT, all 4 parameters of the novices were comparable to those of the expert group. Conclusion Our education system using the LMC is relatively inexpensive and easy to set up, with a free application for analyses, serving as an effective and ubiquitous educational tool for young surgeons.
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