Force Control Issues in Upper and Lower Limbs in Parkinson's Disease and Freezing of Gait.

Parkinson's Disease (PD) has been found to cause force control deficits in upper and lower limbs. About 50% of patients with advanced PD develop a debilitating symptom called freezing of gait (FOG), which has been linked to force control problems in the lower limbs, and some may only have a limited response to the gold standard pharmaceutical therapy, levodopa, resulting in partially levodopa-responsive FOG (PLR-FOG). There has been limited research on investigating upper-limb force control in people with PD with PLR-FOG, and without FOG. In this pilot study, force control was explored using an upper-and-lower-limb haptics-enabled robot in a reaching task while people with PD with and without PLR-FOG were on their levodopa medication. A healthy control group was used for reference, and each cohort completed the task at three different levels of assistance provided by the robot. Similar significant proportional force control deficits were found in the upper and lower limbs in patients with PLR-FOG versus those without FOG. Some aspects of force control were found to be retained, including an ability to increase or decrease force in response to changes in resistance while completing a reaching task. Overall, these results suggest there are force control deficits in both the upper and lower limbs in people with PLR-FOG.

Image-guided surgical planning of percutaneous nephrolithotomy with patient-specific CTRs.

PURPOSE: We propose the utilization of patient-specific concentric-tube robots (CTRs) whose designs are optimized to enhance their volumetric reachability of the renal stone, thus reducing the morbidities associated with percutaneous nephrolithotomy procedures. By employing a nested optimization-driven scheme, this work aims to determine a single surgical tract through which the patient-tailored CTR is deployed. We carry out a sensitivity analysis on the combined percutaneous access and optimized CTR design with respect to breathing-induced excursion of the kidneys based on preoperative images. Further, an investigation is also performed of the appropriateness and effectiveness of the percutaneous access provided by the proposed algorithm compared to that of an expert urologist. METHODS: The method is based on an ellipsoidal approximation to the renal calculi and a grid search over candidate skin areas and available renal calyces using an anatomically constrained kinematic mapping of the CTR. Percutaneous access is selected for collision-free CTR deployment to the centroid of the stones with minimal positional error at the renal calyx. Further optimization of the CTR design results in a robot tailored to the therapeutic anatomical features of each clinical case. The study examined 14 sets of clinical data of PCNL patients, analyzing stone reachability using preoperative images and breathing-induced motions of the kidney. An experienced urologist qualitatively assessed the adequacy of percutaneous access generated by the algorithm. RESULTS: An assessment conducted by an expert urologist found that the percutaneous accesses produced by the proposed approach were found to be comparable to those chosen by the expert surgeon in most clinical cases. The simulated results demonstrated a mean volume coverage of [Formula: see text] for static anatomy and [Formula: see text] and [Formula: see text] when considering a 1 cm excursion of the kidney in the craniocaudal directions due to respiration or tool-tissue interaction. CONCLUSION: The optimization-driven scheme for determining a single tract surgical plan, coupled with the use of a patient-specific CTR, shows promising results for improving percutaneous access in PCNL procedures. This approach clearly shows the potential for enhancing the quality and suitability of percutaneous accesses, addressing the challenges posed by staghorn and non-staghorn stones during PCNL procedures. Further research involving clinical validation is necessary to confirm these findings and explore the potential clinical benefits of the approach.

Robotics-Based Characterization of Sensorimotor Integration in Parkinson's Disease and the Effect of Medication.

