Forces of Tool-Tissue Interaction to Assess Surgical Skill Level

The development of tissue handling skills is sufficient and comparable after training in virtual reality or on a surgical robotic system: a prospective randomized trial

BACKGROUND

Virtual reality is a frequently chosen method for learning the basics of robotic surgery. However, it is unclear whether tissue handling is adequately trained in VR training compared to training on a real robotic system.

METHODS

In this randomized controlled trial, participants were split into two groups for "Fundamentals of Robotic Surgery (FRS)" training on either a DaVinci VR simulator (VR group) or a DaVinci robotic system (Robot group). All participants completed four tasks on the DaVinci robotic system before training (Baseline test), after proficiency in three FRS tasks (Midterm test), and after proficiency in all FRS tasks (Final test). Primary endpoints were forces applied across tests.

RESULTS

This trial included 87 robotic novices, of which 43 and 44 participants received FRS training in VR group and Robot group, respectively. The Baseline test showed no significant differences in force application between the groups indicating a sufficient randomization. In the Midterm and Final test, the force application was not different between groups. Both groups displayed sufficient learning curves with significant improvement of force application. However, the Robot group needed significantly less repetitions in the three FRS tasks Ring tower (Robot: 2.48 vs. VR: 5.45; p < 0.001), Knot Tying (Robot: 5.34 vs. VR: 8.13; p = 0.006), and Vessel Energy Dissection (Robot: 2 vs. VR: 2.38; p = 0.001) until reaching proficiency.

CONCLUSION

Robotic tissue handling skills improve significantly and comparably after both VR training and training on a real robotic system, but training on a VR simulator might be less efficient.

An intelligent grasper to provide real-time force feedback to shorten the learning curve in laparoscopic training

BACKGROUND

A lack of force feedback in laparoscopic surgery often leads to a steep learning curve to the novices and traditional training system equipped with force feedback need a high educational cost. This study aimed to use a laparoscopic grasper providing force feedback in laparoscopic training which can assist in controlling of gripping forces and improve the learning processing of the novices.

METHODS

Firstly, we conducted a pre-experiment to verify the role of force feedback in gripping operations and establish the safe gripping force threshold for the tasks. Following this, we proceeded with a four-week training program. Unlike the novices without feedback (Group A2), the novices receiving feedback (Group B2) underwent training that included force feedback. Finally, we completed a follow-up period without providing force feedback to assess the training effect under different conditions. Real-time force parameters were recorded and compared.

RESULTS

In the pre-experiment, we set the gripping force threshold for the tasks based on the experienced surgeons' performance. This is reasonable as the experienced surgeons have obtained adequate skill of handling grasper. The thresholds for task 1, 2, and 3 were set as 0.731 N, 1.203 N and 0.938 N, respectively. With force feedback, the gripping force applied by the novices with feedback (Group B1) was lower than that of the novices without feedback (Group A1) (p < 0.005). During the training period, the Group B2 takes 6 trails to achieve gripping force of 0.635 N, which is lower than the threshold line, whereas the Group A2 needs 11 trails, meaning that the learning curve of Group B2 was significantly shorter than that of Group A2. Additionally, during the follow-up period, there was no significant decline in force learning, and Group B2 demonstrated better control of gripping operations. The training with force feedback received positive evaluations.

CONCLUSION

Our study shows that using a grasper providing force feedback in laparoscopic training can help to control the gripping force and shorten the learning curve. It is anticipated that the laparoscopic grasper equipped with FBG sensor is promising to provide force feedback during laparoscopic training, which ultimately shows great potential in laparoscopic surgery.

Deep learning-based video-analysis of instrument motion in microvascular anastomosis training

PURPOSE

Attaining sufficient microsurgical skills is paramount for neurosurgical trainees. Kinematic analysis of surgical instruments using video offers the potential for an objective assessment of microsurgical proficiency, thereby enhancing surgical training and patient safety. The purposes of this study were to develop a deep-learning-based automated instrument tip-detection algorithm, and to validate its performance in microvascular anastomosis training.

METHODS

An automated instrument tip-tracking algorithm was developed and trained using YOLOv2, based on clinical microsurgical videos and microvascular anastomosis practice videos. With this model, we measured motion economy (procedural time and path distance) and motion smoothness (normalized jerk index) during the task of suturing artificial blood vessels for end-to-side anastomosis. These parameters were validated using traditional criteria-based rating scales and were compared across surgeons with varying microsurgical experience (novice, intermediate, and expert). The suturing task was deconstructed into four distinct phases, and parameters within each phase were compared between novice and expert surgeons.

RESULTS

The high accuracy of the developed model was indicated by a mean Dice similarity coefficient of 0.87. Deep learning-based parameters (procedural time, path distance, and normalized jerk index) exhibited correlations with traditional criteria-based rating scales and surgeons' years of experience. Experts completed the suturing task faster than novices. The total path distance for the right (dominant) side instrument movement was shorter for experts compared to novices. However, for the left (non-dominant) side, differences between the two groups were observed only in specific phases. The normalized jerk index for both the right and left sides was significantly lower in the expert than in the novice groups, and receiver operating characteristic analysis showed strong discriminative ability.

CONCLUSION

The deep learning-based kinematic analytic approach for surgical instruments proves beneficial in assessing performance in microvascular anastomosis. Moreover, this methodology can be adapted for use in clinical settings.

