Evaluation of objective tools and artificial intelligence in robotic surgery technical skills assessment: a systematic review

BACKGROUND: There is a need to standardize training in robotic surgery, including objective assessment for accreditation. This systematic review aimed to identify objective tools for technical skills assessment, providing evaluation statuses to guide research and inform implementation into training curricula. METHODS: A systematic literature search was conducted in accordance with the PRISMA guidelines. Ovid Embase/Medline, PubMed and Web of Science were searched. Inclusion criterion: robotic surgery technical skills tools. Exclusion criteria: non-technical, laparoscopy or open skills only. Manual tools and automated performance metrics (APMs) were analysed using Messick's concept of validity and the Oxford Centre of Evidence-Based Medicine (OCEBM) Levels of Evidence and Recommendation (LoR). A bespoke tool analysed artificial intelligence (AI) studies. The Modified Downs-Black checklist was used to assess risk of bias. RESULTS: Two hundred and forty-seven studies were analysed, identifying: 8 global rating scales, 26 procedure-/task-specific tools, 3 main error-based methods, 10 simulators, 28 studies analysing APMs and 53 AI studies. Global Evaluative Assessment of Robotic Skills and the da Vinci Skills Simulator were the most evaluated tools at LoR 1 (OCEBM). Three procedure-specific tools, 3 error-based methods and 1 non-simulator APMs reached LoR 2. AI models estimated outcomes (skill or clinical), demonstrating superior accuracy rates in the laboratory with 60 per cent of methods reporting accuracies over 90 per cent, compared to real surgery ranging from 67 to 100 per cent. CONCLUSIONS: Manual and automated assessment tools for robotic surgery are not well validated and require further evaluation before use in accreditation processes.PROSPERO: registration ID CRD42022304901

Preparing for the future of cardiothoracic surgery with virtual reality simulation and surgical planning:a narrative review

Background and Objective: Virtual reality (VR) technology in cardiothoracic surgery has been an area of interest for almost three decades, but computational limitations had restricted its implementation. Recent advances in computing power have facilitated the creation of high-fidelity VR simulations and anatomy visualisation tools. We undertook a non-systematic narrative review of literature on VR simulations and preoperative planning tools in cardiothoracic surgery and present the state-of-the-art, and a future outlook. Methods: A comprehensive search through MEDLINE database was performed in November 2022 for all publications that describe the use of VR in cardiothoracic surgery regarding training purposes, education, simulation, and procedural planning. We excluded papers that were not in English or Dutch, and that used two-dimensional (2D) screens, augmented, and simulated reality. Key Content and Findings: Results were categorised as simulators and preoperative planning tools. Current surgical simulators include the lobectomy module in the LapSim for video assisted thorascopic surgery which has been extensively validated, and the more recent robotic assisted lobectomy simulators from Robotix Mentor and Da Vinci SimNow, which are increasingly becoming integrated into the robotic surgery curriculum. Other perioperative simulators include the CardioPulmonary VR Resuscitation simulator for advanced life support after cardiac surgery, and the VR Extracorporeal Circulation (ECC) simulator for perfusionists to simulate the use of a heart-lung machine (HLM). For surgical planning, there are many small-scale tools available, and many case/pilot studies have been published utilising the visualisation possibilities provided by VR, including congenital cardiac, congenital thoracic, adult cardiac, and adult thoracic diseases. Conclusions: There are many promising tools becoming available to leverage the immersive power of VR in cardiothoracic surgery. The path to validate these simulators is well described, but large-scale trials producing high-level evidence for their efficacy are absent as of yet. Our view is that these tools will become increasingly integral parts of daily practice in this field in the coming decade.</p

A Virtual-Based Haptic Endoscopic Sinus Surgery (ESS) Training System: from Development to Validation

Simulated training platforms offer a suitable avenue for surgical students and professionals to build and improve upon their skills, without the hassle of traditional training methods. To enhance the degree of realistic interaction paradigms of training simulators, great work has been done to both model simulated anatomy in more realistic fashion, as well as providing appropriate haptic feedback to the trainee. As such, this chapter seeks to discuss the ongoing research being conducted on haptic feedback-incorporated simulators specifically for Endoscopic Sinus Surgery (ESS). This chapter offers a brief comparative analysis of some EES simulators, in addition to a deeper quantitative and qualitative look into our approach to designing and prototyping a complete virtual-based haptic EES training platform

An Overview of Self-Adaptive Technologies Within Virtual Reality Training

This overview presents the current state-of-the-art of self-adaptive technologies within virtual reality (VR) training. Virtual reality training and assessment is increasingly used for five key areas: medical, industrial & commercial training, serious games, rehabilitation and remote training such as Massive Open Online Courses (MOOCs). Adaptation can be applied to five core technologies of VR including haptic devices, stereo graphics, adaptive content, assessment and autonomous agents. Automation of VR training can contribute to automation of actual procedures including remote and robotic assisted surgery which reduces injury and improves accuracy of the procedure. Automated haptic interaction can enable tele-presence and virtual artefact tactile interaction from either remote or simulated environments. Automation, machine learning and data driven features play an important role in providing trainee-specific individual adaptive training content. Data from trainee assessment can form an input to autonomous systems for customised training and automated difficulty levels to match individual requirements. Self-adaptive technology has been developed previously within individual technologies of VR training. One of the conclusions of this research is that while it does not exist, an enhanced portable framework is needed and it would be beneficial to combine automation of core technologies, producing a reusable automation framework for VR training

