Healing Health Care Disparities: Development and Pilot Testing of a Virtual Reality Implicit Bias Training Module for Physicians in the Context of Black Maternal Health.

Grounded in communication models of cultural competence, this study reports on the development and testing of the first module in a larger virtual reality (VR) implicit bias training for physicians to help them better: (a) recognize implicit bias and its effects on communication, patients, and patient care; (b) identify their own implicit biases and exercise strategies for managing them; and (c) learn and practice communicating with BIPOC patients in a culture-centered manner that demonstrates respect and builds trust. Led by communication faculty, a large, interdisciplinary team of researchers, clinicians, and engineers developed the first module tested herein focused on training goal (a). Within the module, participants observe five scenes between patient Marilyn Hayes (a Black woman) and Dr. Richard Flynn (her obstetrician, a White man) during a postpartum visit. The interaction contains examples of implicit bias, and participants are asked to both identify and consider how implicit bias impacts communication, the patient, and patient care. The team recruited 30 medical students and resident physicians to participate in a lab-based study that included a pretest, a training experience of the module using a head-mounted VR display, and a posttest. Following the training, participants reported improved attitudes toward implicit bias instruction, greater importance of determining patients' beliefs and perspectives for history-taking, treatment, and providing quality health care; and greater communication efficacy. Participants' agreement with the importance of assessing patients' perspectives, opinions, and psychosocial and cultural contexts did not significantly change. Implications for medical education about cultural competency and implicit bias are discussed.

Reliability in performance assessment creates a potential application of artificial intelligence in veterinary education: evaluation of suturing skills at a single institution.

OBJECTIVES: To evaluate suturing skills of veterinary students using 3 common performance assessments (PAs) and to compare findings to data obtained by an electromyographic armband. SAMPLE: 16 second-year veterinary students. PROCEDURES: Students performed 4 suturing tasks on synthetic tissue models 1, 3, and 5 weeks after a surgical skills course. Digital videos were scored by 4 expert surgeons using 3 PAs (an Objective Structured Clinical Examination [OSCE]- style surgical binary checklist, an Objective Structured Assessment of Technical Skill [OSATS] checklist, and a surgical Global Rating Scale [GRS]). Surface electromyography (sEMG) data collected from the dominant forearm were input to machine learning algorithms. Performance assessment scores were compared between experts and correlated to task completion times and sEMG data. Inter-rater reliability was calculated using the intraclass correlation coefficient (ICC). Inter-rater agreement was calculated using percent agreement with varying levels of tolerance. RESULTS: Reliability was moderate for the OSCE and OSATS checklists and poor for the GRS. Agreement was achieved for the checklists when moderate tolerance was applied but remained poor for the GRS. sEMG signals did not correlate well with checklist scores or task times, but features extracted from signals permitted task differentiation by routine statistical comparison and correct task classification using machine learning algorithms. CLINICAL RELEVANCE: Reliability and agreement of an OSCE-style checklist, OSATS checklist, and surgical GRS assessment were insufficient to characterize suturing skills of veterinary students. To avoid subjectivity associated with PA by raters, further study of kinematics and EMG data is warranted in the surgical skills evaluation of veterinary students.

Low-Cost UVBot Using SLAM to Mitigate the Spread of Noroviruses in Occupational Spaces.

Noroviruses (NoVs) cause over 90% of non-bacterial gastroenteritis outbreaks in adults and children in developed countries. Therefore, there is a need for approaches to mitigate the transmission of noroviruses in workplaces to reduce their substantial health burden. We developed and validated a low-cost, autonomous robot called the UVBot to disinfect occupational spaces using ultraviolet (UV) lamps. The total cost of the UVBOT is less than USD 1000, which is much lower than existing commercial robots that cost as much as USD 35,000. The user-friendly desktop application allows users to control the robot remotely, check the disinfection map, and add virtual walls to the map. A 2D LiDAR and a simultaneous localization and mapping (SLAM) algorithm was used to generate a map of the space being disinfected. Tulane virus (TV), a human norovirus surrogate, was used to validate the UVBot's effectiveness. TV was deposited on a painted drywall and exposed to UV radiation at different doses. A 3-log (99.9%) reduction of TV infectivity was achieved at a UV dose of 45 mJ/cm2. We further calculated the sanitizing speed as 3.5 cm/s and the efficient sanitizing distance reached up to 40 cm from the UV bulb. The design, software, and environment test data are available to the public so that any organization with minimal engineering capabilities can reproduce the UVBot system.

