Virtual reality simulation training improve diagnostic knee arthroscopy and meniscectomy skills: a prospective transfer validity study

PURPOSE

Limited data exist on the actual transfer of skills learned using a virtual reality (VR) simulator for arthroscopy training because studies mainly focused on VR performance improvement and not on transfer to real word (transfer validity). The purpose of this single-blinded, controlled trial was to objectively investigate transfer validity in the context of initial knee arthroscopy training.

METHODS

For this study, 36 junior resident orthopaedic surgeons (postgraduate year one and year two) without prior experience in arthroscopic surgery were enrolled to receive standard knee arthroscopy surgery training (NON-VR group) or standard training plus training on a hybrid virtual reality knee arthroscopy simulator (1 h/month) (VR group). At inclusion, all participants completed a questionnaire on their current arthroscopic technical skills. After 6 months of training, both groups performed three exercises that were evaluated independently by two blinded trainers: i) arthroscopic partial meniscectomy on a bench-top knee simulator; ii) supervised diagnostic knee arthroscopy on a cadaveric knee; and iii) supervised knee partial meniscectomy on a cadaveric knee. Training level was determined with the Arthroscopic Surgical Skill Evaluation Tool (ASSET) score.

RESULTS

Overall, performance (ASSET scores) was better in the VR group than NON-VR group (difference in the global scores: p < 0.001, in bench-top meniscectomy scores: p = 0.03, in diagnostic knee arthroscopy on a cadaveric knee scores: p = 0.04, and in partial meniscectomy on a cadaveric knee scores: p = 0.02). Subgroup analysis by postgraduate year showed that the year-one NON-VR subgroup performed worse than the other subgroups, regardless of the exercise.

CONCLUSION

This study showed the transferability of the technical skills acquired by novice residents on a hybrid virtual reality simulator to the bench-top and cadaveric models. Surgical skill acquired with a VR arthroscopy surgical simulator might safely improve arthroscopy competences in the operating room, also helping to standardise resident training and follow their progress.

LEVEL OF EVIDENCE: 2

Gaze behavior is related to objective technical skills assessment during virtual reality simulator-based surgical training: a proof of concept

PURPOSE

Simulation-based training allows surgical skills to be learned safely. Most virtual reality-based surgical simulators address technical skills without considering non-technical skills, such as gaze use. In this study, we investigated surgeons' visual behavior during virtual reality-based surgical training where visual guidance is provided. Our hypothesis was that the gaze distribution in the environment is correlated with the simulator's technical skills assessment.

METHODS

We recorded 25 surgical training sessions on an arthroscopic simulator. Trainees were equipped with a head-mounted eye-tracking device. A U-net was trained on two sessions to segment three simulator-specific areas of interest (AoI) and the background, to quantify gaze distribution. We tested whether the percentage of gazes in those areas was correlated with the simulator's scores.

RESULTS

The neural network was able to segment all AoI with a mean Intersection over Union superior to 94% for each area. The gaze percentage in the AoI differed among trainees. Despite several sources of data loss, we found significant correlations between gaze position and the simulator scores. For instance, trainees obtained better procedural scores when their gaze focused on the virtual assistance (Spearman correlation test, N = 7, r = 0.800, p = 0.031).

CONCLUSION

Our findings suggest that visual behavior should be quantified for assessing surgical expertise in simulation-based training environments, especially when visual guidance is provided. Ultimately visual behavior could be used to quantitatively assess surgeons' learning curve and expertise while training on VR simulators, in a way that complements existing metrics.

PEg TRAnsfer Workflow recognition challenge report: Do multimodal data improve recognition?

BACKGROUND AND OBJECTIVE

In order to be context-aware, computer-assisted surgical systems require accurate, real-time automatic surgical workflow recognition. In the past several years, surgical video has been the most commonly-used modality for surgical workflow recognition. But with the democratization of robot-assisted surgery, new modalities, such as kinematics, are now accessible. Some previous methods use these new modalities as input for their models, but their added value has rarely been studied. This paper presents the design and results of the "PEg TRAnsfer Workflow recognition" (PETRAW) challenge with the objective of developing surgical workflow recognition methods based on one or more modalities and studying their added value.

METHODS

The PETRAW challenge included a data set of 150 peg transfer sequences performed on a virtual simulator. This data set included videos, kinematic data, semantic segmentation data, and annotations, which described the workflow at three levels of granularity: phase, step, and activity. Five tasks were proposed to the participants: three were related to the recognition at all granularities simultaneously using a single modality, and two addressed the recognition using multiple modalities. The mean application-dependent balanced accuracy (AD-Accuracy) was used as an evaluation metric to take into account class balance and is more clinically relevant than a frame-by-frame score.

RESULTS

Seven teams participated in at least one task with four participating in every task. The best results were obtained by combining video and kinematic data (AD-Accuracy of between 93% and 90% for the four teams that participated in all tasks).