A Deep Learning Approach to Classify Surgical Skill in Microsurgery Using Force Data from a Novel Sensorised Surgical Glove

Microsurgery serves as the foundation for numerous operative procedures. Given its highly technical nature, the assessment of surgical skill becomes an essential component of clinical practice and microsurgery education. The interaction forces between surgical tools and tissues play a pivotal role in surgical success, making them a valuable indicator of surgical skill. In this study, we employ six distinct deep learning architectures (LSTM, GRU, Bi-LSTM, CLDNN, TCN, Transformer) specifically designed for the classification of surgical skill levels. We use force data obtained from a novel sensorized surgical glove utilized during a microsurgical task. To enhance the performance of our models, we propose six data augmentation techniques. The proposed frameworks are accompanied by a comprehensive analysis, both quantitative and qualitative, including experiments conducted with two cross-validation schemes and interpretable visualizations of the network's decision-making process. Our experimental results show that CLDNN and TCN are the top-performing models, achieving impressive accuracy rates of 96.16% and 97.45%, respectively. This not only underscores the effectiveness of our proposed architectures, but also serves as compelling evidence that the force data obtained through the sensorized surgical glove contains valuable information regarding surgical skill.

Tissue Acceleration as a Novel Metric for Surgical Performance During Carotid Endarterectomy

BACKGROUND AND OBJECTIVES

Gentle tissue handling to avoid excessive motion of affected fragile vessels during surgical dissection is essential for both surgeon proficiency and patient safety during carotid endarterectomy (CEA). However, a void remains in the quantification of these aspects during surgery. The video-based measurement of tissue acceleration is presented as a novel metric for the objective assessment of surgical performance. This study aimed to evaluate whether such metrics correlate with both surgeons' skill proficiency and adverse events during CEA.

METHODS

In a retrospective study including 117 patients who underwent CEA, acceleration of the carotid artery was measured during exposure through a video-based analysis. Tissue acceleration values and threshold violation error frequencies were analyzed and compared among the surgeon groups with different surgical experience (3 groups: novice , intermediate , and expert ). Multiple patient-related variables, surgeon groups, and video-based surgical performance parameters were compared between the patients with and without adverse events during CEA.

RESULTS

Eleven patients (9.4%) experienced adverse events after CEA, and the rate of adverse events significantly correlated with the surgeon group. The mean maximum tissue acceleration and number of errors during surgical tasks significantly decreased from novice, to intermediate, to expert surgeons, and stepwise discriminant analysis showed that the combined use of surgical performance factors could accurately discriminate between surgeon groups. The multivariate logistic regression analysis revealed that the number of errors and vulnerable carotid plaques were associated with adverse events.

CONCLUSION

Tissue acceleration profiles can be a novel metric for the objective assessment of surgical performance and the prediction of adverse events during surgery. Thus, this concept can be introduced into futuristic computer-aided surgeries for both surgical education and patient safety.

Force Profile as Surgeon-Specific Signature

Objective

To investigate the notion that a surgeon's force profile can be the signature of their identity and performance.

Summary background data

Surgeon performance in the operating room is an understudied topic. The advent of deep learning methods paired with a sensorized surgical device presents an opportunity to incorporate quantitative insight into surgical performance and processes. Using a device called the SmartForceps System and through automated analytics, we have previously reported surgeon force profile, surgical skill, and task classification. However, an investigation of whether an individual surgeon can be identified by surgical technique has yet to be studied.

Methods

In this study, we investigate multiple neural network architectures to identify the surgeon associated with their time-series tool-tissue forces using bipolar forceps data. The surgeon associated with each 10-second window of force data was labeled, and the data were randomly split into 80% for model training and validation (10% validation) and 20% for testing. Data imbalance was mitigated through subsampling from more populated classes with a random size adjustment based on 0.1% of sample counts in the respective class. An exploratory analysis of force segments was performed to investigate underlying patterns differentiating individual surgical techniques.

Results

In a dataset of 2819 ten-second time segments from 89 neurosurgical cases, the best-performing model achieved a micro-average area under the curve of 0.97, a testing F1-score of 0.82, a sensitivity of 82%, and a precision of 82%. This model was a time-series ResNet model to extract features from the time-series data followed by a linearized output into the XGBoost algorithm. Furthermore, we found that convolutional neural networks outperformed long short-term memory networks in performance and speed. Using a weighted average approach, an ensemble model was able to identify an expert surgeon with 83.8% accuracy using a validation dataset.

Conclusions

Our results demonstrate that each surgeon has a unique force profile amenable to identification using deep learning methods. We anticipate our models will enable a quantitative framework to provide bespoke feedback to surgeons and to track their skill progression longitudinally. Furthermore, the ability to recognize individual surgeons introduces the mechanism of correlating outcome to surgeon performance.

Tool-tissue force segmentation and pattern recognition for evaluating neurosurgical performance

Surgical data quantification and comprehension expose subtle patterns in tasks and performance. Enabling surgical devices with artificial intelligence provides surgeons with personalized and objective performance evaluation: a virtual surgical assist. Here we present machine learning models developed for analyzing surgical finesse using tool-tissue interaction force data in surgical dissection obtained from a sensorized bipolar forceps. Data modeling was performed using 50 neurosurgery procedures that involved elective surgical treatment for various intracranial pathologies. The data collection was conducted by 13 surgeons of varying experience levels using sensorized bipolar forceps, SmartForceps System. The machine learning algorithm constituted design and implementation for three primary purposes, i.e., force profile segmentation for obtaining active periods of tool utilization using T-U-Net, surgical skill classification into Expert and Novice, and surgical task recognition into two primary categories of Coagulation versus non-Coagulation using FTFIT deep learning architectures. The final report to surgeon was a dashboard containing recognized segments of force application categorized into skill and task classes along with performance metrics charts compared to expert level surgeons. Operating room data recording of > 161 h containing approximately 3.6 K periods of tool operation was utilized. The modeling resulted in Weighted F1-score = 0.95 and AUC = 0.99 for force profile segmentation using T-U-Net, Weighted F1-score = 0.71 and AUC = 0.81 for surgical skill classification, and Weighted F1-score = 0.82 and AUC = 0.89 for surgical task recognition using a subset of hand-crafted features augmented to FTFIT neural network. This study delivers a novel machine learning module in a cloud, enabling an end-to-end platform for intraoperative surgical performance monitoring and evaluation. Accessed through a secure application for professional connectivity, a paradigm for data-driven learning is established.