Classification of Activities of Daily Living Based on Grasp Dynamics Obtained from a Leap Motion Controller.

Stroke is one of the leading causes of mortality and disability worldwide. Several evaluation methods have been used to assess the effects of stroke on the performance of activities of daily living (ADL). However, these methods are qualitative. A first step toward developing a quantitative evaluation method is to classify different ADL tasks based on the hand grasp. In this paper, a dataset is presented that includes data collected by a leap motion controller on the hand grasps of healthy adults performing eight common ADL tasks. Then, a set of features with time and frequency domains is combined with two well-known classifiers, i.e., the support vector machine and convolutional neural network, to classify the tasks, and a classification accuracy of over 99% is achieved.

Upper Limb Home-Based Robotic Rehabilitation During COVID-19 Outbreak.

The coronavirus disease (COVID-19) outbreak requires rapid reshaping of rehabilitation services to include patients recovering from severe COVID-19 with post-intensive care syndromes, which results in physical deconditioning and cognitive impairments, patients with comorbid conditions, and other patients requiring physical therapy during the outbreak with no or limited access to hospital and rehabilitation centers. Considering the access barriers to quality rehabilitation settings and services imposed by social distancing and stay-at-home orders, these patients can be benefited from providing access to affordable and good quality care through home-based rehabilitation. The success of such treatment will depend highly on the intensity of the therapy and effort invested by the patient. Monitoring patients' compliance and designing a home-based rehabilitation that can mentally engage them are the critical elements in home-based therapy's success. Hence, we study the state-of-the-art telerehabilitation frameworks and robotic devices, and comment about a hybrid model that can use existing telerehabilitation framework and home-based robotic devices for treatment and simultaneously assess patient's progress remotely. Second, we comment on the patients' social support and engagement, which is critical for the success of telerehabilitation service. As the therapists are not physically present to guide the patients, we also discuss the adaptability requirement of home-based telerehabilitation. Finally, we suggest that the reformed rehabilitation services should consider both home-based solutions for enhancing the activities of daily living and an on-demand ambulatory rehabilitation unit for extensive training where we can monitor both cognitive and motor performance of the patients remotely.

Convolutional Correlation Analysis for Enhancing the Performance of SSVEP-Based Brain-Computer Interface.

Currently, most of the high-performance models for frequency recognition of steady-state visual evoked potentials (SSVEPs) are linear. However, SSVEPs collected from different channels can have non-linear relationship among each other. Linearly combining electroencephalogram (EEG) from multiple channels is not the most accurate solution in SSVEPs classification. To further improve the performance of SSVEP-based brain-computer interface (BCI), we propose a convolutional neural network-based non-linear model, i.e. convolutional correlation analysis (Conv-CA). Different from pure deep learning models, Conv-CA use convolutional neural networks (CNNs) at the top of a self-defined correlation layer. The CNNs function on how to transform multiple channel EEGs into a single EEG signal. The correlation layer calculates the correlation coefficients between the transformed single EEG signal and reference signals. The CNNs provide non-linear operations to combine EEGs in different channels and different time. And the correlation layer constrains the fitting space of the deep learning model. A comparison study between the proposed Conv-CA method and the task-related component analysis (TRCA) based methods is conducted. Both methods are validated on a 40-class SSVEP benchmark dataset recorded from 35 subjects. The study verifies that the Conv-CA method significantly outperforms the TRCA-based methods. Moreover, Conv-CA has good explainability since its inputs of the correlation layer can be analyzed for visualizing what the model learnt from the data. Conv-CA is a non-linear extension of spatial filters. Its CNN structures can be further explored and tuned for reaching a better performance. The structure of combining neural networks and unsupervised features has the potential to be applied to the classification of other signals.

Hand gesture recognition using surface electromyography.

