Assessment of Minimally Invasive Suturing Skills: Is Instrument Tracking an Accurate Prediction?

Exploring the Role of Artificial Intelligence (AI)-Driven Training in Laparoscopic Suturing: A Systematic Review of Skills Mastery, Retention, and Clinical Performance in Surgical Education

Background: Artificial Intelligence (AI)-driven training systems are becoming increasingly important in surgical education, particularly in the context of laparoscopic suturing. This systematic review aims to assess the impact of AI on skill acquisition, long-term retention, and clinical performance, with a specific focus on the types of machine learning (ML) techniques applied to laparoscopic suturing training and their associated advantages and limitations. Methods: A comprehensive search was conducted across multiple databases, including PubMed, IEEE Xplore, Cochrane Library, and ScienceDirect, for studies published between 2005 and 2024. Following the PRISMA guidelines, 1200 articles were initially screened, and 33 studies met the inclusion criteria. This review specifically focuses on ML techniques such as deep learning, motion capture, and video segmentation and their application in laparoscopic suturing training. The quality of the included studies was assessed, considering factors such as sample size, follow-up duration, and potential biases. Results: AI-based training systems have shown notable improvements in the laparoscopic suturing process, offering clear advantages over traditional methods. These systems enhance precision, efficiency, and long-term retention of key suturing skills. The use of personalized feedback and real-time performance tracking allows learners to gain proficiency more rapidly and ensures that skills are retained over time. These technologies are particularly beneficial for novice surgeons and provide valuable support in resource-limited settings, where access to expert instructors and advanced equipment may be scarce. Key machine learning techniques, including deep learning, motion capture, and video segmentation, have significantly improved specific suturing tasks, such as needle manipulation, insertion techniques, knot tying, and grip control, all of which are critical to mastering laparoscopic suturing. Conclusions: AI-driven training tools are reshaping laparoscopic suturing education by improving skill acquisition, providing real-time feedback, and enhancing long-term retention. Deep learning, motion capture, and video segmentation techniques have proven most effective in refining suturing tasks such as needle manipulation and knot tying. While AI offers significant advantages, limitations in accuracy, scalability, and integration remain. Further research, particularly large-scale, high-quality studies, is necessary to refine these tools and ensure their effective implementation in real-world clinical settings.

Role of surgical simulation on self-reported confidence level on cardiothoracic surgical trainees

BACKGROUND

Simulated self-practice using simulation models could improve fine motor skills and self confidence in surgical trainees.

AIMS

The purpose of this study is to evaluate on self-reported confidence level in cardiothoracic surgical trainees by using surgical simulation models.

METHODS

We conducted a cross-sectional study on all surgeons (n=10) involved in MIS simulation training. All surgeons are required to perform on three minimally invasive surgery (MIS) procedures (Mitral Valve Repair, Mitral Valve Replacement and Aortic Valve Replacement). A questionnaire was designed based on two existing scales related to self-confidence, the surgical self-efficacy scale [SSES] and the perceived competency scale [PCS]. We assessed their self-confidence (before and after training) in the use of simulation in MIS procedures using rating scales 1-5. The mean score was calculated for each domain and used as the predictor variable. We also developed six questions (PCS) using Objective Structured Assessment of Technical Skills (OSAT) related to each domain and asked participants how confident they were after performing each MICS procedure.

RESULTS

The mean score was 4.7 for all assessed domains, except "knowledge" (3.8). Surgeons who had performed one or more MIS procedures had higher scores (P<0.05). There was no correlation between the number of MIS procedures performed and self-confidence scores.

CONCLUSIONS

The results indicate that the cardiac surgery training based on MIS simulation improves trainees and consultants in terms of the level of self-confidence. Although surgeons generally have high levels of self-confidence after simulation training in MIS cardiac procedures, there is still room for improvement with respect to technical skills related to the procedure itself and its results.

Experts in Minimally Invasive Surgery are Outperformed by Trained Novices on Suturing Skills

INTRODUCTION

Learning minimally invasive suturing can be challenging, creating a barrier to further implementation, especially with the development of easier methods. Nevertheless, mastering intracorporeal knot tying is crucial when alternative techniques prove inadequate. Therefore, the minimally invasive surgery (MIS) suturing skills of MIS experts are compared with a group of novices during their learning curve on a simulator.