CONCLUSION

The improvement of surgical workflow recognition methods using multiple modalities compared with unimodal methods was significant for all teams. However, the longer execution time required for video/kinematic-based methods(compared to only kinematic-based methods) must be considered. Indeed, one must ask if it is wise to increase computing time by 2000 to 20,000% only to increase accuracy by 3%. The PETRAW data set is publicly available at www.synapse.org/PETRAW to encourage further research in surgical workflow recognition.

Validation of virtual reality arthroscopy simulator relevance in characterising experienced surgeons

BACKGROUND

Virtual reality (VR) simulation is particularly suitable for learning arthroscopy skills. Despite significant research, one drawback often outlined is the difficulty in distinguishing performance levels (Construct Validity) in experienced surgeons. Therefore, it seems adequate to search new methods of performance measurements using probe trajectories instead of commonly used metrics.

HYPOTHESIS

It was hypothesized that a larger experience in surgical shoulder arthroscopy would be correlated with better performance on a VR shoulder arthroscopy simulator and that experienced operators would share similar probe trajectories.

MATERIALS & METHODS

After answering to standardized questionnaires, 104 trajectories from 52 surgeons divided into 2 cohorts (26 intermediates and 26 experts) were recorded on a shoulder arthroscopy simulator. The procedure analysed was the "loose body removal" in a right shoulder joint. 10 metrics were computed on the trajectories including procedure duration, overall path length, economy of motion and smoothness. Additionally, Dynamic Time Warping (DTW) was computed on the trajectories for unsupervised hierarchical clustering of the surgeons.

RESULTS

Experts were significantly faster (Median 70.9s Interquartile range [56.4-86.3] vs. 116.1s [82.8-154.2], p<0.01), more fluid (4.6.10⁵mm.s^-3 [3.1.10⁵-7.2.10⁵] vs. 1.5.10⁶mm.s^-3 [2.6.10⁶-3.5.10⁶], p=0.05), and economical in their motion (19.3mm² [9.1-25.9] vs. 33.8mm² [14.8-50.5], p<0.01), but there was no significant difference in performance for path length (671.4mm [503.8-846.1] vs 694.6mm [467.0-1090.1], p=0.62). The DTW clustering differentiates two expertise related groups of trajectories with performance similarities, respectively including 48 expert trajectories for the first group and 52 intermediates and 4 expert trajectories for the second group (Sensitivity of 92%, Specificity of 100%). Hierarchical clustering with DTW significantly identified expert operators from intermediate operators and found trajectory similarities among 24/26 experts.

CONCLUSION

This study demonstrated the Construct Validity of the VR shoulder arthroscopy simulator within groups of experienced surgeons. With new types of metrics simply based on the simulator's raw trajectories, it was possible to significantly distinguish levels of expertise. We demonstrated that clustering analysis with Dynamic Time Warping was able to reliably discriminate between expert operators and intermediate operators.

CLINICAL RELEVANCE

The results have implications for the future of arthroscopic surgical training or post-graduate accreditation programs using virtual reality simulation.

LEVEL OF EVIDENCE

III; prospective comparative study.

MIcro-surgical anastomose workflow recognition challenge report

BACKGROUND AND OBJECTIVE

Automatic surgical workflow recognition is an essential step in developing context-aware computer-assisted surgical systems. Video recordings of surgeries are becoming widely accessible, as the operational field view is captured during laparoscopic surgeries. Head and ceiling mounted cameras are also increasingly being used to record videos in open surgeries. This makes videos a common choice in surgical workflow recognition. Additional modalities, such as kinematic data captured during robot-assisted surgeries, could also improve workflow recognition. This paper presents the design and results of the MIcro-Surgical Anastomose Workflow recognition on training sessions (MISAW) challenge whose objective was to develop workflow recognition models based on kinematic data and/or videos.

METHODS

The MISAW challenge provided a data set of 27 sequences of micro-surgical anastomosis on artificial blood vessels. This data set was composed of videos, kinematics, and workflow annotations. The latter described the sequences at three different granularity levels: phase, step, and activity. Four tasks were proposed to the participants: three of them were related to the recognition of surgical workflow at three different granularity levels, while the last one addressed the recognition of all granularity levels in the same model. We used the average application-dependent balanced accuracy (AD-Accuracy) as the evaluation metric. This takes unbalanced classes into account and it is more clinically relevant than a frame-by-frame score.

RESULTS

Six teams participated in at least one task. All models employed deep learning models, such as convolutional neural networks (CNN), recurrent neural networks (RNN), or a combination of both. The best models achieved accuracy above 95%, 80%, 60%, and 75% respectively for recognition of phases, steps, activities, and multi-granularity. The RNN-based models outperformed the CNN-based ones as well as the dedicated modality models compared to the multi-granularity except for activity recognition.

CONCLUSION

For high levels of granularity, the best models had a recognition rate that may be sufficient for applications such as prediction of remaining surgical time. However, for activities, the recognition rate was still low for applications that can be employed clinically. The MISAW data set is publicly available at http://www.synapse.org/MISAW to encourage further research in surgical workflow recognition.