Integration of multi-modal sensory inputs and modulation of motor outputs based on perceptual estimates is called Sensorimotor Integration (SMI). Optimal functioning of SMI is essential for perceiving the environment, modulating the motor outputs, and learning or modifying motor skills to suit the demands of the environment. Growing evidence suggests that patients diagnosed with Parkinson's Disease (PD) may suffer from an impairment in SMI that contributes to perceptual deficits, leading to motor abnormalities. However, the exact nature of the SMI impairment is still unclear. This study uses a robot-assisted assessment tool to quantitatively characterize SMI impairments in PD patients and how they affect voluntary movements. A set of assessment tasks was developed using a robotic manipulandum equipped with a virtual-reality system. The sensory conditions of the virtual environment were varied to facilitate the assessment of SMI. A hundred PD patients (before and after medication) and forty-three control subjects completed the tasks under varying sensory conditions. The kinematic measures obtained from the robotic device were used to evaluate SMI. The findings reveal that across all sensory conditions, PD patients had 36% higher endpoint error, 38% higher direction error in reaching tasks, and 43% higher number of violations in tracing tasks than control subjects due to impairment in integrating sensory inputs. However, they still retained motor learning ability and the ability to modulate motor outputs. The medication worsened the SMI deficits as PD patients, after medication, performed worse than before medication when encountering dynamic sensory environments and exhibited impaired motor learning ability.

Visual velocity perception dysfunction in Parkinson's disease.

OBJECTIVE: Compared with motor deficits, sensory information processing in Parkinson's disease (PD) is relatively unexplored. While there is increasing interest in understanding the sensory manifestations of PD, the extent of sensory abnormality in PD has remained relatively unexplored. Furthermore, most investigations on the sensory aspects of PD involve motor aspects, causing confounding results. As sensory deficits often arise in early PD development stages, they present a potential technological target for diagnosis and disease monitoring that is affordable and accessible. Considering this, the current study's aim is to assess visual spatiotemporal perception independent of goal directed movements in PD by designing and using a scalable computational tool. METHODS: A flexible 2-D virtual reality environment was created to evaluate various cases of visual perception. Using the tool, an experimental task quantifying the visual perception of velocity was tested on 37 individuals with PD and 17 age-matched control participants. RESULTS: PD patients, both ON and OFF PD therapy, displayed perceptual impairments (p = 0.001 and p = 0.008, respectively) at slower tested velocity magnitudes. These impairments were even observed in early stages of PD (p = 0.015). CONCLUSION: Visual velocity perception is impaired in PD patients, which suggests impairments in visual spatiotemporal processing occur in PD and provides a promising modality to be used with disease monitoring software. SIGNIFICANCE: Visual velocity perception shows high sensitivity to PD at all stages of the disease. Dysfunction in visual velocity perception may contribute to observed motor dysfunction in PD.

Robot-assisted investigation of sensorimotor control in Parkinson's disease.

Sensorimotor control (SMC) is a complex function that involves sensory, cognitive, and motor systems working together to plan, update and execute voluntary movements. Any abnormality in these systems could lead to deficits in SMC, which would negatively impact an individual's ability to execute goal-directed motions. Recent studies have shown that patients diagnosed with Parkinson's disease (PD) have dysfunctions in sensory, motor, and cognitive systems, which could give rise to SMC deficits. However, SMC deficits in PD and how they affect a patient's upper-limb movements have not been well understood. The objective of the study was to investigate SMC deficits in PD and how they affect the planning and correction of upper-limb motions. This was accomplished using a robotic manipulandum equipped with a virtual-reality system. Twenty age-matched healthy controls and fifty-six PD patients (before and after medication) completed an obstacle avoidance task under dynamic conditions (target and obstacles in moving or stationary form, with and without mechanical perturbations). Kinematic information from the robot was used to extract eighteen features that evaluated the SMC functions of the participants. The findings show that the PD patients before medication were 32% slower, reached 16% fewer targets, hit 41% more obstacles, and were 26% less efficient than the control participants, and the difference in these features was statistically significant under dynamic conditions. In addition to the motor deficits, the PD patients also showed deficits in handling high cognitive loads and interpreting sensory cues. Further, the PD patients after medication exhibited worse sensory and cognitive performance than before medication under complex testing conditions. The PD patients also showed deficits in following the computational models leading to poor motor planning.

Video Context Improves Performance in Identifying Operative Planes on Static Surgical Images.

