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.

Design and validation of looping assistance methods in robotic-assisted neonatal surgical suturing in a chest model.

BACKGROUND: Neonate patients have a reduced thoracic cavity, making thoracoscopic procedures even more challenging than their adult counterparts. METHODS: We evaluated five control strategies for robot-assisted thoracoscopic surgical looping in simulations and experiments with a physical robotic system in a neonate surgical phantom. The strategies are composed of state-of-the-art constrained optimization and a novel looping force feedback term. RESULTS: All control strategies allowed users to successfully perform looping. A user study in simulation showed that the proposed strategy was superior in terms of Physical demand p < 0.05 $\left(p< 0.05\right)$ and task duration p < 0.05 $\left(p< 0.05\right)$ . The cumulative sum analysis of inexperienced users shows that the proposed looping force feedback can speed up the learning. Results with surgeons did not show a significant difference among control strategies. CONCLUSIONS: Assistive strategies in looping show promise and further work is needed to extend these benefits to other subtasks in robot-aided surgical suturing.

A force measurement platform for a vitreoretinal surgical simulator using an artificial eye module integrated with a quartz crystal resonator.

To provide quantitative feedback on surgical progress to ophthalmologists practicing inner limiting membrane (ILM) peeling, we developed an artificial eye module comprising a quartz crystal resonator (QCR) force sensor and a strain body that serves as a uniform force transmitter beneath a retinal model. Although a sufficiently large initial force must be loaded onto the QCR force sensor assembly to achieve stable contact with the strain body, the highly sensitive and wide dynamic-range property of this sensor enables the eye module to detect the slight forceps contact force. A parallel-plate strain body is used to achieve a uniform force sensitivity over the 4-mm-diameter ILM peeling region. Combining these two components allowed for a measurable force range of 0.22 mN to 29.6 N with a sensitivity error within -11.3 to 4.2% over the ILM peeling area. Using this eye module, we measured the applied force during a simulation involving artificial ILM peeling by an untrained individual and compensated for the long-term drift of the obtained force data using a newly developed algorithm. The compensated force data clearly captured the characteristics of several types of motion sequences observed from video recordings of the eye bottom using an ophthalmological microscope. As a result, we succeeded in extracting feature values that can be potentially related to trainee skill level, such as the mean and standard deviation of the pushing and peeling forces, corresponding, in the case of an untrained operator, to 122.6 ± 95.2 and 20.4 ± 13.2 mN, respectively.

Bionic eye system mimicking microfluidic structure and intraocular pressure for glaucoma surgery training.

Among increasing eye diseases, glaucoma may hurt the optic nerves and lead to vision loss, the treatment of which is to reduce intraocular pressure (IOP). In this research, we introduce a new concept of the surgery simulator for Minimally Invasive Glaucoma Surgery (MIGS). The concept is comprised of an anterior eye model and a fluidic circulatory system. The model made of flexible material includes a channel like the Schlemm's canal (SC) and a membrane like the trabecular meshwork (TM) covering the SC. The system can monitor IOP in the model by a pressure sensor. In one of the MIGS procedures, the TM is cleaved to reduce the IOP. Using the simulator, ophthalmologists can practice the procedure and measure the IOP. First, considering the characteristics of human eyes, we defined requirements and target performances for the simulator. Next, we designed and manufactured the prototype. Using the prototype, we measured the IOP change before and after cleaving the TM. Finally, we demonstrated the availability by comparing experimental results and target performances. This simulator is also expected to be used for evaluations and developments of new MIGS instruments and ophthalmic surgery robots in addition to the surgical training of ophthalmologists.

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.

Motion analysis of the JHU-ISI Gesture and Skill Assessment Working Set II: learning curve analysis.

PURPOSE: The Johns Hopkins-Intuitive Gesture and Skill Assessment Working Set (JIGSAWS) dataset is used to develop robotic surgery skill assessment tools, but there has been no detailed analysis of this dataset. The aim of this study is to perform a learning curve analysis of the existing JIGSAWS dataset. METHODS: Five trials were performed in JIGSAWS by eight participants (four novices, two intermediates and two experts) for three exercises (suturing, knot-tying and needle passing). Global Rating Scores and time, path length and movements were analyzed quantitatively and qualitatively by graphical analysis. RESULTS: There are no significant differences in Global Rating Scale scores over time. Time in the suturing exercise and path length in needle passing had significant differences. Other kinematic parameters were not significantly different. Qualitative analysis shows a learning curve only for suturing. Cumulative sum analysis suggests completion of the learning curve for suturing by trial 4. CONCLUSIONS: The existing JIGSAWS dataset does not show a quantitative learning curve for Global Rating Scale scores, or most kinematic parameters which may be due in part to the limited size of the dataset. Qualitative analysis shows a learning curve for suturing. Cumulative sum analysis suggests completion of the suturing learning curve by trial 4. An expanded dataset is needed to facilitate subset analyses.