A synthetic model simulator for intracranial aneurysm clipping: validation of the UpSurgeOn AneurysmBox

Background and objectives

In recent decades, the rise of endovascular management of aneurysms has led to a significant decline in operative training for surgical aneurysm clipping. Simulation has the potential to bridge this gap and benchtop synthetic simulators aim to combine the best of both anatomical realism and haptic feedback. The aim of this study was to validate a synthetic benchtop simulator for aneurysm clipping (AneurysmBox, UpSurgeOn).

Methods

Expert and novice surgeons from multiple neurosurgical centres were asked to clip a terminal internal carotid artery aneurysm using the AneurysmBox. Face and content validity were evaluated using Likert scales by asking experts to complete a post-task questionnaire. Construct validity was evaluated by comparing expert and novice performance using the modified Objective Structured Assessment of Technical Skills (mOSATS), developing a curriculum-derived assessment of Specific Technical Skills (STS), and measuring the forces exerted using a force-sensitive glove.

Results

Ten experts and eighteen novices completed the task. Most experts agreed that the brain looked realistic (8/10), but far fewer agreed that the brain felt realistic (2/10). Half the expert participants (5/10) agreed that the aneurysm clip application task was realistic. When compared to novices, experts had a significantly higher median mOSATS (27 vs. 14.5; p < 0.01) and STS score (18 vs. 9; p < 0.01); the STS score was strongly correlated with the previously validated mOSATS score (p < 0.01). Overall, there was a trend towards experts exerting a lower median force than novices, however, these differences were not statistically significant (3.8 N vs. 4.0 N; p = 0.77). Suggested improvements for the model included reduced stiffness and the addition of cerebrospinal fluid (CSF) and arachnoid mater.

Conclusion

At present, the AneurysmBox has equivocal face and content validity, and future versions may benefit from materials that allow for improved haptic feedback. Nonetheless, it has good construct validity, suggesting it is a promising adjunct to training.

Patient-Specific 3D Printed Soft Models for Liver Surgical Planning and Hands-On Training

Background: Pre-surgical simulation-based training with three-dimensional (3D) models has been intensively developed in complex surgeries in recent years. This is also the case in liver surgery, although with fewer reported examples. The simulation-based training with 3D models represents an alternative to current surgical simulation methods based on animal or ex vivo models or virtual reality (VR), showing reported advantages, which makes the development of realistic 3D-printed models an option. This work presents an innovative, low-cost approach for producing patient-specific 3D anatomical models for hands-on simulation and training. Methods: The article reports three paediatric cases presenting complex liver tumours that were transferred to a major paediatric referral centre for treatment: hepatoblastoma, hepatic hamartoma and biliary tract rhabdomyosarcoma. The complete process of the additively manufactured liver tumour simulators is described, and the different steps for the correct development of each case are explained: (1) medical image acquisition; (2) segmentation; (3) 3D printing; (4) quality control/validation; and (5) cost. A digital workflow for liver cancer surgical planning is proposed. Results: Three hepatic surgeries were planned, with 3D simulators built using 3D printing and silicone moulding techniques. The 3D physical models showed highly accurate replications of the actual condition. Additionally, they proved to be more cost-effective in comparison with other models. Conclusions: It is demonstrated that it is possible to manufacture accurate and cost-effective 3D-printed soft surgical planning simulators for treating liver cancer. The 3D models allowed for proper pre-surgical planning and simulation training in the three cases reported, making it a valuable aid for surgeons.

A Sensorised Surgical Glove to Analyze Forces During Neurosurgery

BACKGROUND

Measuring intraoperative forces in real time can provide feedback mechanisms to improve patient safety and surgical training. Previous force monitoring has been achieved through the development of specialized and adapted instruments or use designs that are incompatible with neurosurgical workflow.

OBJECTIVE

To design a universal sensorised surgical glove to detect intraoperative forces, applicable to any surgical procedure, and any surgical instrument in either hand.

METHODS

We created a sensorised surgical glove that was calibrated across 0 to 10 N. A laboratory experiment demonstrated that the sensorised glove was able to determine instrument-tissue forces. Six expert and 6 novice neurosurgeons completed a validated grape dissection task 20 times consecutively wearing the sensorised glove. The primary outcome was median and maximum force (N).

RESULTS

The sensorised glove was able to determine instrument-tissue forces reliably. The average force applied by experts (2.14 N) was significantly lower than the average force exerted by novices (7.15 N) ( P = .002). The maximum force applied by experts (6.32 N) was also significantly lower than the maximum force exerted by novices (9.80 N) ( P = .004). The sensorised surgical glove's introduction to operative workflow was feasible and did not impede on task performance.

CONCLUSION

We demonstrate a novel and scalable technique to detect forces during neurosurgery. Force analysis can provide real-time data to optimize intraoperative tissue forces, reduce the risk of tissue injury, and provide objective metrics for training and assessment.