BACKGROUND: Correct identification of the surgical tissue planes of dissection is paramount at the operating room, and the needed skills seem to be improved with realistic dynamic models rather than mere still images. The objective is to assess the role of adding video prequels to still images taken from operations on the precision and accuracy of tissue plane identification using a validated simulation model, considering various levels of surgeons' experience. METHODS: A prospective observational study was conducted involving 15 surgeons distributed to three equal groups, including a consultant group [C], a senior group [S], and a junior group [J]. Subjects were asked to identify and draw ideal tissue planes in 20 images selected at suitable operative moments of identification before and after showing a 10- second videoclip preceding the still image. A validated comparative metric (using a modified Hausdorff distance [%Hdu] for object matching) was used to measure the distance between lines. A precision analysis was carried out based on the difference in %Hdu between lines drawn before and after watching the videos, and between-group comparisons were analyzed using a one-way analysis of variance (ANOVA). The analysis of accuracy was done on the difference in %Hdu between lines drawn by the subjects and the ideal lines provided by an expert panel. The impact of videos on accuracy was assessed using a repeated-measures ANOVA. RESULTS: The C group showed the highest preciseness as compared to the S and J groups (mean Hdu 9.17±11.86 versus 12.1±15.5 and 20.0±18.32, respectively, p <0.001) and significant differences between groups were found in 14 images (70%). Considering the expert panel as a reference, the interaction between time and experience level was significant ( F (2, 597) = 4.52, p <0.001). Although the subjects of the J group were significantly less accurate than other surgeons, only this group showed significant improvements in mean %Hdu values after watching the lead-in videos ( F (1, 597) = 6.04, p = 0.014). CONCLUSIONS: Adding video context improved the ability of junior trainees to identify tissue planes of dissection. A realistic model is recommended considering experience-based differences in precision in training programs.

Texture-based Intraoperative Image Guidance for Tumor Localization in Minimally Invasive Surgery.

Intraoperative tumor localization in a deflated lung in minimally invasive surgery (MIS) is challenging as the lung cannot be manually palpated through small incisions. To do so remotely, an articulated multisensory imaging device combining tactile and ultrasound sensors was developed. It visualizes the surface tactile map and the depth of the tissue. However, with little maneuverability in MIS, localizing tumors using instrumented palpation is both tedious and inefficient. In this paper, a texture- based image guidance system that classifies tactile-guided ultrasound texture regions and provides beliefs on their types is proposed. The resulting interactive feedback allows directed palpation in MIS. A k-means classifier is used to first cluster gray-level co-occurrence matrix (GLCM)-based texture features of the ultrasound regions, followed by hidden Markov model-based belief propagation to establish confidence about the clustered features observing repeated patterns. When the beliefs converge, the system autonomously detects tumor and nontumor textures. The approach was tested on 20 ex vivo soft tissue specimens in a staged MIS. The results showed that with guidance, tumors in MIS could be localized with 98% accuracy, 99% sensitivity, and 97% specificity.Clinical Relevance- Texture-based image guidance adds efficiency and control to instrumented palpation in MIS. It renders fluidity and accuracy in image acquisition using a hand-held device where fatigue from prolonged handling affects imaging quality.

Abnormal Vision-Based Displacement Perception in Parkinson's Disease.

In this work, we investigate the effect of Parkinson's disease (PD), and common corresponding therapies on vision-based perception of motion, a critical perceptual ability required for performing a wide range of activities of daily livings. While PD has been recognized as mainly a motor disorder, sensory manifestation of PD can also play a major role in the resulting disability. In this paper, for the first time, the effect of disease duration and common therapies on vision-based perception of displacement were investigated. The study is conducted in a movement-independent manner, to reject the shadowing effects and isolate the targeted perceptual disorder to the maximum possible extent. Data was collected using a computerized graphical tool on 37 PD patients [6 early-stage de novo, 25 mid-stage using levodopa therapy, six later-stage using deep brain stimulation (DBS)] and 15 control participants. Besides the absolute measurement of perception through a psychometric analysis on two tested position reference magnitudes, we also investigated the linearity in perception using Weber's fraction. The results showed that individuals with PD displayed significant perceptual impairments compared to controls, though early-stage patients were not impaired. Mid-stage patients displayed impairments at the greater of the two tested reference magnitudes, while late-stage patients were impaired at both reference magnitudes. Levodopa and DBS use did not cause statistically significant differences in absolute displacement perception. The findings suggest abnormal visual processing in PD increasing with disease development, perhaps contributing to sensory-based impairments of PD such as bradykinesia, visuospatial deficits, and abnormal object recognition.