Development of Integrated 3-Dimensional Computer Graphics Human Head Model.

BACKGROUND: Understanding the complex anatomy of neurostructures is very important in various stages of medical education, from medical students to experienced neurosurgeons, and, ultimately, for the knowledge of human beings. OBJECTIVE: To develop an interactive computer graphics (CG) anatomic head model and present the current progress. METHODS: Based on the prior head 3-dimensional CG (3DCG) polygon model, 23 additional published papers and textbooks were consulted, and 2 neurosurgeons and 1 CG technician performed revision and additional polygon modeling. Three independent neurosurgeons scored the clear visibility of anatomic structures relevant to neurosurgical procedures (anterior petrosal and supracerebellar infratentorial approaches) in the integrated 3DCG model (i model) and patients' radiological images (PRIs) such as those obtained from computed tomography, magnetic resonance imaging, and angiography. RESULTS: The i model consisted of 1155 parts (.stl format), with a total of 313 763 375 polygons, including 10 times more information than the foundation model. The i model was able to illustrate complex and minute neuroanatomic structures that PRIs could not as well as extracranial structures such as paranasal sinuses. Our subjective analysis showed that the i model had better clear visibility scores than PRIs, particularly in minute nerves, vasculatures, and dural structures. CONCLUSION: The i model more clearly illustrates minute anatomic structures than PRIs and uniquely illustrates nuclei and fibers that radiological images do not. The i model complements cadaveric dissection by increasing accessibility according to spatial, financial, ethical, and social aspects and can contribute to future medical education.

Motion analysis of the JHU-ISI Gesture and Skill Assessment Working Set using Robotics Video and Motion Assessment Software.

PURPOSE: The JIGSAWS dataset is a fixed dataset of robot-assisted surgery kinematic data used to develop predictive models of skill. The purpose of this study is to analyze the relationships of self-defined skill level with global rating scale scores and kinematic data (time, path length and movements) from three exercises (suturing, knot-tying and needle passing) (right and left hands) in the JIGSAWS dataset. METHODS: Global rating scale scores are reported in the JIGSAWS dataset and kinematic data were calculated using ROVIMAS software. Self-defined skill levels are in the dataset (novice, intermediate, expert). Correlation coefficients (global rating scale-skill level and global rating scale-kinematic parameters) were calculated. Kinematic parameters were compared among skill levels. RESULTS: Global rating scale scores correlated with skill in the knot-tying exercise (r = 0.55, p = 0.0005). In the suturing exercise, time, path length (left) and movements (left) were significantly different (p < 0.05) for novices and experts. For knot-tying, time, path length (right and left) and movements (right) differed significantly for novices and experts. For needle passing, no kinematic parameter was significantly different comparing novices and experts. The only kinematic parameter that correlated with global rating scale scores is time in the knot-tying exercise. CONCLUSION: Global rating scale scores weakly correlate with skill level and kinematic parameters. The ability of kinematic parameters to differentiate among self-defined skill levels is inconsistent. Additional data are needed to enhance the dataset and facilitate subset analyses and future model development.

The effect of simulator fidelity on procedure skill training: a literature review.

OBJECTIVES: To evaluate the effect of simulator fidelity on procedure skill training through a review of existing studies. METHODS: MEDLINE, OVID and EMBASE databases were searched between January 1990 and January 2019. Search terms included "simulator fidelity and comparison" and "low fidelity" and "high fidelity" and "comparison" and "simulator". Author classification of low- and high-fidelity was used for non-laparoscopic procedures. Laparoscopic simulators are classified using a proposed schema. All included studies used a randomized methodology with two or more groups and were written in English. Data was abstracted to a standard data sheet and critically appraised from 17 eligible full papers. RESULTS: Of 17 studies, eight were for laparoscopic and nine for other skill training. Studies employed evaluation methodologies, including subjective and objective measures. The evaluation was conducted once in 13/17 studies and before-after in 4/17. Didactic training only or control groups were used in 5/17 studies, while 10/17 studies included two groups only. Skill acquisition and simulator fidelity were different for the level of training in 1/17 studies. Simulation training was followed by clinical evaluation or a live animal evaluation in 3/17 studies. Low-fidelity training was not inferior to training with a high-fidelity simulator in 15/17 studies. CONCLUSIONS: Procedure skill after training with low fidelity simulators was not inferior to skill after training with high fidelity simulators in 15/17 studies. Some data suggest that the effectiveness of different fidelity simulators depends on the level of training of participants and requires further study.