Surface electromyography has become one of the popular methods for recognizing hand gestures. In this paper, the performance of four classification methods on sEMG signals have been investigated. These methods are developed by combinations of two feature extraction methods, including Mean Absolute Value and Short-Time Fourier Transform, and two classifiers, including Support Vector Machine and Convolutional Neural Network. These classification methods achieved an accuracy over 97 % on the NinaPro dataset 1. In addition, a new dataset, which includes the Activities of Daily Living, was proposed and an accuracy over 98 % was obtained by applying the presented classification methods.This methodology can provide the basis for a robust quantitative technique to evaluate hand grasps of stroke patients in performing activities of daily living that in turn can lead to a more efficient rehabilitation regimen.

Force calibration for an endovascular robotic system with proximal force measurement.

Surgeons, while performing manual endovascular procedures with conventional surgical tools (catheters and guidewires), experience forces on the tool outside the patient's body that are proximal to the point of actuation. Currently, most of the robotic systems for endovascular procedures use active catheters to navigate vasculature and to measure the contact forces at the distal end (tool tip). These tools are more expensive than the conventional surgical tools used in endovascular procedures. To avoid dependence on specialized devices like active catheters, we have developed a novel endovascular robotic system (ERS) that uses conventional surgical tools. Our robot can indirectly measure proximal forces and provide haptic feedback to surgeons. This paper discusses the theory, methodology, and calibration of indirect proximal force measurement. This new calibration technique is presented as a nested optimization problem that is solved using bi-level optimization. The results of experimental validation of the new force calibration methodology are also discussed. The results show that unbiasing of the indirect force measurement by means of force calibration will allow the use of conventional tools in robotic endovascular procedures.

A new implementation for online calculation of manipulator Jacobian.

This paper describes a new implementation for calculating Jacobian and its time derivative for robot manipulators in real-time. The estimation of Jacobian is the key in the real-time implementation of kinematics and dynamics of complex planar or spatial robots with fixed as well as floating axes in which the Jacobian form changes with the structure. The proposed method is suitable for such implementations. The new method is based on matrix differential calculus. Unlike the conventional methods, which are based on screw theory, the Jacobian calculation in the proposed approach has been reduced to the inner product of two matrices. Use of the new method to derive linear and angular velocity parts of Jacobian and its time derivative is described in detail. We have demonstrated the method using a two-DOF spatial robot and a hyper-redundant spatial robot.

Surgical Robot with Environment Reconstruction and Force Feedback.

We present a new surgical robot hardware-in-the-loop simulator, with 3D surgical field reconstruction in RGB-D sensor range, which allows tool-tissue interactions to be presented as haptic feedback and thus provides the situation awareness of unwanted collision. First, the point cloud of the complete surgical environment is constructed from multiple frames of sensor data to avoid the occlusion issue. Then the user selects a region of interest where the robot's tool must avoid (also called forbidden region). The real-time haptic force rendering algorithm computes the interaction force which is then communicated to a haptic device at 1 kHz, to assist the surgeon to perform safe actions. The robot used is a RAVEN II system, RGB-D sensor is used to scan the environment, and two Omni haptic devices provide the 3-DoF haptic force. A registration pipeline is presented to complete the surgical environment point cloud mapping in preoperative surgery planning phase, which improves quality of haptic rendering in the presence of occlusion. Furthermore, we propose a feasible and fast algorithm which extends the existing work on the proxy-based method for haptic rendering between a Haptic Interaction Point (HIP) and a point cloud. The proposed methodology has the potential of improving the safety of surgical robots.

3D printing for preoperative planning and surgical training: a review.

Surgeons typically rely on their past training and experiences as well as visual aids from medical imaging techniques such as magnetic resonance imaging (MRI) or computed tomography (CT) for the planning of surgical processes. Often, due to the anatomical complexity of the surgery site, two dimensional or virtual images are not sufficient to successfully convey the structural details. For such scenarios, a 3D printed model of the patient's anatomy enables personalized preoperative planning. This paper reviews critical aspects of 3D printing for preoperative planning and surgical training, starting with an overview of the process-flow and 3D printing techniques, followed by their applications spanning across multiple organ systems in the human body. State of the art in these technologies are described along with a discussion of current limitations and future opportunities.

Design and Development of Surgeon Augmented Endovascular Robotic System.