A deep explainable artificial intelligent framework for neurological disorders discrimination.

Pathological hand tremor (PHT) is a common symptom of Parkinson's disease (PD) and essential tremor (ET), which affects manual targeting, motor coordination, and movement kinetics. Effective treatment and management of the symptoms relies on the correct and in-time diagnosis of the affected individuals, where the characteristics of PHT serve as an imperative metric for this purpose. Due to the overlapping features of the corresponding symptoms, however, a high level of expertise and specialized diagnostic methodologies are required to correctly distinguish PD from ET. In this work, we propose the data-driven [Formula: see text] model, which processes the kinematics of the hand in the affected individuals and classifies the patients into PD or ET. [Formula: see text] is trained over 90 hours of hand motion signals consisting of 250 tremor assessments from 81 patients, recorded at the London Movement Disorders Centre, ON, Canada. The [Formula: see text] outperforms its state-of-the-art counterparts achieving exceptional differential diagnosis accuracy of [Formula: see text]. In addition, using the explainability and interpretability measures for machine learning models, clinically viable and statistically significant insights on how the data-driven model discriminates between the two groups of patients are achieved.

Robotics and AI for Teleoperation, Tele-Assessment, and Tele-Training for Surgery in the Era of COVID-19: Existing Challenges, and Future Vision.

The unprecedented shock caused by the COVID-19 pandemic has severely influenced the delivery of regular healthcare services. Most non-urgent medical activities, including elective surgeries, have been paused to mitigate the risk of infection and to dedicate medical resources to managing the pandemic. In this regard, not only surgeries are substantially influenced, but also pre- and post-operative assessment of patients and training for surgical procedures have been significantly impacted due to the pandemic. Many countries are planning a phased reopening, which includes the resumption of some surgical procedures. However, it is not clear how the reopening safe-practice guidelines will impact the quality of healthcare delivery. This perspective article evaluates the use of robotics and AI in 1) robotics-assisted surgery, 2) tele-examination of patients for pre- and post-surgery, and 3) tele-training for surgical procedures. Surgeons interact with a large number of staff and patients on a daily basis. Thus, the risk of infection transmission between them raises concerns. In addition, pre- and post-operative assessment also raises concerns about increasing the risk of disease transmission, in particular, since many patients may have other underlying conditions, which can increase their chances of mortality due to the virus. The pandemic has also limited the time and access that trainee surgeons have for training in the OR and/or in the presence of an expert. In this article, we describe existing challenges and possible solutions and suggest future research directions that may be relevant for robotics and AI in addressing the three tasks mentioned above.

Intraoperative Localization of STN During DBS Surgery Using a Data-Driven Model.

A new approach is presented for localizing the Subthalamic Nucleus (STN) during Deep Brain Stimulation (DBS) surgery based on microelectrode recordings (MERs). DBS is an accepted treatment for individuals living with Parkinson's Disease (PD). This surgery involves implantation of a permanent electrode inside the STN to deliver electrical current. Since the STN is a very small region inside the brain, accurate placement of an electrode is a challenging task for the surgical team. Prior to placement of the permanent electrode, microelectrode recordings of brain activity are used intraoperatively to localize the STN. The placement of the electrode and the success of the therapy depend on this location. In this paper, an objective approach is implemented to help the surgical team in localizing the STN. This is achieved by processing the MER signals and extracting features during the surgery to be used in a Machine Learning (ML) algorithm for defining the neurophysiological borders of the STN. For this purpose, a new classification approach is proposed with the goal of detecting both the dorsal and the ventral borders of the STN during the surgical procedure. Results collected from 100 PD patients in this study, show that by calculating and extracting wavelet transformation features from MER signals and using a data-driven computational deep neural network model, it is possible to detect the borders of the STN with an accuracy of 92%. The proposed method can be implemented in real-time during the surgery to model the neurophysiological nonlinearity along the path of the electrode trajectory during insertion.