The effects of different levels of realism on the training of CNNs with only synthetic images for the semantic segmentation of robotic instruments in a head phantom.

PURPOSE: The manual generation of training data for the semantic segmentation of medical images using deep neural networks is a time-consuming and error-prone task. In this paper, we investigate the effect of different levels of realism on the training of deep neural networks for semantic segmentation of robotic instruments. An interactive virtual-reality environment was developed to generate synthetic images for robot-aided endoscopic surgery. In contrast with earlier works, we use physically based rendering for increased realism. METHODS: Using a virtual reality simulator that replicates our robotic setup, three synthetic image databases with an increasing level of realism were generated: flat, basic, and realistic (using the physically-based rendering). Each of those databases was used to train 20 instances of a UNet-based semantic-segmentation deep-learning model. The networks trained with only synthetic images were evaluated on the segmentation of 160 endoscopic images of a phantom. The networks were compared using the Dwass-Steel-Critchlow-Fligner nonparametric test. RESULTS: Our results show that the levels of realism increased the mean intersection-over-union (mIoU) of the networks on endoscopic images of a phantom ([Formula: see text]). The median mIoU values were 0.235 for the flat dataset, 0.458 for the basic, and 0.729 for the realistic. All the networks trained with synthetic images outperformed naive classifiers. Moreover, in an ablation study, we show that the mIoU of physically based rendering is superior to texture mapping ([Formula: see text]) of the instrument (0.606), the background (0.685), and the background and instruments combined (0.672). CONCLUSIONS: Using physical-based rendering to generate synthetic images is an effective approach to improve the training of neural networks for the semantic segmentation of surgical instruments in endoscopic images. Our results show that this strategy can be an essential step in the broad applicability of deep neural networks in semantic segmentation tasks and help bridge the domain gap in machine learning.

Validation of a wrist-type home nocturnal blood pressure monitor in the sitting and supine position according to the ANSI/AAMI/ISO81060-2:2013 guidelines: Omron HEM-9601T.

This study aimed to validate the accuracy of the Omron HEM-9601T, an automatic wrist-type device for self-blood pressure (BP) measurement with a timer function for automatic measurement of nocturnal BP, in the sitting position according to the American National Standards Institute/Association for the Advancement of Medical Instrumentation/International Organization for Standardization (ANSI/AAMI/ISO) 81060-2:2013 guidelines, and to assess its performance in the supine position by applying the same protocol as conducted in the sitting position. The mean differences between the reference BPs and HEM-9601T readings were 1.2 ± 6.9/1.1 ± 5.5 mmHg, 2.2 ± 6.5/1.8 ± 5.7 mmHg, 0.1 ± 6.6/1.5 ± 6.2 mmHg, and -0.8 ± 7.2/0.5 ± 6.4 mmHg for systolic BP/diastolic BP for criterion 1 in the sitting position, supine with sideways palm position, supine with upward palm position, and supine with downward palm position, respectively. In addition, the mean differences and their standard deviations for systolic BP and diastolic BP calculated according to criterion 2 in the ANSI/AAMI/ISO 81060-2:2013 guidelines were acceptable in all four positions. In conclusion, the Omron HEM-9601T fulfilled the validation criteria of the ANSI/AAMI/ISO81060-2:2013 guidelines when used in the sitting position with the wrist at heart level, and its accuracy in the supine position was acceptable and roughly equivalent to that in the sitting position. The wrist-type home BP monitor could be a more suitable tool for repeated nocturnal BP measurements at home than upper-arm devices, and could improve the reliability of diagnosis and management of nocturnal hypertension.

Real-time surgical needle detection using region-based convolutional neural networks.

