A multi-camera, multi-view system for training and skill assessment for robot-assisted surgery

Natural Language Processing and soft data for motor skill assessment: A case study in surgical training simulations

BACKGROUND AND OBJECTIVE

Automated surgical skill assessment using kinematic and video data (hard data) sources has been widely adopted in the literature. However, experts' opinions (soft data) in the form of free-text could be an invaluable source for evaluating one's skill level since the availability and semantic richness of the soft data are both higher than the hard data. In this paper, the feasibility of using soft data as a single source of skill assessment is analyzed with various Natural Language Processing (NLP) algorithms of different levels of complexity.

METHODS

An experiment named "Vertex Pursuit" was designed to address the absence of a dataset with free-text soft data in synchronization with hard data. This experiment challenges participants' hand-eye coordination, both-hand coordination, precision, and dexterity by tracking haptic device movements along a star pentagon shape. Top-performing participants receive additional training to provide expert feedback through free-text comments evaluating their peers' trials. Traditional machine learning approaches are employed, including various word and sentence embedding techniques combined with a diverse set of classifiers, to assess skill levels based on this soft data. Additionally, encoder-only and decoder-only large language models (LLMs) are applied to the data, with the latter leveraging three prompt engineering techniques.

RESULTS

The task of skill assessment using soft data is demonstrated to be a complex NLP task, and as the complexity of the method increases, the results improve. The top performance was achieved with the decoder-only LLMs and the rule-based prompting strategy.

CONCLUSION

This paper studied the feasibility of using soft data in a simulated surgical skill assessment scenario. While further research is needed, the proposed methods can reduce subjectivity, alleviate the burden on human experts, and enable more widespread, scalable skill evaluation in surgical training programs.

Remote teaching system for robotic surgery and its validation: results of a randomized controlled study

BACKGROUND

Currently, only a limited number of remote assistance modalities are utilized in the basic phase of robotic surgery training to facilitate the rapid acquisition of robotic surgery skills by surgeons. This study aimed to investigate the benefits of real-time remote surgical robotic skill training based on a multi-channel video recording and playback system.

METHODS

We randomly divided 40 medical students without prior expertise in the use of surgical robots into two groups to assess the performance of trainees on a robotic simulator (Mimic dV-Trainer). The remote group received remote training, while the control group received live one-on-one guidance. We compared the learning curves of the two groups based on simulator scores. Furthermore, the NASA task load index (NASA-TLX) scale was used to measure the fatigue load of the trainers.

RESULTS

We observed no significant differences in the demographics or initial baseline skill levels between the two groups. Participants in the remote group achieved higher total scores in the Match Board 2 and Thread the Rings 1 exercises compared to the control group. In addition, trainers in the remote group reported lower subjective fatigue load than in the control group.

CONCLUSIONS

The remote approach to surgical robotics skills training based on the Remote Teaching System for Robotic Surgery (ReTeRoS) is both feasible and has the potential for large-scale training.

A systematic review of video-based educational interventions in robotic surgical training

As robotic surgical procedures become more prevalent in practice, there is a demand for effective and efficient educational strategies in robotic surgery. Video has been used in open and laparoscopic surgery to instruct trainees in the acquisition of operative knowledge and surgical skill. Robotic surgery is an ideal application of video-based technology given the access of video recording directly from the console. This review will present the evidence base for video-based educational tools in robotic surgery to guide the development of future educational interventions using this technology. A systematic review of the literature was performed using the key words "video" "robotic surgery" and "education". From a total of 538 results, 15 full text articles were screened. Inclusion criteria were the presentation of an educational intervention using video and the application of this intervention to robotic surgery. The results of 10 publications are presented in this review. Analysis of the key concepts presented in these publications revealed three themes: video as technology, video as instruction, video as feedback. All studies showed a video-based learning had a positive effect on educational outcomes. There are limited published studies looking specifically at the use of video as an educational intervention in robotic surgical training. Existing studies primarily focus on the use of video as a review tool for skill development. There is scope to expand the use of robotic video as a teaching tool through adaptation of novel technology such as 3D headsets and concepts of cognitive simulation including guided mental imagery and verbalisation.

Digital technologies: An exploratory study of their role in the resilience of healthcare services

Descriptions of resilient performance in healthcare services usually emphasize the role of skills and knowledge of caregivers. At the same time, the human factors discipline often frames digital technologies as sources of brittleness. This paper presents an exploratory investigation of the upside of ten digital technologies derived from Healthcare 4.0 (H4.0) in terms of their perceived contribution to six healthcare services and the four abilities of resilient healthcare: monitor, anticipate, respond, and learn. This contribution was assessed through a multinational survey conducted with 109 experts. Emergency rooms (ERs) and intensive care units (ICUs) stood out as the most benefited by H4.0 technologies. That is consistent with the high complexity of those services, which demand resilient performance. Four H4.0 technologies were top ranked regarding their impacts on the resilience of those services. They are further explored in follow-up interviews with ER and ICU professionals from hospitals in emerging and developed economies to collect examples of applications in their routines.

Applications of Haptic Technology, Virtual Reality, and Artificial Intelligence in Medical Training During the COVID-19 Pandemic

This paper examines how haptic technology, virtual reality, and artificial intelligence help to reduce the physical contact in medical training during the COVID-19 Pandemic. Notably, any mistake made by the trainees during the education process might lead to undesired complications for the patient. Therefore, training of the medical skills to the trainees have always been a challenging issue for the expert surgeons, and this is even more challenging in pandemics. The current method of surgery training needs the novice surgeons to attend some courses, watch some procedure, and conduct their initial operations under the direct supervision of an expert surgeon. Owing to the requirement of physical contact in this method of medical training, the involved people including the novice and expert surgeons confront a potential risk of infection to the virus. This survey paper reviews recent technological breakthroughs along with new areas in which assistive technologies might provide a viable solution to reduce the physical contact in the medical institutes during the COVID-19 pandemic and similar crises.