A Preliminary Study on Grip-Induced Nerve Damage Caused by a Soft Pneumatic Elastomeric Gripper

Forceps, clamps, and haemostats are essential surgical tools required for all surgical interventions. While they are widely used to grasp, hold, and manipulate soft tissue, their metallic rigid structure may cause tissue damage due to the potential risk of applying excessive gripping forces. Soft pneumatic surgical grippers fabricated by silicone elastomeric materials with low Young’s modulus may offer a promising solution to minimize this unintentional damage due to their inherent excellent compliance and compressibility. The goal of this work is to evaluate and compare the grip-induced nerve damage caused by the soft pneumatic elastomeric gripper and conventional haemostats during surgical manipulation. Twenty-four Wistar rats (male, seven weeks) are subjected to sciatic nerve compression (right hind limb) using the soft pneumatic elastomer gripper and haemostats. A histopathological analysis is conducted at different time-points (Day 0, Day 3, Day 7 and Day 13) after the nerve compression to examine the morphological tissue changes between the rats in the ‘soft gripper’ group and the ‘haemostats’ group. A free walking analysis is also performed to examine the walking function of the rats after recovery from different time points. Comparing the rigid haemostats and soft gripper groups, there is a visible difference in the degree of axonal vacuolar degeneration between the groups, which could suggest the presence of substantial nerve damage in the ‘haemostats’ group. The rats in the haemostats group exhibited reduced right hind paw pressure and paw size after the nerve compression. It shows that the rats tend not to exert more force on the affected right hind limb in the haemostats group compared to the soft gripper group. In addition, the stance duration was reduced in the injured right hind limb compared to the normal left hind limb in the haemostats group. These observations show that the soft pneumatic surgical gripper made of silicone elastomeric materials might reduce the severity of grip-induced damage by providing a safe compliant grip compared to the conventional haemostats. The soft pneumatic elastomer gripper could complement the current surgical gripping tool in delicate tissue manipulation.

Gaze Behaviors of Neurosurgeon in Performing Gross Movements under Microscope

BACKGROUND

Frequent intermittent bleeding control and suction are often necessary during microneurosurgical procedures. We compared the visual searching strategy that guides these types of gross hand movements between expert surgeons and neurosurgical residents.

METHODS

A total of 70 trials of a microsurgical task consisting of moving a cotton ball with bipolar forceps and a suction instrument under the microscope were recorded. Based on surgical videos, we extracted total task time, boarding time, gaze-tool time gap, number of fixations during the boarding time, and target-locked fixation duration. A comparison of these measures between experts and resident surgeons were performed.

RESULTS

No significant difference was found for total task time, boarding time, and number of fixations during the boarding time between the 2 groups of surgeons. However, we found significant differences in the measure of gaze-tool time gap (P < 0.001, partial η² = 0.54) and the target-locked fixation (P < 0.001, partial η² = 0.24). Specifically, expert microsurgeons' eyes move to the target board earlier before their tools in hands move (1.01 ± 0.30 seconds) compared with resident microsurgeons (0.44 ± 0.17 seconds). The target-locked fixation duration was also longer among experts comparing to residents (experts: 1.16 ± 0.82 seconds, residents: 0.40 ± 0.30 seconds).

CONCLUSIONS

Task time analysis for this basic microsurgical task is not sufficient to evaluate participants' level of expertise. Gaze behavior analysis helps to reveal hidden differences between experts and residents. This research provides more evidence that supports the use of gaze analysis for assessing surgeons' skills in microsurgery.

Nondominant Hand Skills Spatial and Psychomotor Analysis During a Complex Virtual Reality Neurosurgical Task-A Case Series Study

BACKGROUND

Virtual reality surgical simulators provide detailed psychomotor performance data, allowing qualitative and quantitative assessment of hand function. The nondominant hand plays an essential role in neurosurgery in exposing the operative area, assisting the dominant hand to optimize task execution, and hemostasis. Outlining expert-level nondominant hand skills may be critical to understand surgical expertise and aid learner training.

OBJECTIVE

To (1) provide validity for the simulated bimanual subpial tumor resection task and (2) to use this simulation in qualitative and quantitative evaluation of nondominant hand skills for bipolar forceps utilization.

METHODS

In this case series study, 45 right-handed participants performed a simulated subpial tumor resection using simulated bipolar forceps in the nondominant hand for assisting the surgery and hemostasis. A 10-item questionnaire was used to assess task validity. The nondominant hand skills across 4 expertise levels (neurosurgeons, senior trainees, junior trainees, and medical students) were analyzed by 2 visual models and performance metrics.

RESULTS

Neurosurgeon median (range) overall satisfaction with the simulated scenario was 4.0/5.0 (2.0-5.0). The visual models demonstrated a decrease in high force application areas on pial surface with increased expertise level. Bipolar-pia mater interactions were more focused around the tumoral region for neurosurgeons and senior trainees. These groups spent more time using the bipolar while interacting with pia. All groups spent significantly higher time in the left upper pial quadrant than other quadrants.

CONCLUSION

This work introduces new approaches for the evaluation of nondominant hand skills which may help surgical trainees by providing both qualitative and quantitative feedback.

Tool-Tissue Forces in Hemangioblastoma Surgery

OBJECTIVE

Surgical resection of intracranial hemangioblastoma poses technical challenges that may be difficult to impart to trainees. Here, we introduce knowledge of tool-tissue forces in Newton (N), observed during hemangioblastoma surgery.