OBJECTIVE: Inadequate visual and force feedback while navigating surgical tools elevate the risk of endovascular procedures. It also poses occupational hazard due to repeated exposure to X-rays. A teleoperated robotic system that augments surgeon's actions is a solution. METHOD: We have designed and developed an endovascular robotic system that augments surgeon's actions using conventional surgical tools, as well as generates feedback in order to ensure safety during the procedure. The reaction force from vasculature is estimated from motor current that drives the surgical tool. Calibration required for force estimation is based on bilevel optimization. Input shaping is used in conjunction with a cascaded controller to avoid large responses due to faster inputs and to track tool position. The design, realization, and testing of our system are presented. RESULTS: The responses of the system in comparison with the dynamics model is similar vis-à-vis the same input commands. Any error in the position tracking is reduced by the cascaded controller. Phase-portrait analysis of the system showed that the system is stable. The reaction force estimation is validated against load cell measurements. The safety mechanism in the events of excessive reaction forces while interacting with vasculature is demonstrated. CONCLUSION AND SIGNIFICANCE: Our system is a step toward intelligent robots that can assist surgeons during endovascular procedures by monitoring and alerting the surgeons regarding detrimental parameters. It arrests any unintended excursions of the surgical tools or surgeon's actions. This will also eliminate the need for surgeons to be in radiation environment.

Surgeon Training in Telerobotic Surgery via a Hardware-in-the-Loop Simulator.

This work presents a software and hardware framework for a telerobotic surgery safety and motor skill training simulator. The aims are at providing trainees a comprehensive simulator for acquiring essential skills to perform telerobotic surgery. Existing commercial robotic surgery simulators lack features for safety training and optimal motion planning, which are critical factors in ensuring patient safety and efficiency in operation. In this work, we propose a hardware-in-the-loop simulator directly introducing these two features. The proposed simulator is built upon the Raven-II™ open source surgical robot, integrated with a physics engine and a safety hazard injection engine. Also, a Fast Marching Tree-based motion planning algorithm is used to help trainee learn the optimal instrument motion patterns. The main contributions of this work are (1) reproducing safety hazards events, related to da Vinci™ system, reported to the FDA MAUDE database, with a novel haptic feedback strategy to provide feedback to the operator when the underlying dynamics differ from the real robot's states so that the operator will be aware and can mitigate the negative impact of the safety-critical events, and (2) using motion planner to generate semioptimal path in an interactive robotic surgery training environment.

Augmented-reality-based skills training for robot-assisted urethrovesical anastomosis: a multi-institutional randomised controlled trial.

OBJECTIVE: To validate robot-assisted surgery skills acquisition using an augmented reality (AR)-based module for urethrovesical anastomosis (UVA). METHODS: Participants at three institutions were randomised to a Hands-on Surgical Training (HoST) technology group or a control group. The HoST group was given procedure-based training for UVA within the haptic-enabled AR-based HoST environment. The control group did not receive any training. After completing the task, the control group was offered to cross over to the HoST group (cross-over group). A questionnaire administered after HoST determined the feasibility and acceptability of the technology. Performance of UVA using an inanimate model on the daVinci Surgical System (Intuitive Surgical Inc., Sunnyvale, CA, USA) was assessed using a UVA evaluation score and a Global Evaluative Assessment of Robotic Skills (GEARS) score. Participants completed the National Aeronautics and Space Administration Task Load Index (NASA TLX) questionnaire for cognitive assessment, as outcome measures. A Wilcoxon rank-sum test was used to compare outcomes among the groups (HoST group vs control group and control group vs cross-over group). RESULTS: A total of 52 individuals participated in the study. UVA evaluation scores showed significant differences in needle driving (3.0 vs 2.3; P = 0.042), needle positioning (3.0 vs 2.4; P = 0.033) and suture placement (3.4 vs 2.6; P = 0.014) in the HoST vs the control group. The HoST group obtained significantly higher scores (14.4 vs 11.9; P 0.012) on the GEARS. The NASA TLX indicated lower temporal demand and effort in the HoST group (5.9 vs 9.3; P = 0.001 and 5.8 vs 11.9; P = 0.035, respectively). In all, 70% of participants found that HoST was similar to the real surgical procedure, and 75% believed that HoST could improve confidence for carrying out the real intervention. CONCLUSION: Training in UVA in an AR environment improves technical skill acquisition with minimal cognitive demand.

