VR and machine learning: novel pathways in surgical hands-on training

The evolution of simulation-based medical education research: From traditional to virtual simulations

Background

Simulation-based medical education (SBME) is a widely used method in medical education. This study aims to analyze publications on SBME in terms of countries, institutions, journals, authors, and keyword co-occurrence, as well as to identify trends in SBME research.

Methods

We retrieved the Publications on SBME from the Web of Science Core Collection (WoSCC) database from its inception to January 27, 2024. Microsoft Excel 2019, CiteSpace, and VOSviewer were used to identify the distribution of countries, journals, and authors, as well as to determine the research hotspots.

Results

We retrieved a total of 11272 publications from WoSCC. The number of documents published in 2022 was the highest in the last few decades. The USA, the UK, and Canada were three key contributors to this field. The University of Toronto, Stanford University, and Harvard Medical School were the top major institutions with a larger number of publications. Konge, Lars was the most productive author, while McGaghie, William C was the highest cited author. BMC Medical Education has the highest number of publications among journals. The foundational themes of SBME are "Patient simulation," "extending reality," and "surgical skills."

Conclusions

SBME has attracted considerable attention in medical education. The research hotspot is gradually shifting from traditional simulations with real people or mannequins to virtual, digitally-based simulations and online education. Further studies will be conducted to elucidate the mechanisms of SBME. The utilization of SBME will be more rationalized.

AI-powered real-time annotations during urologic surgery: The future of training and quality metrics

INTRODUCTION AND OBJECTIVE

Real-time artificial intelligence (AI) annotation of the surgical field has the potential to automatically extract information from surgical videos, helping to create a robust surgical atlas. This content can be used for surgical education and qualitative initiatives. We demonstrate the first use of AI in urologic robotic surgery to capture live surgical video and annotate key surgical steps and safety milestones in real-time.

SUMMARY BACKGROUND DATA

While AI models possess the capability to generate automated annotations based on a collection of video images, the real-time implementation of such technology in urological robotic surgery to aid surgeon and training staff it is still pending to be studied.

METHODS

We conducted an educational symposium, which broadcasted 2 live procedures, a robotic-assisted radical prostatectomy (RARP) and a robotic-assisted partial nephrectomy (RAPN). A surgical AI platform system (Theator, Palo Alto, CA) generated real-time annotations and identified operative safety milestones. This was achieved through trained algorithms, conventional video recognition, and novel Video Transfer Network technology which captures clips in full context, enabling automatic recognition and surgical mapping in real-time.

RESULTS

Real-time AI annotations for procedure #1, RARP, are found in Table 1. The safety milestone annotations included the apical safety maneuver and deliberate views of structures such as the external iliac vessels and the obturator nerve. Real-time AI annotations for procedure #2, RAPN, are found in Table 1. Safety milestones included deliberate views of structures such as the gonadal vessels and the ureter. AI annotated surgical events included intraoperative ultrasound, temporary clip application and removal, hemostatic powder application, and notable hemorrhage.

CONCLUSIONS

For the first time, surgical intelligence successfully showcased real-time AI annotations of 2 separate urologic robotic procedures during a live telecast. These annotations may provide the technological framework for send automatic notifications to clinical or operational stakeholders. This technology is a first step in real-time intraoperative decision support, leveraging big data to improve the quality of surgical care, potentially improve surgical outcomes, and support training and education.

Artificial Intelligence in Surgical Training for Kidney Cancer: A Systematic Review of the Literature

The prevalence of renal cell carcinoma (RCC) is increasing due to advanced imaging techniques. Surgical resection is the standard treatment, involving complex radical and partial nephrectomy procedures that demand extensive training and planning. Furthermore, artificial intelligence (AI) can potentially aid the training process in the field of kidney cancer. This review explores how artificial intelligence (AI) can create a framework for kidney cancer surgery to address training difficulties. Following PRISMA 2020 criteria, an exhaustive search of PubMed and SCOPUS databases was conducted without any filters or restrictions. Inclusion criteria encompassed original English articles focusing on AI's role in kidney cancer surgical training. On the other hand, all non-original articles and articles published in any language other than English were excluded. Two independent reviewers assessed the articles, with a third party settling any disagreement. Study specifics, AI tools, methodologies, endpoints, and outcomes were extracted by the same authors. The Oxford Center for Evidence-Based Medicine's evidence levels were employed to assess the studies. Out of 468 identified records, 14 eligible studies were selected. Potential AI applications in kidney cancer surgical training include analyzing surgical workflow, annotating instruments, identifying tissues, and 3D reconstruction. AI is capable of appraising surgical skills, including the identification of procedural steps and instrument tracking. While AI and augmented reality (AR) enhance training, challenges persist in real-time tracking and registration. The utilization of AI-driven 3D reconstruction proves beneficial for intraoperative guidance and preoperative preparation. Artificial intelligence (AI) shows potential for advancing surgical training by providing unbiased evaluations, personalized feedback, and enhanced learning processes. Yet challenges such as consistent metric measurement, ethical concerns, and data privacy must be addressed. The integration of AI into kidney cancer surgical training offers solutions to training difficulties and a boost to surgical education. However, to fully harness its potential, additional studies are imperative.

Incorporation of virtual reality in the clinical training of medical students studying esophageal and mediastinal anatomy and surgery

PURPOSE

To analyze the effectiveness of incorporating virtual reality (VR) in lectures on esophageal and mediastinal anatomy and surgical procedures for medical students at Gifu University during clinical training.