METHODS

Seven surgeons (2 groups: trainees and mentor), with mentor (n = 1) and trainees (n = 6, PGY 1-6 including clinical fellowship), participated in 6 intracranial hemangioblastoma surgeries. Using sensorized bipolar forceps, we evaluated tool-tissue force profiles of 5 predetermined surgical tasks: 1) dissection, 2) coagulation, 3) retracting, 4) pulling, and 5) manipulating. Force profile for each trial included force duration, average, maximum, minimum, range, standard deviation (SD), and correlation coefficient. Force errors including unsuccessful trial bleeding or incomplete were compared between surgeons and with successful trials.

RESULTS

Force data from 718 trials were collected. The mean (standard deviation) of force used in all surgical tasks and across all surgical levels was 0.20 ± 0.17 N. The forces exerted by trainee surgeons were significantly lower than those of the mentor (0.15 vs. 0.24; P < 0.0001). A total of 18 (4.5%) trials were unsuccessful, 4 of them being unsuccessful trial-bleeding and the rest, unsuccessful trial-incomplete. The force in unsuccessful trial-bleeding was higher than successful trials (0.3 [0.09] vs. 0.17 [0.11]; P = 0.0401). Toward the end of surgery, higher force was observed (0.17 vs. 0.20; P < 0.0001).

CONCLUSIONS

The quantification of tool-tissue forces during hemangioblastoma surgery with feedback to the surgeon, could well enhance surgical training and allow avoidance of bleeding associated with high force error.

A cost-effective IoT learning environment for the training and assessment of surgical technical skills with visual learning analytics

BACKGROUND

Surgeons need to train and certify their technical skills. This is usually done with the intervention of experts who monitor and assess trainees. Nevertheless, this is a time-consuming task that is subject to variations among evaluators. In recent decades, subjectivity has been significantly reduced through 1) the introduction of standard curricula, such as the Fundamentals of Laparoscopic Surgery (FLS) program, which measures students' performance in specific exercises, and 2) rubrics, which are widely accepted in the literature and serve to provide feedback about the overall technical skills of the trainees. Although these two elements reduce subjectivity, they do not, however, eliminate the figure of the expert evaluator, and so the process remains time consuming.

OBJECTIVES

The objective of this work is to automate those parts of the work of the expert evaluator that the technology can measure objectively, using sensors to collect evidence, and visualizations to provide feedback. We designed and developed 1) a cost-effective IoT (Internet of Things) learning environment for the training and assessment of surgical technical skills and 2) visualizations supported by the literature on visual learning analytics (VLA) to provide feedback about the exercises (in real time) and overall performance (at the end of the training) of the trainee.

METHODS

A hybrid approach was followed based on previous research for the design of the sensor based IoT learning environment. Previous studies were used as the basis for getting best practices on the tracking of surgical instruments and on the detection of the force applied to the tissue, with a focus on reducing the costs of data collection. The monitoring of the specific exercises required the design of sensors and collection mechanisms from scratch as there is little existing research on this subject. Moreover, it was necessary to design the overall architecture to collect, process, synchronize and communicate the data coming from the different sensors to provide high-level information relevant to the end user. The information to be presented was already validated by the literature and the focus was on how to visualize this information and the optimal time for its presentation to end users. The visualizations were validated with 18 VLA experts assessing the technical aspects of the visualizations and 4 medical experts assessing their functional aspects.

RESULTS

This IoT learning environment amplifies the evaluation mechanisms already validated by the literature, allowing automatic data collection. First, it uses IoT sensors to automatically correct two of the exercises defined in the FLS (peg transfer and precision cutting), providing real-time visualizations. Second it monitors the movement of the surgical instruments and the force applied to the tissues during the exercise, computing 6 of the high-level indicators used by expert evaluators in their rubrics (efficiency, economy of movement, hand tremor, depth perception, bimanual dexterity, and respect for tissue), providing feedback about the technical skills of the trainee using a radar chart with these six indicators at the end of the training (summative visualizations).

CONCLUSIONS

The proposed IoT learning environment is a promising and cost-effective alternative to help in the training and assessment of surgical technical skills. The system shows the trainees' progress and presents new indicators about the correctness of each specific exercise through real-time visualizations, as well as their general technical skills through summative visualizations, aligned with the 6 more frequent indicators in standardized scales. Early results suggest that although both types of visualizations are useful, it is necessary to reduce the cognitive load of the graphs presented in real time during training. Nevertheless, an additional evaluation is needed to confirm these results.

Current concepts and perspectives on brain arteriovenous malformations: A review of pathogenesis and multidisciplinary treatment

Brain arteriovenous malformations (bAVMs) are unusual vascular pathologies characterized by the abnormal aggregation of dilated arteries and veins in the brain parenchyma and for which the absence of a normal vascular structure and capillary bed leads to direct connections between arteries and veins. Although bAVMs have long been believed to be congenital anomalies that develop during the prenatal period, current studies show that inflammation is associated with AVM genesis, growth, and rupture. Interventional treatment options include microsurgery, stereotactic radiosurgery, and endovascular embolization, and management often comprises a multidisciplinary combination of these modalities. The appropriate selection of patients with brain arteriovenous malformations for interventional treatment requires balancing the risk of treatment complications against the risk of hemorrhaging during the natural course of the pathology; however, no definitive guidelines have been established for the management of brain arteriovenous malformations. In this paper, we comprehensively review the current basic and clinical studies on bAVMs and discuss the contemporary status of multidisciplinary management of bAVMs.