Construct validation of the key components of Fundamental Skills of Robotic Surgery (FSRS) curriculum--a multi-institution prospective study.

BACKGROUND: Recent incorporation of simulation in surgical training necessitates developing validated platforms for training and assessment. A tool should fulfill the fundamental criteria of validation. OBJECTIVE: To report the ability of a simulation-based robotic training curriculum-Fundamental Skills of Robotic Surgery (FSRS)-to assess and distinguish between different performance levels of operator experience (construct validity). MATERIALS AND METHODS: This is a prospective multicenter observational study. Participants were classified as novice (0 robotic cases performed) and experts (>150 robotic cases performed). All participants were required to complete 4 key tasks in a previously validated FSRS curriculum: ball placement, coordinated tool control, fourth arm control, and needle handling and exchange. Using the metrics available in the simulator software, the performances of each group were compared to evaluate construct validation. RESULTS: A convenience cohort of 61 surgeons participated. Novice group (n = 49) consisted of 41 fellows/residents/medical students and 8 trained open/laparoscopic surgeons, whereas expert group consisted of 12 surgeons. The novice group had no previous robotic console experience, whereas the expert group had >150 prior robotic cases experience. An overall significant difference was observed in favor of the expert group in 4 skill sets (p < 0.05). Time to complete all 4 tasks was significantly shorter in the expert group (p < 0.001). The expert group displayed significantly lesser tool collision (p = 0.002) and reduced tissue damage (p < 0.001). In performing most tasks, the expert group's camera (p < 0.001) and clutch usage (p < 0.001) was significantly greater when compared with the novice group. CONCLUSION: The components of the FSRS curriculum showed construct validity. This validation would help in effectively implementing this curriculum for robot-assisted surgical training.

Simulation-based robot-assisted surgical training: a health economic evaluation.

OBJECTIVE: To determine the overall cost effectiveness of surgical skills training on Robotic Surgical Simulator (RoSS). METHODS: This study evaluates the cost analysis of utilizing RoSS for robot-assisted surgical training, at Roswell Park Center for Robotic Surgery. Trainees were queried for time spent on the RoSS console over a period of 1 year, starting from June 2010 to June 2011. Time spent was converted to training time consumed on robotic console, resulting in loss of OR time and revenue. The mechanical durability of the RoSS was also determined. RESULTS: 105 trainees spent 361 h on the RoSS. This duration converted to 73 robot-assisted radical prostatectomy cases, and 72 animal lab sessions. RoSS prevented a potential loss of $600,000, while 72 animal labs would have cost more than $72,000 without including initial robot installation, annual maintenance and personnel expenses. The mechanical durability testing determined breakdown at 180 and 360 h for master control and pinch device, which were repaired under warranty. CONCLUSION: RoSS is a cost effective surgical simulator for implementation of a simulation-based robot-assisted surgical training program.

Development and validation of a composite scoring system for robot-assisted surgical training--the Robotic Skills Assessment Score.

BACKGROUND: A standardized scoring system does not exist in virtual reality-based assessment metrics to describe safe and crucial surgical skills in robot-assisted surgery. This study aims to develop an assessment score along with its construct validation. MATERIALS AND METHODS: All subjects performed key tasks on previously validated Fundamental Skills of Robotic Surgery curriculum, which were recorded, and metrics were stored. After an expert consensus for the purpose of content validation (Delphi), critical safety determining procedural steps were identified from the Fundamental Skills of Robotic Surgery curriculum and a hierarchical task decomposition of multiple parameters using a variety of metrics was used to develop Robotic Skills Assessment Score (RSA-Score). Robotic Skills Assessment mainly focuses on safety in operative field, critical error, economy, bimanual dexterity, and time. Following, the RSA-Score was further evaluated for construct validation and feasibility. Spearman correlation tests performed between tasks using the RSA-Scores indicate no cross correlation. Wilcoxon rank sum tests were performed between the two groups. RESULTS: The proposed RSA-Score was evaluated on non-robotic surgeons (n = 15) and on expert-robotic surgeons (n = 12). The expert group demonstrated significantly better performance on all four tasks in comparison to the novice group. Validation of the RSA-Score in this study was carried out on the Robotic Surgical Simulator. CONCLUSION: The RSA-Score is a valid scoring system that could be incorporated in any virtual reality-based surgical simulator to achieve standardized assessment of fundamental surgical tents during robot-assisted surgery.