Automatic annotation of surgical activities using virtual reality environments

PURPOSE

Annotation of surgical activities becomes increasingly important for many recent applications such as surgical workflow analysis, surgical situation awareness, and the design of the operating room of the future, especially to train machine learning methods in order to develop intelligent assistance. Currently, annotation is mostly performed by observers with medical background and is incredibly costly and time-consuming, creating a major bottleneck for the above-mentioned technologies. In this paper, we propose a way to eliminate, or at least limit, the human intervention in the annotation process.

METHODS

Meaningful information about interaction between objects is inherently available in virtual reality environments. We propose a strategy to convert automatically this information into annotations in order to provide as output individual surgical process models.

VALIDATION

We implemented our approach through a peg-transfer task simulator and compared it to manual annotations. To assess the impact of our contribution, we studied both intra- and inter-observer variability.

RESULTS AND CONCLUSION

In average, manual annotations took more than 12 min for 1 min of video to achieve low-level physical activity annotation, whereas automatic annotation is achieved in less than a second for the same video period. We also demonstrated that manual annotation introduced mistakes as well as intra- and inter-observer variability that our method is able to suppress due to the high precision and reproducibility.

Assessment of surgical skills by using surgical navigation in robot-assisted partial nephrectomy

PURPOSE

To assess surgical skills in robot-assisted partial nephrectomy (RAPN) with and without surgical navigation (SN).

METHODS

We employed an SN system that synchronizes the real-time endoscopic image with a virtual reality three-dimensional (3D) model for RAPN and evaluated the skills of two expert surgeons with regard to the identification and dissection of the renal artery (non-SN group, n = 21 [first surgeon n = 9, second surgeon n = 12]; SN group, n = 32 [first surgeon n = 11, second surgeon n = 21]). We converted all movements of the robotic forceps during RAPN into a dedicated vocabulary. Using RAPN videos, we classified all movements of the robotic forceps into direct action (defined as movements of the robotic forceps that directly affect tissues) and connected motion (defined as movements that link actions). In addition, we analyzed the frequency, duration, and occupancy rate of the connected motion.

RESULTS

In the SN group, the R.E.N.A.L nephrometry score was lower (7 vs. 6, P = 0.019) and the time to identify and dissect the renal artery (16 vs. 9 min, P = 0.008) was significantly shorter. The connected motions of inefficient "insert," "pull," and "rotate" motions were significantly improved by SN. SN significantly improved the frequency, duration, and occupancy rate of connected motions of the right hand of the first surgeon and of both hands of the second surgeon. The improvements in connected motions were positively associated with SN for both surgeons.

CONCLUSION

This is the first study to investigate SN for nephron-sparing surgery. SN with 3D models might help improve the connected motions of expert surgeons to ensure efficient RAPN.

Surgical motion analysis using discriminative interpretable patterns

OBJECTIVE

The analysis of surgical motion has received a growing interest with the development of devices allowing their automatic capture. In this context, the use of advanced surgical training systems makes an automated assessment of surgical trainee possible. Automatic and quantitative evaluation of surgical skills is a very important step in improving surgical patient care.

MATERIAL AND METHOD

In this paper, we present an approach for the discovery and ranking of discriminative and interpretable patterns of surgical practice from recordings of surgical motions. A pattern is defined as a series of actions or events in the kinematic data that together are distinctive of a specific gesture or skill level. Our approach is based on the decomposition of continuous kinematic data into a set of overlapping gestures represented by strings (bag of words) for which we compute comparative numerical statistic (tf-idf) enabling the discriminative gesture discovery via its relative occurrence frequency.

RESULTS

We carried out experiments on three surgical motion datasets. The results show that the patterns identified by the proposed method can be used to accurately classify individual gestures, skill levels and surgical interfaces. We also present how the patterns provide a detailed feedback on the trainee skill assessment.

CONCLUSIONS

The proposed approach is an interesting addition to existing learning tools for surgery as it provides a way to obtain a feedback on which parts of an exercise have been used to classify the attempt as correct or incorrect.

Automatic data-driven real-time segmentation and recognition of surgical workflow

PURPOSE

With the intention of extending the perception and action of surgical staff inside the operating room, the medical community has expressed a growing interest towards context-aware systems. Requiring an accurate identification of the surgical workflow, such systems make use of data from a diverse set of available sensors. In this paper, we propose a fully data-driven and real-time method for segmentation and recognition of surgical phases using a combination of video data and instrument usage signals, exploiting no prior knowledge. We also introduce new validation metrics for assessment of workflow detection.

METHODS

The segmentation and recognition are based on a four-stage process. Firstly, during the learning time, a Surgical Process Model is automatically constructed from data annotations to guide the following process. Secondly, data samples are described using a combination of low-level visual cues and instrument information. Then, in the third stage, these descriptions are employed to train a set of AdaBoost classifiers capable of distinguishing one surgical phase from others. Finally, AdaBoost responses are used as input to a Hidden semi-Markov Model in order to obtain a final decision.

RESULTS

On the MICCAI EndoVis challenge laparoscopic dataset we achieved a precision and a recall of 91 % in classification of 7 phases.

CONCLUSION

Compared to the analysis based on one data type only, a combination of visual features and instrument signals allows better segmentation, reduction of the detection delay and discovery of the correct phase order.