A data-driven performance dashboard for surgical dissection

Surgical error and resulting complication have significant patient and economic consequences. Inappropriate exertion of tool-tissue force is a common variable for such error, that can be objectively monitored by sensorized tools. The rich digital output establishes a powerful skill assessment and sharing platform for surgical performance and training. Here we present SmartForceps data app incorporating an Expert Room environment for tracking and analysing the objective performance and surgical finesse through multiple interfaces specific for surgeons and data scientists. The app is enriched by incoming geospatial information, data distribution for engineered features, performance dashboard compared to expert surgeon, and interactive skill prediction and task recognition tools to develop artificial intelligence models. The study launches the concept of democratizing surgical data through a connectivity interface between surgeons with a broad and deep capability of geographic reach through mobile devices with highly interactive infographics and tools for performance monitoring, comparison, and improvement.

Immersive 3-Dimensional Virtual Reality Modeling for Case-Specific Presurgical Discussions in Cerebrovascular Neurosurgery

BACKGROUND

Adequate surgical planning includes a precise understanding of patient-specific anatomy and is a necessity for neurosurgeons. Although the use of virtual reality (VR) technology is emerging in surgical planning and education, few studies have examined the effectiveness of immersive VR during surgical planning using a modern head-mounted display.

OBJECTIVE

To investigate if and how immersive VR aids presurgical discussions of cerebrovascular surgery.

METHODS

A multiuser immersive VR system, BananaVisionTM, was developed and used during presurgical discussions in a prospective patient cohort undergoing cerebrovascular surgery. A questionnaire/interview was administered to multiple surgeons after the surgeries to evaluate the effectiveness of the VR system compared to conventional imaging modalities. An objective assessment of the surgeon's knowledge of patient-specific anatomy was also conducted by rating surgeons' hand-drawn presurgical illustrations.

RESULTS

The VR session effectively enhanced surgeons' understanding of patient-specific anatomy in the majority of cases (83.3%). An objective assessment of surgeons' presurgical illustrations was consistent with this result. The VR session also effectively improved the decision-making process regarding minor surgical techniques in 61.1% of cases and even aided surgeons in making critical surgical decisions about cases involving complex and challenging anatomy. The utility of the VR system was rated significantly higher by trainees than by experts.

CONCLUSION

Although rated as more useful by trainees than by experts, immersive 3D VR modeling increased surgeons' understanding of patient-specific anatomy and improved surgical strategy in certain cases involving challenging anatomy.

Calibration of surgical tools using multilevel modeling with LINEX loss function: Theory and experiment

Quantifying the tool–tissue interaction forces in surgery can be used in the training process of novice surgeons to help them better handle surgical tools and avoid exerting excessive forces. A significant challenge concerns the development of proper statistical learning techniques to model the relationship between the true force exerted on the tissue and several outputs read from sensors mounted on the surgical tools. We propose a nonparametric bootstrap technique and a Bayesian multilevel modeling methodology to estimate the true forces. We use the linear exponential loss function to asymmetrically penalize the over and underestimation of the applied forces to the tissue. We incorporate the direction of the force as a group factor in our analysis. A weighted approach is used to account for the nonhomogeneity of read voltages from the surgical tool. Our proposed Bayesian multilevel models provide estimates that are more accurate than those under the maximum likelihood and restricted maximum likelihood approaches. Moreover, confidence bounds are much narrower and the biases and root mean squared errors are significantly smaller in our multilevel models with the linear exponential loss function.

Tool-tissue forces in surgery: A systematic review

Background

Excessive tool-tissue interaction forces often result in tissue damage and intraoperative complications, while insufficient forces prevent the completion of the task. This review sought to explore the tool-tissue interaction forces exerted by instruments during surgery across different specialities, tissues, manoeuvres and experience levels.

Materials & methods

A PRISMA-guided systematic review was carried out using Embase, Medline and Web of Science databases.

Results

Of 462 articles screened, 45 studies discussing surgical tool-tissue forces were included. The studies were categorized into 9 different specialities with the mean of average forces lowest for ophthalmology (0.04N) and highest for orthopaedic surgery (210N). Nervous tissue required the least amount of force to manipulate (mean of average: 0.4N), whilst connective tissue (including bone) required the most (mean of average: 45.8). For manoeuvres, drilling recorded the highest forces (mean of average: 14N), whilst sharp dissection recorded the lowest (mean of average: 0.03N). When comparing differences in the mean of average forces between groups, novices exerted 22.7% more force than experts, and presence of a feedback mechanism (e.g. audio) reduced exerted forces by 47.9%.

Conclusions

The measurement of tool-tissue forces is a novel but rapidly expanding field. The range of forces applied varies according to surgical speciality, tissue, manoeuvre, operator experience and feedback provided. Knowledge of the safe range of surgical forces will improve surgical safety whilst maintaining effectiveness. Measuring forces during surgery may provide an objective metric for training and assessment. Development of smart instruments, robotics and integrated feedback systems will facilitate this.

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This review explores tool-tissue forces during surgery, a new and expanding field.

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Forces were lowest in ophthalmology (0.04N) and highest in orthopaedics (210N).

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Forces were lowest during sharp dissection (0.03N) and highest when drilling (14N).

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Being an expert (vs. novice) and having feedback mechanisms (e.g. haptic) reduced exerted forces.

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Development of force metrics will facilitate training, assessment & novel technology.