Fundamental skills of robotic surgery: a multi-institutional randomized controlled trial for validation of a simulation-based curriculum.

OBJECTIVE: To develop and establish effectiveness of simulation-based robotic curriculum--fundamental skills of robotic surgery (FSRS). METHODS: FSRS curriculum was developed and incorporated into a virtual reality simulator, Robotic Surgical Simulator (RoSS). Fifty-three participants were randomized into an experimental group (EG) or control group (CG). The EG was asked to complete the FSRS and 1 final test on the da Vinci Surgical System (dVSS). The dVSS test consisted of 3 tasks: ball placement, suture pass, and fourth arm manipulation. The CG was directly tested on the dVSS then offered the chance to complete the FSRS and re-tested on the dVSS as a crossover (CO) group. RESULTS: Sixty-five percent of participants had never formally trained using laparoscopic surgery. Ball placement: the EG demonstrated shorter time (142 vs 164 seconds, P = .134) and more precise (1.5 vs 2.5 drops, P = .014). The CO took less time (P <.001) with greater precision (P <.001). Instruments were rarely lost from the field. Suture pass: the EG demonstrated better camera utilization (4.3 vs 3.0, P = .078). Less instrument loss occurred (0.5 vs 1.1, P = .026). Proper camera usage significantly improved (P = .009). Fourth arm manipulation: the EG took less time (132 vs 157 seconds, P = .302). Meanwhile, loss of instruments was less frequent (0.2 vs 0.8, P = .076). Precision in the CO improved significantly (P = .042) and camera control and safe instrument manipulation showed improvement (1.5 vs 3.5, 0.2 vs 0.9, respectively). CONCLUSION: FSRS curriculum is a valid, feasible, and structured curriculum that demonstrates its effectiveness by significant improvements in basic robotic surgery skills.

Validation of Robotic Surgery Simulator (RoSS).

Recent growth of daVinci Robotic Surgical System as a minimally invasive surgery tool has led to a call for better training of future surgeons. In this paper, a new virtual reality simulator, called RoSS is presented. Initial results from two studies - face and content validity, are very encouraging. 90% of the cohort of expert robotic surgeons felt that the simulator was excellent or somewhat close to the touch and feel of the daVinci console. Content validity of the simulator received 90% approval in some cases. These studies demonstrate that RoSS has the potential of becoming an important training tool for the daVinci surgical robot.

Content validation of a novel robotic surgical simulator.

OBJECTIVE: • To assess the content validity of an early prototype robotic simulator. Minimally invasive surgery poses challenges for training future surgeons. The Robotic Surgical Simulator (RoSS) is a novel virtual reality simulator for the da Vinci Surgical System. PATIENTS AND METHODS: • Participants attending the 2010 International Robotic Urology Symposium were invited to experience RoSS. Afterwards, participants completed a survey regarding the appropriateness of the simulator as a teaching tool. RESULTS: • Forty-two subjects including surgeons experienced with robotics (n= 31) and novices (n= 11) participated in this study. • Eighty per cent of the entire cohort had an average of 4 years of experience with robot-assisted surgery. • Eleven (26%) novices lacked independent robot-assisted experience. The expert group comprised 17 (41%) surgeons averaging 881 (160-2200) robot-assisted cases. Experts rated the 'clutch control' virtual simulation task as a good (71%) or excellent (29%) teaching tool. • Seventy-eight per cent rated the 'ball place' task as good or excellent but 22% rated it as poor. • Twenty-seven per cent rated the 'needle removal' task as an excellent teaching tool, 60% rated it good and 13% rated it poor. • Ninety-one per cent rated the 'fourth arm tissue removal' task as good or excellent. • Ninety-four per cent responded that RoSS would be useful for training purposes. • Eighty-eight per cent felt that RoSS would be an appropriate training and testing format before operating room experience for residents. • Seventy-nine per cent indicated that RoSS could be used for privileging or certifying in robotic surgery. CONCLUSION: • Results based on expert evaluation of RoSS as a teaching modality illustrate that RoSS has appropriate content validity.