Analysis of executional and procedural errors in dry‐lab robotic surgery experiments

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.

COMPASS: a formal framework and aggregate dataset for generalized surgical procedure modeling

PURPOSE

We propose a formal framework for the modeling and segmentation of minimally invasive surgical tasks using a unified set of motion primitives (MPs) to enable more objective labeling and the aggregation of different datasets.

METHODS

We model dry-lab surgical tasks as finite state machines, representing how the execution of MPs as the basic surgical actions results in the change of surgical context, which characterizes the physical interactions among tools and objects in the surgical environment. We develop methods for labeling surgical context based on video data and for automatic translation of context to MP labels. We then use our framework to create the COntext and Motion Primitive Aggregate Surgical Set (COMPASS), including six dry-lab surgical tasks from three publicly available datasets (JIGSAWS, DESK, and ROSMA), with kinematic and video data and context and MP labels.

RESULTS

Our context labeling method achieves near-perfect agreement between consensus labels from crowd-sourcing and expert surgeons. Segmentation of tasks to MPs results in the creation of the COMPASS dataset that nearly triples the amount of data for modeling and analysis and enables the generation of separate transcripts for the left and right tools.

CONCLUSION

The proposed framework results in high quality labeling of surgical data based on context and fine-grained MPs. Modeling surgical tasks with MPs enables the aggregation of different datasets and the separate analysis of left and right hands for bimanual coordination assessment. Our formal framework and aggregate dataset can support the development of explainable and multi-granularity models for improved surgical process analysis, skill assessment, error detection, and autonomy.

Transfer of acquired practical skills from dry lab into live surgery using the avatera robotic system: An experimental study

OBJECTIVE

To evaluate the transfer of the practical skills of robot-assisted surgery acquired in the dry-lab into a real live experimental setting for performing upper and lower urinary tract surgeries.

MATERIAL AND METHODS

An in vivo experimental study design was utilized. Six urology trainees and fellows; two 2nd year trainees with no previous exposure to laparoscopic surgery (Group 1), two 4th year residents with medium exposure to laparoscopic surgery (Group 2) and two fellows trained to perform laparoscopic surgeries (Group 3) performed ureteral reimplantation into the bladder, pyeloplasty, and radical nephrectomy on three female pigs under general anesthesia. Prior to performing the requested procedures, each participant completed 10-14 h dry-lab robotic training acquiring skills in basic surgical tasks, such as suturing, cutting and needle passage. The recorded variables were the successful completion of the procedures, the console time, and the time to perform different steps and major complications.

RESULTS

All procedures were completed successfully by all groups except the pyeloplasty by group 1 which was complicated by bleeding from the renal vein, and the procedure was abandoned. Group 3 achieved shorter console time for all successfully completed procedures and for separate surgical steps compared to all groups, followed by Group 2. The slowest group for all procedures and steps analyzed was Group 3.

CONCLUSIONS

Although further clinical evidence is needed, the robotic-assisted urological procedures and the most challenging steps could be performed safely and effectively after proper training in the dry lab under mentor supervision according to our study.

Artificial intelligence and automation in endoscopy and surgery

Modern endoscopy relies on digital technology, from high-resolution imaging sensors and displays to electronics connecting configurable illumination and actuation systems for robotic articulation. In addition to enabling more effective diagnostic and therapeutic interventions, the digitization of the procedural toolset enables video data capture of the internal human anatomy at unprecedented levels. Interventional video data encapsulate functional and structural information about a patient's anatomy as well as events, activity and action logs about the surgical process. This detailed but difficult-to-interpret record from endoscopic procedures can be linked to preoperative and postoperative records or patient imaging information. Rapid advances in artificial intelligence, especially in supervised deep learning, can utilize data from endoscopic procedures to develop systems for assisting procedures leading to computer-assisted interventions that can enable better navigation during procedures, automation of image interpretation and robotically assisted tool manipulation. In this Perspective, we summarize state-of-the-art artificial intelligence for computer-assisted interventions in gastroenterology and surgery.