Pre-Treat Xenogenic Collagenous Blocks of Bone Substitutes with Saline Facilitate Their Manipulation and Guarantee High Bone Regeneration Rates, Qualitatively and Quantitatively

Deproteinized bovine bone mineral particles embedded in collagen (DBBM-C) are widely used for bone regenerations with excellent, albeit sometimes variable clinical outcomes. Clinicians usually prepare DBBM-C by mixing with blood. Replacing blood by saline represents an alternative. We investigated if saline treatment could improve DBBM-C i. handling in vitro and ii. biological performances in a rabbit calvarial model. In vitro, DBBM-C blocks soaked in saline or blood were submitted to compression tests. In vivo, four poly ether ether ketone (PEEK)cylinders were placed on 16 rabbit skulls, filled with DBBM-C soaked in blood or saline for 2-4-8-12 weeks before histomorphometry. DBBM-C blocks were fully hydrated after 30 s in saline when 120 s in blood could not hydrate blocks core. Stiffness gradually decreased 2.5-fold after blood soaking whereas a six-fold decrease was measured after 30 s in saline. In vivo, saline treatment allowed 50% more bone regeneration during the first month when compared to blood soaking. This difference was then no longer visible. New bone morphology and maturity were equivalent in both conditions. DBBM-C saline-soaking facilitated its handling and accelerated bone regeneration of highly qualitative tissues when compared to blood treatment. Saline pretreatment thus may increase the clinical predictability of bone augmentation procedures.

Objective and automated assessment of surgical technical skills with IoT systems: A systematic literature review

The assessment of surgical technical skills to be acquired by novice surgeons has been traditionally done by an expert surgeon and is therefore of a subjective nature. Nevertheless, the recent advances on IoT (Internet of Things), the possibility of incorporating sensors into objects and environments in order to collect large amounts of data, and the progress on machine learning are facilitating a more objective and automated assessment of surgical technical skills. This paper presents a systematic literature review of papers published after 2013 discussing the objective and automated assessment of surgical technical skills. 101 out of an initial list of 537 papers were analyzed to identify: 1) the sensors used; 2) the data collected by these sensors and the relationship between these data, surgical technical skills and surgeons' levels of expertise; 3) the statistical methods and algorithms used to process these data; and 4) the feedback provided based on the outputs of these statistical methods and algorithms. Particularly, 1) mechanical and electromagnetic sensors are widely used for tool tracking, while inertial measurement units are widely used for body tracking; 2) path length, number of sub-movements, smoothness, fixation, saccade and total time are the main indicators obtained from raw data and serve to assess surgical technical skills such as economy, efficiency, hand tremor, or mind control, and distinguish between two or three levels of expertise (novice/intermediate/advanced surgeons); 3) SVM (Support Vector Machines) and Neural Networks are the preferred statistical methods and algorithms for processing the data collected, while new opportunities are opened up to combine various algorithms and use deep learning; and 4) feedback is provided by matching performance indicators and a lexicon of words and visualizations, although there is considerable room for research in the context of feedback and visualizations, taking, for example, ideas from learning analytics.

Monitor-based exoscopic 3D4k neurosurgical interventions: a two-phase prospective-randomized clinical evaluation of a novel hybrid device

Background

Promoting a disruptive innovation in microsurgery, exoscopes promise alleviation of physical strain and improved image quality through digital visualization during microneurosurgical interventions. This study investigates the impact of a novel 3D4k hybrid exoscope (i.e., combining digital and optical visualization) on surgical performance and team workflow in preclinical and clinical neurosurgical settings.

Methods

A pre-clinical workshop setting has been developed to assess usability and implementability through skill-based scenarios (neurosurgical participants n = 12). An intraoperative exploration in head and spine surgery (n = 9) and a randomized clinical study comparing ocular and monitor mode in supratentorial brain tumor cases (n = 20) followed within 12 months. Setup, procedure, case characteristics, surgical performance, and user experience have been analyzed for both ocular group (OG) and monitor group (MG).

Results

Brain tumor cases using frontal, frontoparietal, or temporal approaches have been identified as favorable use cases for introducing exoscopic neurosurgery. Mean monitor distance and angle were 180 cm and 10°. Surgical ergonomics when sitting improved significantly in MG compared with OG (P = .03). Hand-eye coordination required familiarization in MG. Preclinical data showed a positive correlation between lateral camera inclination and impact on hand-eye coordination (r_s = 0.756, P = .01). There was no significant added surgical time in MG. Image quality in current generation 3D4k monitors has been rated inferior to optic visualization yet awaits updates.

Conclusions

The hybrid exoscopic device can be integrated into established neurosurgical workflows. Currently, exoscopic interventions seem most suited for cranial tumor surgery in lesions that are not deep-seated. Ergonomics improve in monitor mode compared to conventional microsurgery.

Electronic supplementary material

The online version of this article (10.1007/s00701-020-04361-2) contains supplementary material, which is available to authorized users.

A Novel Bipolar Cautery Tool for Minimally-Invasive Neuroendoscopic Procedures

Electrosurgery is used in the operating room on a daily basis as a means to cut tissue and maintain hemostasis. The principle of this technology lies in the transfer of electricity from an electrosurgical unit to the operating site on a patient's body and modifying the waveform of that electricity to achieve the desired surgical effect. Bipolar cautery uses two electrodes, an active and a return, both at the surgical site to perform electrosurgery. Bipolar cautery can be very useful in helping surgeons to operate; however, current designs are not well suited to a 2.1 mm working channel in endoscopic procedures due to their rigid structure, limited range of motion, and bulky design. This paper describes a novel approach to designing a minimally- invasive bipolar cautery tool suitable for flexible neuroendoscopy. The system features 1.9 mm diameter bipolar tips which resemble grasping forceps, making it easier for surgeons to hold tissue while performing electrosurgery. The electrode wires also function as the actuating cables used to open and close the tips, which require 2.10 mm to open the tips to 30.9 °. The results show that the tool can safely cauterize a porcine brain specimen at various settings on the electrosurgical unit, and increasing the setting increases the area of tissue affected by the electricity. Repeatability was demonstrated and exhaustion was reached after the tool was opened and closed 73 times. Future work will involve improving the current design to increase the number of cycles the tool can survive before losing function.

