Laparoscopic surgical skill evaluation with motion capture and eyeglass gaze cameras: A pilot study

Matsumoto, Shiro; Kawahira, Hiroshi; Oiwa, Kosuke; Maeda, Yoshitaka; Nozawa, Akio; Lefor, Alan Kawarai; Hosoya, Yoshinori; Sata, Naohiro

doi:10.1111/ases.13065

Asian J Endoscop Surgery

2022

DOI: 10.1111/ases.13065

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Laparoscopic surgical skill evaluation with motion capture and eyeglass gaze cameras: A pilot study

Shiro Matsumoto

Hiroshi Kawahira

Kosuke Oiwa

et al.

Abstract: Introduction: An eyeglass gaze camera and a skeletal coordinate camera without sensors attached to the operator's body were used to monitor gaze and movement during a simulated surgical procedure. These new devices have the potential to change skill assessment for laparoscopic surgery. The suitability of these devices for skill assessment was investigated.Material and Methods: Six medical students, six intermediate surgeons, and four experts performed suturing tasks in a dry box. The tip positions of the instr… Show more

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Cited by 3 publications

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Autonomous sequential surgical skills assessment for the peg transfer task in a laparoscopic box-trainer system with three cameras

Fathabadi

Grantner²,

Shebrain³

et al. 2023

Robotica

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In laparoscopic surgery, surgeons should develop several manual laparoscopic skills before carrying out real operative procedures using a low-cost box trainer. The Fundamentals of Laparoscopic Surgery (FLS) program was developed as a program to assess fundamental knowledge and surgical skills, required for basic laparoscopic surgery. The peg transfer task is a hands-on exam in the FLS program that assists a trainee to understand the relative minimum amount of grasping force necessary to move the pegs from one place to another place without dropping them. In this paper, an autonomous, sequential assessment algorithm based on deep learning, a multi-object detection method, and, several sequential If-Then conditional statements have been developed to monitor each step of a surgeon’s performance. Images from three different cameras are used to assess whether the surgeon executes the peg transfer task correctly and to display a notification on any errors on the monitor immediately. This algorithm improves the performance of a laparoscopic box-trainer system using top, side, and front cameras and removes the need for any human monitoring during a peg transfer task. The developed algorithm can detect each object and its status during a peg transfer task and notifies the resident about the correct or failed outcome. In addition, this system can correctly determine the peg transfer execution time, and the move, carry, and dropped states for each object by the top, side, and front-mounted cameras. Based on the experimental results, the proposed surgical skill assessment system can identify each object at a high score of fidelity, and the train-validation total loss for the single-shot detector (SSD) ResNet50 v1 was about 0.05. Also, the mean average precision (mAP) and Intersection over Union (IoU) of this detection system were 0.741, and 0.75, respectively. This project is a collaborative research effort between the Department of Electrical and Computer Engineering and the Department of Surgery, at Western Michigan University.

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Autonomous sequential surgical skills assessment for the peg transfer task in a laparoscopic box-trainer system with three cameras

Fathabadi

Grantner²,

Shebrain³

et al. 2023

Robotica

View full text Add to dashboard Cite

show abstract

Laparoscopic distal gastrectomy skill evaluation from video: a new artificial intelligence-based instrument identification system

Matsumoto,

Kawahira,

Fukata

et al. 2024

Sci Rep

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The advent of Artificial Intelligence (AI)-based object detection technology has made identification of position coordinates of surgical instruments from videos possible. This study aimed to find kinematic differences by surgical skill level. An AI algorithm was developed to identify X and Y coordinates of surgical instrument tips accurately from video. Kinematic analysis including fluctuation analysis was performed on 18 laparoscopic distal gastrectomy videos from three expert and three novice surgeons (3 videos/surgeon, 11.6 h, 1,254,010 frames). Analysis showed the expert surgeon cohort moved more efficiently and regularly, with significantly less operation time and total travel distance. Instrument tip movement did not differ in velocity, acceleration, or jerk between skill levels. The evaluation index of fluctuation β was significantly higher in experts. ROC curve cutoff value at 1.4 determined sensitivity and specificity of 77.8% for experts and novices. Despite the small sample, this study suggests AI-based object detection with fluctuation analysis is promising because skill evaluation can be calculated in real time with potential for peri-operational evaluation.

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Development of a System for Determining Technique Level of Vascular Anastomosis Using Hand Motion

Chen,

Shoji,

Kowatari

et al. 2024

JRM

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In off-pump coronary artery bypass grafting (OPCAB), the coronary arteries are joined as the heart beats. This procedure requires high skill and experience to be performed reliably and quickly. Although training kits are commonly used for technical training, the inability of trainees to always be with experienced surgeons for guidance and to receive immediate evaluation remains problematic. To address this problem, a system that allows a single trainee to observe and quantitatively evaluate the procedures performed by an experienced surgeon is being developed. In this study, to analyze the differences between the motions of experienced and novice surgeons, Leap Motion was used to measure the hand motion of the vascular anastomosis performed by both surgeons using a training kit. Using the measured data, the features of the vascular anastomosis surgical techniques performed by experienced and novices were tested using the Mann–Whitney U test. In addition, a binary classification was performed using machine learning with these features. The binary classification results show that trainees can be classified as experts or novices with high accuracy using the developed system.

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Laparoscopic surgical skill evaluation with motion capture and eyeglass gaze cameras: A pilot study

Cited by 3 publications

References 25 publications

Autonomous sequential surgical skills assessment for the peg transfer task in a laparoscopic box-trainer system with three cameras

Autonomous sequential surgical skills assessment for the peg transfer task in a laparoscopic box-trainer system with three cameras

Laparoscopic distal gastrectomy skill evaluation from video: a new artificial intelligence-based instrument identification system

Development of a System for Determining Technique Level of Vascular Anastomosis Using Hand Motion

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