PHTNet: Characterization and Deep Mining of Involuntary Pathological Hand Tremor using Recurrent Neural Network Models.

The global aging phenomenon has increased the number of individuals with age-related neurological movement disorders including Parkinson's Disease (PD) and Essential Tremor (ET). Pathological Hand Tremor (PHT), which is considered among the most common motor symptoms of such disorders, can severely affect patients' independence and quality of life. To develop advanced rehabilitation and assistive technologies, accurate estimation/prediction of nonstationary PHT is critical, however, the required level of accuracy has not yet been achieved. The lack of sizable datasets and generalizable modeling techniques that can fully represent the spectrotemporal characteristics of PHT have been a critical bottleneck in attaining this goal. This paper addresses this unmet need through establishing a deep recurrent model to predict and eliminate the PHT component of hand motion. More specifically, we propose a machine learning-based, assumption-free, and real-time PHT elimination framework, the PHTNet, by incorporating deep bidirectional recurrent neural networks. The PHTNet is developed over a hand motion dataset of 81 ET and PD patients collected systematically in a movement disorders clinic over 3 years. The PHTNet is the first intelligent systems model developed on this scale for PHT elimination that maximizes the resolution of estimation and allows for prediction of future and upcoming sub-movements.

Differential Temporal Perception Abilities in Parkinson's Disease Patients Based on Timing Magnitude.

Non-motor symptoms in Parkinson's Disease (PD) predate motor symptoms and substantially decrease quality of life; however, detection, monitoring, and treatments are unavailable for many of these symptoms. Temporal perception abnormalities in PD are generally attributed to altered Basal Ganglia (BG) function. Present studies are confounded by motor control facilitating movements that are integrated into protocols assessing temporal perception. There is uncertainty regarding the BG's influence on timing processes of different time scales and how PD therapies affect this perception. In this study, PD patients using Levodopa (n = 25), Deep Brain Stimulation (DBS; n = 6), de novo patients (n = 6), and healthy controls (n = 17) completed a visual temporal perception task in seconds and sub-section timing scales using a computer-generated graphical tool. For all patient groups, there were no impairments seen at the smaller tested magnitudes (using sub-second timing). However, all PD groups displayed significant impairments at the larger tested magnitudes (using interval timing). Neither Levodopa nor DBS therapy led to significant improvements in timing abilities. Levodopa resulted in a strong trend towards impairing timing processes and caused a deterioration in perceptual coherency according to Weber's Law. It is shown that timing abnormalities in PD occur in the seconds range but do not extend to the sub-second range. Furthermore, observed timing deficits were shown to not be solely caused by motor deficiency. This provides evidence to support internal clock models involving the BG (among other neural regions) in interval timing, and cerebellar control of sub-second timing. This study also revealed significant temporal perception deficits in recently diagnosed PD patients; thus, temporal perception abnormalities might act as an early disease marker, with the graphical tool showing potential for disease monitoring.

Visual Displacement Perception in Parkinson's Disease Analyzed Using a Computer-Generated Graphical Tool.

Parkinson's Disease (PD) is typically classified by the onset of motor impairments, however, non-motor symptoms are also present in all disease stages. Vision abnormalities contribute to the non-motor PD deficits, yet little research has studied how PD affects visual perceptions with no produced motor responses. This provides motivation for the current study which focuses on examining allocentric visual displacement perception - information used for object identification - in PD patients. To study this PD participants OFF and ON Levodopa therapy, and age-matched healthy control participants were tested. A modular graphics toolbox was implemented to carry out the perceptual testing. Individuals with PD were shown to have impairments in displacement perception of the larger tested magnitudes when both OFF and ON Levodopa compared to control participants, suggesting impairments in visual displacement processing pathways. These abnormalities could contribute to difficulties some PD patients have with visual recognition and visuospatial navigation. Furthermore, the study validated the graphical tool as a means of quantifying perceptual abilities that can be expanded to many perceptual modalities and paired with robotic devices.