Development of a Deep Learning-Based Algorithm to Detect the Distal End of a Surgical Instrument

This work aims to develop an algorithm to detect the distal end of a surgical instrument using object detection with deep learning. We employed nine video recordings of carotid endarterectomies for training and testing. We obtained regions of interest (ROI; 32 × 32 pixels), at the end of the surgical instrument on the video images, as supervised data. We applied data augmentation to these ROIs. We employed a You Only Look Once Version 2 (YOLOv2) -based convolutional neural network as the network model for training. The detectors were validated to evaluate average detection precision. The proposed algorithm used the central coordinates of the bounding boxes predicted by YOLOv2. Using the test data, we calculated the detection rate. The average precision (AP) for the ROIs, without data augmentation, was 0.4272 ± 0.108. The AP with data augmentation, of 0.7718 ± 0.0824, was significantly higher than that without data augmentation. The detection rates, including the calculated coordinates of the center points in the centers of 8 × 8 pixels and 16 × 16 pixels, were 0.6100 ± 0.1014 and 0.9653 ± 0.0177, respectively. We expect that the proposed algorithm will be efficient for the analysis of surgical records.

Data analytics interrogates robotic surgical performance using a microsurgery-specific haptic device

OBJECTIVES

With the increase in robot-assisted cases, recording the quantifiable dexterity of surgeons is essential for proficiency evaluations. The present study employs sensor-based kinematics and recorded surgeon experience for evaluating a new haptic device.

METHODS

Thirty surgeons performed a task simulating micromanipulation with neuroArmPLUS^HD and two commercially available hand-controllers. The surgical performance was evaluated based on subjective measures obtained from survey and objective features derived from the sensors. Statistical analyses were performed to assess the hand-controllers and regression analysis was used to identify the key features and develop a machine learning model for surgical skill assessment.

FINDINGS

MANCOVA tests on objective features demonstrated significance (α = 0.05) for time (p = 0.02), errors (p = 0.01), distance (p = 0.03), clutch incidents (p = 0.03), and forces (p = 0.00). The majority of metrics were in favor of neuroArmPLUS^HD. The surgeons found it smoother, more comfortable, less tiring, and easier to maneuver with more realistic force feedback. The ensemble machine learning model trained with 5-fold cross-validation showed an accuracy (SD) of 0.78 (0.15) in surgeon skill classification.

CONCLUSIONS

This study validates the importance of incorporating a superior haptic device in telerobotic surgery for standardization of surgical education and patient care.

A Comparison of Visual Rating Scales and Simulated Virtual Reality Metrics in Neurosurgical Training: A Generalizability Theory Study

BACKGROUND

Adequate assessment and feedback remains a cornerstone of psychomotor skills acquisition, particularly within neurosurgery where the consequence of adverse operative events is significant. However, a critical appraisal of the reliability of visual rating scales in neurosurgery is lacking. Therefore, we sought to design a study to compare visual rating scales with simulated metrics in a neurosurgical virtual reality task.

METHODS

Neurosurgical faculty rated anonymized participant video recordings of the removal of simulated brain tumors using a visual rating scale made up of seven composite elements. Scale reliability was evaluated using generalizability theory, and scale subcomponents were compared with simulated metrics using Pearson correlation analysis.

RESULTS

Four staff neurosurgeons evaluated 16 medical student neurosurgery applicants. Overall scale reliability and internal consistency were 0.73 and 0.90, respectively. Reliability of 0.71 was achieved with two raters. Individual participants, raters, and scale items accounted for 27%, 11%, and 0.6% of the data variability. The hemostasis scale component related to the greatest number of simulated metrics, whereas respect for no-go zones and tissue was correlated with none. Metrics relating to instrument force and patient safety (brain volume removed and blood loss) were captured by the fewest number of rating scale components.

CONCLUSIONS

To our knowledge, this is the first study comparing participant's ratings with simulated performance. Given rating scales capture less well instrument force, quantity of brain volume removed, and blood loss, we suggest adopting a hybrid educational approach using visual rating scales in an operative environment, supplemented by simulated sessions to uncover potentially problematic surgical technique.

Challenges in developing a magnetic resonance-compatible haptic hand-controller for neurosurgical training

A haptic device is an actuated human-machine interface utilized by an operator to dynamically interact with a remote environment. This interaction could be virtual (virtual reality) or physical such as using a robotic arm. To date, different mechanisms have been considered to actuate the haptic device to reflect force feedback from the remote environment. In a low-force environment or limited working envelope, the control of some actuation mechanisms such as hydraulic and pneumatic may be problematic. In the development of a haptic device, challenges include limited space, high accuracy or resolution, limitations in kinematic and dynamic solutions, points of singularity, dexterity as well as control system development/design. Furthermore, the haptic interface designed to operate in a magnetic resonance imaging environment adds additional challenges related to electromagnetic interference, static/variable magnetic fields, and the use of magnetic resonance-compatible materials. Such a device would allow functional magnetic resonance imaging to obtain information on the subject's brain activity while performing a task. When used for surgical trainees, functional magnetic resonance imaging could provide an assessment of surgical skills. In this application, the trainee, located supine within the magnet bore while observing the task environment on a graphical user interface, uses a low-force magnetic resonance-compatible haptic device to perform virtual surgical tasks in a limited space. In the quest to develop such a device, this review reports the multiple challenges faced and their potential solutions. The review also investigates efforts toward prototyping such devices and classifies the main components of a magnetic resonance-compatible device including actuation and sensory systems and materials used.