METHODS

We divided medical students participating in clinical training, randomly, into two groups of 30 students each: those who received a lecture using 3D images (3D group) and those who received a lecture using VR images (VR group). Four days after the lecture, the students completed a written test to allow us to evaluate their comprehension, and a questionnaire on their opinion of the lectures.

RESULTS

Based on the results of the written test, the VR group achieved better understanding of computed tomography (CT) images (p = 0.0001) and better interpretation of surgical images (p = 0.0163). However, there was no difference in the scores for spatial recognition and general problems. The questionnaire revealed that the VR group became more interested in mediastinal anatomy (p = 0.0165) and surgery (p = 0.0135).

CONCLUSIONS

Our findings suggest that VR enhances the learning process. The lecture incorporating the VR experience was more effective than the traditional lecture for promoting an understanding of CT images and interpretation of surgical images; thus, it enhances the learning experience for medical students studying surgery.

Essentials for Standardising the Undergraduate Urology Curriculum in Europe: Outcomes of a Delphi Consensus from the European School of Urology

Background

The burden of urological diseases is rising as the worldwide population ages. Although specialist urological provision is needed, a large proportion of these conditions will be managed in primary care. The importance of including urology in medical education currently remains unclear.

Objective

To provide recommendations on undergraduate medical education for urology in Europe.

Design, setting, and participants

A three-round Delphi process to reach consensus on standardising the undergraduate urology curriculum in Europe was endorsed by the European School of Urology.

Outcome measurements and statistical analysis

The levels of agreement were set using a nine-point scale according to the GRADE grid: 1–3, disagree; 4–6, uncertain; and 7–9, agree. Consensus was defined as at least 70% of the participants scoring within the same 3-point grouping.

Results and limitations

Overall, consensus was reached for 20 of 34 statements (70.5%) across the three Delphi rounds, with agreement for 75% (n = 15) and disagreement for 25% (n = 5). The following main points were agreed. Urological teaching should be introduced before year 5 of medical school, with at least 20 h of theoretical activities and at least 30 h of practical activities. Urology should be taught as a stand-alone subject rather than combined with another surgical specialty or a nephrology programme. The participants agreed that urology should be taught according to symptoms. A urology programme should include the anatomy and physiology of the urinary tract, and students should know how to clinically assess a urological patient.

Conclusions

Our recommended urology pathway will allow European medical schools to provide a more comprehensive undergraduate urology curriculum. It will also help to improve and maintain standards of urology undergraduate teaching across Europe.

Patient summary

Our survey showed that urology in universities should have, at minimum, time for theoretical and practical activities and should be taught as a stand-alone subject on the basis of symptoms. Students should give feedback to facilitate constant improvement and evolution of the teaching programme.

Skepticism towards advancing VR technology - student acceptance of VR as a teaching and assessment tool in medicine

Objective: The high didactic potential of Virtual Reality (VR) contrasts with the point of view of students that the technology only has a relatively low significance for current and future teaching. This discrepancy was studied in a differentiated manner in order to gear the further development and implementation of VR towards the target group. Methods: From January 2020 to July 2020, medical students (N=318) were asked to watch ten videos online and rate them on the basis of acceptance indicators (e.g., fun and fairness). Using obstetrics as an example, the videos demonstrated five levels of VR technology functionality (e.g., haptic and adaptive feedback), some of which were visionary, in two use scenarios (teaching and the OSCE). The individual and aggregate indicators were compared with non-parametric testing procedures across application scenarios, functional levels and genders. In addition, correlations between the acceptance and the factors of semester, age, computer affinity, and previous VR experience were analyzed. Results: Across all functional levels, VR was more likely to be accepted in the classroom than in the OSCE. Comparisons across functional levels also revealed that the VR ready to be marketed was significantly more accepted than the visionary functions. This skepticism toward advancing VR technology was most pronounced with regard to the vision of autonomous VR examinations and among female students with a low computer affinity. Conclusion: The results suggest that the students' reservations are due to a lack of experience with the VR technology. In order for young physicians to become familiar with the technology and to be able to use it competently in the everyday clinical practice in the future, VR should not only be used as a teaching tool but also be part of the curriculum. Practical examinations using VR, on the other hand, are only recommended once the technology has become established in teaching and has been proven to be reliable.

How the use of the artificial intelligence could improve surgical skills in urology: state of the art and future perspectives

PURPOSE OF REVIEW

As technology advances, surgical training has evolved in parallel over the previous decade. Training is commonly seen as a way to prepare surgeons for their day-to-day work; however, more importantly, it allows for certification of skills to ensure maximum patient safety. This article reviews advances in the use of machine learning and artificial intelligence for improvements of surgical skills in urology.

RECENT FINDINGS

Six studies have been published, which met the inclusion criteria. All articles assessed the application of artificial intelligence in improving surgical training. Different approaches were taken, such as using machine learning to identify and classify suturing gestures, creating automated objective evaluation reports, and determining surgical technical skill levels to predict clinical outcomes. The articles illustrated the continuously growing role of artificial intelligence to address the difficulties currently present in evaluating urological surgical skills.

SUMMARY

Artificial intelligence allows us to efficiently analyze the surmounting data related to surgical training and use it to come to conclusions that normally would require human intelligence. Although these metrics have been shown to predict surgeon expertise and surgical outcomes, evidence is still scarce regarding their ability to directly improve patient outcomes. Considering this, current active research is growing on the topic of deep learning-based computer vision to provide automated metrics needed for real-time surgeon feedback.