Haptics in Robot-Assisted Surgery: Challenges and Benefits

Robotic surgery is transforming the current surgical practice, not only by improving the conventional surgical methods but also by introducing innovative robot-enhanced approaches that broaden the capabilities of clinicians. Being mainly of man-machine collaborative type, surgical robots are seen as media that transfer pre- and intra-operative information to the operator and reproduce his/her motion, with appropriate filtering, scaling, or limitation, to physically interact with the patient. The field, however, is far from maturity and, more critically, is still a subject of controversy in medical communities. Limited or absent haptic feedback is reputed to be among reasons that impede further spread of surgical robots. In this paper objectives and challenges of deploying haptic technologies in surgical robotics is discussed and a systematic review is performed on works that have studied the effects of providing haptic information to the users in major branches of robotic surgery. It has been tried to encompass both classical works and the state of the art approaches, aiming at delivering a comprehensive and balanced survey both for researchers starting their work in this field and for the experts

Technical skill assessment in minimally invasive surgery using artificial intelligence: a systematic review.

BACKGROUND Technical skill assessment in surgery relies on expert opinion. Therefore, it is time-consuming, costly, and often lacks objectivity. Analysis of intraoperative data by artificial intelligence (AI) has the potential for automated technical skill assessment. The aim of this systematic review was to analyze the performance, external validity, and generalizability of AI models for technical skill assessment in minimally invasive surgery. METHODS A systematic search of Medline, Embase, Web of Science, and IEEE Xplore was performed to identify original articles reporting the use of AI in the assessment of technical skill in minimally invasive surgery. Risk of bias (RoB) and quality of the included studies were analyzed according to Quality Assessment of Diagnostic Accuracy Studies criteria and the modified Joanna Briggs Institute checklists, respectively. Findings were reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement. RESULTS In total, 1958 articles were identified, 50 articles met eligibility criteria and were analyzed. Motion data extracted from surgical videos (n = 25) or kinematic data from robotic systems or sensors (n = 22) were the most frequent input data for AI. Most studies used deep learning (n = 34) and predicted technical skills using an ordinal assessment scale (n = 36) with good accuracies in simulated settings. However, all proposed models were in development stage, only 4 studies were externally validated and 8 showed a low RoB. CONCLUSION AI showed good performance in technical skill assessment in minimally invasive surgery. However, models often lacked external validity and generalizability. Therefore, models should be benchmarked using predefined performance metrics and tested in clinical implementation studies

Development and Validation of a Hybrid Virtual/Physical Nuss Procedure Surgical Trainer

With continuous advancements and adoption of minimally invasive surgery, proficiency with nontrivial surgical skills involved is becoming a greater concern. Consequently, the use of surgical simulation has been increasingly embraced by many for training and skill transfer purposes. Some systems utilize haptic feedback within a high-fidelity anatomically-correct virtual environment whereas others use manikins, synthetic components, or box trainers to mimic primary components of a corresponding procedure. Surgical simulation development for some minimally invasive procedures is still, however, suboptimal or otherwise embryonic. This is true for the Nuss procedure, which is a minimally invasive surgery for correcting pectus excavatum (PE) – a congenital chest wall deformity. This work aims to address this gap by exploring the challenges of developing both a purely virtual and a purely physical simulation platform of the Nuss procedure and their implications in a training context. This work then describes the development of a hybrid mixed-reality system that integrates virtual and physical constituents as well as an augmentation of the haptic interface, to carry out a reproduction of the primary steps of the Nuss procedure and satisfy clinically relevant prerequisites for its training platform. Furthermore, this work carries out a user study to investigate the system’s face, content, and construct validity to establish its faithfulness as a training platform

25th International Congress of the European Association for Endoscopic Surgery (EAES) Frankfurt, Germany, 14-17 June 2017 : Oral Presentations

Introduction: Ouyang has recently proposed hiatal surface area (HSA) calculation by multiplanar multislice computer tomography (MDCT) scan as a useful tool for planning treatment of hiatus defects with hiatal hernia (HH), with or without gastroesophageal reflux (MRGE). Preoperative upper endoscopy or barium swallow cannot predict the HSA and pillars conditions. Aim to asses the efficacy of MDCT’s calculation of HSA for planning the best approach for the hiatal defects treatment. Methods: We retrospectively analyzed 25 patients, candidates to laparoscopic antireflux surgery as primary surgery or hiatus repair concomitant with or after bariatric surgery. Patients were analyzed preoperatively and after one-year follow-up by MDCT scan measurement of esophageal hiatus surface. Five normal patients were enrolled as control group. The HSA’s intraoperative calculation was performed after complete dissection of the area considered a triangle. Postoperative CT-scan was done after 12 months or any time reflux symptoms appeared. Results: (1) Mean HSA in control patients with no HH, no MRGE was cm2 and similar in non-complicated patients with previous LSG and cruroplasty. (2) Mean HSA in patients candidates to cruroplasty was 7.40 cm2. (3) Mean HSA in patients candidates to redo cruroplasty for recurrence was 10.11 cm2. Discussion. MDCT scan offer the possibility to obtain an objective measurement of the HSA and the correlation with endoscopic findings and symptoms. The preoperative information allow to discuss with patients the proper technique when a HSA[5 cm2 is detected. During the follow-up a correlation between symptoms and failure of cruroplasty can be assessed. Conclusions: MDCT scan seems to be an effective non-invasive method to plan hiatal defect treatment and to check during the follow-up the potential recurrence. Future research should correlate in larger series imaging data with intraoperative findings