Robotics-Assisted Surgical Skills Evaluation based on Electrocortical Activity.

Skills assessment in Robotics-Assisted Minimally Invasive Surgery (RAMIS) is mainly performed based on temporal, motion-based and outcome-based metrics. While these components are essential for the proper assessment of skills in RAMIS, they do not suffice for full representation of all underlying aspects of skilled performance. Besides such commonplace components of skills, there exist other elements to be taken into account for comprehensive skills assessment. Among such elements are cognitive states (such as levels of stress, attention, concentration) that can directly affect performance. Investigating the impact of electrocortical activity and cognitive states of RAMIS surgeons over their performance has, however, received little attention in the literature. Therefore, in this paper, novel performance metrics based on electroencephalography (EEG) signals are studied for potential augmentation into RAMIS training and its assessment platform. For this purpose, a user study was conducted involving 23 novices and 9 expert RAMIS surgeons. The participants were asked to perform two tasks on the dv-Trainer®, (Mimic Technologies) RAMIS simulator, while their brain EEG signals were being measured using the Muse EEG headband (InteraXon Inc.). The performance metrics were defined as mean values of band powers of EEG signals over various ranges of frequency. Statistical analysis was performed to evaluate metrics over 5 different ranges of frequency for 4 electrode locations and during 2 RAMIS training tasks. The results indicated statistically significant differences in electrocortical activity between novices and experts in temporoparietal and left frontal regions of their brain for mid to high-frequency ranges. Overall, RAMIS experts showed lower levels of electrocortical activity in those regions compared to novices. The results indicate that electrocortical activity measured by EEG signals have the potential to provide useful information for skills assessment in RAMIS.

Toward Lung Tumor Localization based on Strain Variability of Lung Surface during Video-Assisted Thoracoscopic Surgery.

Tumor localization, especially in case of minimally invasive lung tumor resection surgery, is extremely challenging due to the continuous motion of the organ. This motion can be troublesome as it results in spatial discrepancy corresponding to preoperative and intraoperative tumor location. In order to characterize lung tissue stiffness for the purpose of lung tumor localization, in this paper, we present a novel characterization approach based on variability in resistance of the healthy region vs. the tumorous region resulting from lung motion. The proposed approach is numerically validated on a Finite Element (FE) model of the lung with varying surface stiffnesses, where higher stiffness represents tumor and lower stiffness corresponds to healthy lung tissue. The numerical simulation validates the sensitivity of our mechanism for different grades of tumors by demonstrating that the strain on the healthy tissue is 31.8 and 67.1 times higher than that on the tumor surface for a selected relative stiffness variation of 3.6x and 24.4x respectively, at a pressure of 1.6 KPa. Additionally, a framework is developed to validate the proposed approach in a video of a video-assisted thoracoscopic surgery (VATS), where multiple landmarks on the lung surface are tracked. This enables us to quantify the motion of points residing on healthy surface and tumorous surface. The motion data is further analyzed to study the relative surface strain, and it is shown that the proposed approach differentiates a tumor from healthy surface.

Development of a physical shoulder simulator for the training of basic arthroscopic skills.

BACKGROUND: Orthopaedic training programs are incorporating arthroscopic simulations into their residency curricula. There is a need for a physical shoulder simulator that accommodates lateral decubitus and beach chair positions, has realistic anatomy, allows for an objective measure of performance and provides feedback to trainees. METHODS: A physical shoulder simulator was developed for training basic arthroscopic skills. Sensors were embedded in the simulator to provide a means to assess performance. Subjects of varying skill level were invited to use the simulator and their performance was objectively assessed. RESULTS: Novice subjects improved their performance after practice with the simulator. A survey completed by experts recognized the simulator as a valuable tool for training basic arthroscopic skills. CONCLUSIONS: The physical shoulder simulator helps train novices in basic arthroscopic skills and provides objective measures of performance. By using the physical shoulder simulator, residents could improve their basic arthroscopic skills, resulting in improved patient safety.