METHODS

The novice participants repeatedly performed the intracorporeal suturing task on the EoSim MIS simulator (up to a maximum of 20 repetitions). The experts (>50 MIS procedures and advanced MIS experience) completed the same task once. The first and last exercises of the novices and the expert tasks were all blindly recorded and assessed by two independent assessors using the Laparoscopic Suturing Competency Assessment Tool (LS-CAT). Additionally, objective assessment parameters, "time" and "distance", using instrument tracking, were collected. The scores of the experts were then compared with the novices.

RESULTS

At the end of the training, novices significantly outperformed the experts on both the expert assessment (LS-CAT: 16.8 versus 26.8, P = 0.001) and objective parameters (median time: 190 s versus 161 s, P < 0.001; median distance: 6.1 m versus 3.6 m, P < 0.001). Although the experts showed slightly better performance than the novices during their first task, the difference was not significant on the expert assessment (LS-CAT experts 16.8, novices 20.5, P = 0.057).

CONCLUSIONS

Our findings underscore the significance of continued MIS suturing training for both residents and surgeons. In this study, trained novices demonstrated a significant outperformance of experts on both quantitative and qualitative outcome parameters within a simulated setting.

Simulation training in urology

PURPOSE OF REVIEW

This review outlines recent innovations in simulation technology as it applies to urology. It is essential for the next generation of urologists to attain a solid foundation of technical and nontechnical skills, and simulation technology provides a variety of safe, controlled environments to acquire this baseline knowledge.

RECENT FINDINGS

With a focus on urology, this review first outlines the evidence to support surgical simulation, then discusses the strides being made in the development of 3D-printed models for surgical skill training and preoperative planning, virtual reality models for different urologic procedures, surgical skill assessment for simulation, and integration of simulation into urology residency curricula.

SUMMARY

Simulation continues to be an integral part of the journey towards the mastery of skills necessary for becoming an expert urologist. Clinicians and researchers should consider how to further incorporate simulation technology into residency training and help future generations of urologists throughout their career.

Automatic performance evaluation of the intracorporeal suture exercise

PURPOSE

This work uses deep learning algorithms to provide automated feedback on the suture with intracorporeal knot exercise in the fundamentals of laparoscopic surgery simulator. Different metrics were designed to provide informative feedback to the user on how to complete the task more efficiently. The automation of the feedback will allow students to practice at any time without the supervision of experts.

METHODS

Five residents and five senior surgeons participated in the study. Object detection, image classification, and semantic segmentation deep learning algorithms were used to collect statistics on the practitioner's performance. Three task-specific metrics were defined. The metrics refer to the way the practitioner holds the needle before the insertion to the Penrose drain, and the amount of movement of the Penrose drain during the needle's insertion.

RESULTS

Good agreement between the human labeling and the different algorithms' performance and metric values was achieved. The difference between the scores of the senior surgeons and the surgical residents was statistically significant for one of the metrics.

CONCLUSION

We developed a system that provides performance metrics of the intracorporeal suture exercise. These metrics can help surgical residents practice independently and receive informative feedback on how they entered the needle into the Penrose.

Comparing Simulator Metrics and Rater Assessment of Laparoscopic Suturing Skills

BACKGROUND

Laparoscopic intracorporeal suturing is important to master and competence should be ensured using an optimal method in a simulated environment before proceeding to real operations. The objectives of this study were to gather validity evidence for two tools for assessing laparoscopic intracorporeal knot tying and compare the rater-based assessment of laparoscopic intracorporeal suturing with the assessment based on simulator metrics.

METHODS

Twenty-eight novices and 19 experienced surgeons performed four laparoscopic sutures on a Simball Box simulator twice. Two surgeons used the Intracorporeal Suturing Assessment Tool (ISAT) for blinded video rating.

RESULTS

Composite Simulator Score (CSS) had higher test-retest reliability than the ISAT. The correlation between the number performed procedures including suturing and ISAT score was 0.51, p<0.001, and 0.59 p<0.001 for CSS. We found an inter-rater reliability (0.72, p<0.001 for test 1 and 0.53 p<0.001 for test 2). The pass/fail rates for ISAT and CSS were similar.

CONCLUSION

CSS and ISAT provide similar results for assessing laparoscopic suturing but assess different aspects of performance. Using simulator metrics and raters' assessments in combination should be considered for a more comprehensive evaluation of laparoscopic knot-tying competency.