OBJECTIVE: Conventional surgical assistance and skill analysis for suturing mostly focus on the motions of the tools. As the quality of the suturing is determined by needle motions relative to the tissues, having knowledge of the needle motion would be useful for surgical assistance and skill analysis. As the first step toward demonstrating the usefulness of the knowledge of the needle motion, we developed a needle detection algorithm. METHODS: Owing to the small needle size, attaching sensors to it is difficult. Therefore, we developed a real-time video-based needle detection algorithm using a region-based convolutional neural network. RESULTS: Our method successfully detected the needle with an average precision of 89.2%. The needle was robustly detected even when the needle was heavily occluded by the tools and/or the blood vessels during microvascular anastomosis. However, there were some incorrect detections, including partial detection. CONCLUSION: To the best of our knowledge, this is the first time deep neural networks have been applied to real-time needle detection. In the future, we will develop a needle pose estimation algorithm using the predicted needle location toward computer-aided surgical assistance and surgical skill analysis.

SmartArm: Integration and validation of a versatile surgical robotic system for constrained workspaces.

BACKGROUND: With the increasing presence of surgical robots minimally invasive surgery, there is a growing necessity of a versatile surgical system for deep and narrow workspaces. METHODS: We developed a versatile system for constrained workspaces called SmartArm. It has two industrial-type robotic arms with flexible tools attached to its distal tip, with a total of nine active degrees-of-freedom. The system has a control algorithm based on constrained optimization that allows the safe generation of task constraints and intuitive teleoperation. RESULTS: The SmartArm system is evaluated in a master-slave experiment in which a medically untrained user operates the robot to suture the dura mater membrane at the skull base of a realistic head phantom. Our results show that the user could accomplish the task proficiently, with speed and accuracy comparable to manual suturing by surgeons. Conclusions We demonstrated the integration and validation of the SmartArm.

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.

Development of a Spherical Model with a 3D Microchannel: An Application to Glaucoma Surgery.

Three-dimensional (3D) microfluidic channels, which simulate human tissues such as blood vessels, are useful in surgical simulator models for evaluating surgical devices and training novice surgeons. However, animal models and current artificial models do not sufficiently mimic the anatomical and mechanical properties of human tissues. Therefore, we established a novel fabrication method to fabricate an eye model for use as a surgical simulator. For the glaucoma surgery task, the eye model consists of a sclera with a clear cornea; a 3D microchannel with a width of 200-500 µm, representing the Schlemm's canal (SC); and a thin membrane with a thickness of 40-132 µm, representing the trabecular meshwork (TM). The sclera model with a clear cornea and SC was fabricated by 3D molding. Blow molding was used to fabricate the TM to cover the inner surface of the sclera part. Soft materials with controllable mechanical behaviors were used to fabricate the sclera and TM parts to mimic the mechanical properties of human tissues. Additionally, to simulate the surgery with constraints similar to those in a real operation, the eye model was installed on a skull platform. Therefore, in this paper, we propose an integration method for fabricating an eye model that has a 3D microchannel representing the SC and a membrane representing the TM, to develop a glaucoma model for training novice surgeons.

Validation of an automatic device for the self-measurement of blood pressure in sitting and supine positions according to the ANSI/AAMI/ISO81060-2: 2013 guidelines: the Omron HEM-9700T.

AIM: We evaluated the performance of the Omron HEM-9700T, an automatic upper-arm-type device for the self-measurement of blood pressure (BP), in the sitting and supine positions according to the ANSI/AAMI/ISO81060-2:2013 guidelines. PARTICIPANTS AND METHODS: We screened 106 participants for the validation study in both positions. After excluding 21 participants, a total of 85 participants [38 (44.7%) men; 47 (55.3%) women] with mean±SD age of 54.5±12.2 years (range: 21-78 years) were included. Their arm circumference was 27.5±1.4cm (range: 17.9-40.8cm). The arm circumference distribution met the ANSI/AAMI/ISO81060-2:2013 requirement. The participants' BP measurements alternated between the use of a conventional mercury sphygmomanometer and the automatic device according to the ANSI/AAMI/ISO81060-2:2013 guidelines to measure their BP. RESULTS: The mean differences between reference BPs and HEM-9700T readings in the sitting and supine positions were -0.6±6.4/1.3±5.5 and -1.0±6.2/1.9±5.4mmHg for systolic BP/diastolic BP, respectively. CONCLUSION: The Omron HEM-9700T fulfilled the validation criteria of the ANSI/AAMI/ISO81060-2:2013 guidelines for both the sitting and supine positions.

Validation of two watch-type wearable blood pressure monitors according to the ANSI/AAMI/ISO81060-2:2013 guidelines: Omron HEM-6410T-ZM and HEM-6410T-ZL.