Analysis of Energy-Based Metrics for Laparoscopic Skills Assessment.

OBJECTIVE: The complexity of minimally invasive surgery (MIS) requires that trainees practice MIS skills in numerous training sessions. The goal of these training sessions is to learn how to move the instruments smoothly without damaging the surrounding tissue and achieving operative tasks with accuracy. In order to enhance the efficiency of these training sessions, the proficiency of the trainees should be assessed using an objective assessment method. Several performance metrics have been proposed and analyzed for MIS tasks. The differentiation of various levels of expertise is limited without the presence of an external evaluator. METHODS: In this study, novel objective performance metrics are proposed based on mechanical energy expenditure and work. The three components of these metrics are potential energy, kinetic energy, and work. These components are optimally combined through both one-step and two-step methods. Evaluation of these metrics is accomplished for suturing and knot-tying tasks based on the performance of 30 subjects across four levels of experience. RESULTS: The results of this study show that the one-step combined metric provides 47 and 60 accuracy in determining the level of expertise of subjects for the suturing and knot-tying tasks, respectively. The two-step combined metric provided 67 accuracy for both of the tasks studied. CONCLUSION: The results indicate that energy expenditure is a useful metric for developing objective and efficient assessment methods. SIGNIFICANCE: These metrics can be used to evaluate and determine the proficiency levels of trainees, provide feedback and, consequently, enhance surgical simulators.

Energy-Based Metrics for Arthroscopic Skills Assessment.

Minimally invasive skills assessment methods are essential in developing efficient surgical simulators and implementing consistent skills evaluation. Although numerous methods have been investigated in the literature, there is still a need to further improve the accuracy of surgical skills assessment. Energy expenditure can be an indication of motor skills proficiency. The goals of this study are to develop objective metrics based on energy expenditure, normalize these metrics, and investigate classifying trainees using these metrics. To this end, different forms of energy consisting of mechanical energy and work were considered and their values were divided by the related value of an ideal performance to develop normalized metrics. These metrics were used as inputs for various machine learning algorithms including support vector machines (SVM) and neural networks (NNs) for classification. The accuracy of the combination of the normalized energy-based metrics with these classifiers was evaluated through a leave-one-subject-out cross-validation. The proposed method was validated using 26 subjects at two experience levels (novices and experts) in three arthroscopic tasks. The results showed that there are statistically significant differences between novices and experts for almost all of the normalized energy-based metrics. The accuracy of classification using SVM and NN methods was between 70% and 95% for the various tasks. The results show that the normalized energy-based metrics and their combination with SVM and NN classifiers are capable of providing accurate classification of trainees. The assessment method proposed in this study can enhance surgical training by providing appropriate feedback to trainees about their level of expertise and can be used in the evaluation of proficiency.

Mastery Learning - does the method of learning make a difference in skills acquisition for robotic surgery?

BACKGROUND: Few studies compare the effectiveness of blocked vs random practice conditions in minimally invasive surgery training, and none have evaluated these in robotic surgery training. METHODS: The dV-Trainer® and the da Vinci® Surgical System (dVSS) were used to compare practice conditions. Forty-two participants were randomized into blocked and random practice groups. Each participant performed five tasks: Ring Walk, Thread the Rings, Needle Targeting, Suture Sponge and Tubes Level 2. Transfer to the dVSS was also assessed. RESULTS: No significant differences were observed between the two groups, except for a few instances. For example, during Ring Walk, the random group performed significantly faster than the blocked group (100.78 ± 5.26 s vs 121.59 ± 5.26 s, p < 0.01). CONCLUSIONS: The study results do not follow the current evidence presented in the education literature. This is the first time that blocked versus random practice was tested for robotic surgery training.