There is growing evidence of the clinical significance of daytime masked hypertension (MHT) and blood pressure (BP) variability (BPV). Recently, watch-type wearable devices for self-BP measurement have become available. Such devices might be promising tools to identify patients with daytime MHT or large BPV in their real-life conditions. The present study aimed to validate the accuracy of the Omron HEM-6410T-ZM and the Omron HEM-6410T-ZL, which are automatic watch-type wearable devices for self-BP measurement, according to the American National Standards Institute, Inc/Association for the Advancement of Medical Instrumentation/International Organization for Standardization (ANSI/AAMI/ISO) 81060-2:2013 guideline. Watches were held with the wrist at heart level. The mean differences between reference BPs and HEM-6410T-ZM readings were -0.9 ± 7.6/-1.1 ± 6.1 mm Hg for systolic BP (SBP)/diastolic BP (DBP) for criterion 1, and -0.9 ± 6.8/-1.1 ± 5.5 mm Hg for SBP/DBP for criterion 2. The mean differences between reference BPs and HEM-6410T-ZL readings were 2.4 ± 7.3/0.7 ± 7.0 mm Hg for SBP/DBP for criterion 1, and 2.4 ± 6.5/0.7 ± 6.5 mm Hg for SBP/DBP for criterion 2. The Omron HEM-6410T-ZM and the Omron HEM-6410T-ZL both fulfilled both validation criteria 1 and 2 of the ANSI/AAMI/ISO 81060-2:2013 guidelines.

Validation of a wrist-type home nocturnal blood pressure monitor in the sitting and supine position according to the ANSI/AAMI/ISO81060-2:2013 guidelines: Omron HEM-9600T.

The purpose of the present study was to evaluate the performance of the Omron HEM-9600T, an automatic wrist-type device for self BP measurement, in the sitting position with the wrist at heart level and supine position according to the ANSI/AAMI/ISO81060-2:2013 guidelines. In the supine position, we evaluated the device under 3 different conditions: using the supine with sideways palm position, the supine with upwards palm position, and the supine with downwards palm position. After 106 subjects were screened and 21 subjects were excluded, the same 85 subjects (38 men [44.7%] and 47 women [55.3%]) were included in the analyses for each position. The average age of the subjects was 54.5 ± 12.2 years (mean ± SD). The mean wrist circumference was 17.0 ± 2.4 cm. The wrist size distribution fulfilled the requirements of the guidelines. The mean differences between reference BPs and HEM-9600T readings were 1.0 ± 6.7/1.4 ± 5.7 mm Hg, 6.6 ± 7.2/5.5 ± 6.0 mm Hg, 4.8 ± 7.2/4.9 ± 5.8 mm Hg, and 2.1 ± 7.2/2.8 ± 6.8 mm Hg for SBP/DBP in the sitting position, supine with sideways palm position, supine with upwards palm position, and supine with downwards palm position, respectively. In conclusion, the Omron HEM-9600T in the sitting position fulfilled the validation criteria of the ANSI/AAMI/ISO81060-2:2013 guidelines. On the other hand, the accuracies of HEM-9600T in the supine position differed depending on the positioning of the palm, with only the downwards palm-position measurement fulfilling both validation criteria of the ANSI/AAMI/ISO81060-2:2013 guidelines.

Virtual reality simulation of robotic transsphenoidal brain tumor resection: Evaluating dynamic motion scaling in a master-slave system.

BACKGROUND: Integrating simulators with robotic surgical procedures could assist in designing and testing of novel robotic control algorithms and further enhance patient-specific pre-operative planning and training for robotic surgeries. METHODS: A virtual reality simulator, developed to perform the transsphenoidal resection of pituitary gland tumours, tested the usability of robotic interfaces and control algorithms. It used position-based dynamics to allow soft-tissue deformation and resection with haptic feedback; dynamic motion scaling control was also incorporated into the simulator. RESULTS: Neurosurgeons and residents performed the surgery under constant and dynamic motion scaling conditions (CMS vs DMS). DMS increased dexterity and reduced the risk of damage to healthy brain tissue. Post-experimental questionnaires indicated that the system was well-evaluated by experts. CONCLUSION: The simulator was intuitively and realistically operated. It increased the safety and accuracy of the procedure without affecting intervention time. Future research can investigate incorporating this simulation into a real micro-surgical robotic system.