Evaluation of a Theory-Based Virtual Counseling Application in Nursing Education.

Effective communication skills in nursing are necessary for high-quality nursing care, but given the decline in nursing students' attitudes and their low self-confidence in effective communication with patients, a participatory and experiential training method is needed. Therefore, a virtual counseling application was developed using artificial intelligence and a three-dimensional avatar to facilitate learning of communication skills. This study aimed to evaluate the effectiveness of this theory-based virtual intervention on nursing students' learning attitudes, communication self-efficacy, and clinical performance. A longitudinal quasi-experimental study was conducted. Ninety-three undergraduate nursing students received virtual patient trainings with four clinical scenarios over 2 years. Data were analyzed using McNemar test and analysis of variance. Virtual patient training improved students' learning attitudes toward communication skills for scenarios involving the pregnant woman (20.4%, P = .03) and depressed patient (17.1%, P = .01) and enhanced perceived self-efficacy for scenarios involving the pregnant woman (22.6%, P = .002) and stressed nursing student (18.3%, P = .002). Students received lower clinical communication scores for pediatric, obstetric, and medical practicums compared with a previous cohort who received no training. Overall, this virtual counseling application can provide a valuable and cost-effective communication learning resource for the nursing curriculum.

Metadata Correction: A Virtual Counseling Application Using Artificial Intelligence for Communication Skills Training in Nursing Education: Development Study.

[This corrects the article DOI: 10.2196/14658.].

A Virtual Counseling Application Using Artificial Intelligence for Communication Skills Training in Nursing Education: Development Study.

BACKGROUND: The ability of nursing undergraduates to communicate effectively with health care providers, patients, and their family members is crucial to their nursing professions as these can affect patient outcomes. However, the traditional use of didactic lectures for communication skills training is ineffective, and the use of standardized patients is not time- or cost-effective. Given the abilities of virtual patients (VPs) to simulate interactive and authentic clinical scenarios in secured environments with unlimited training attempts, a virtual counseling application is an ideal platform for nursing students to hone their communication skills before their clinical postings. OBJECTIVE: The aim of this study was to develop and test the use of VPs to better prepare nursing undergraduates for communicating with real-life patients, their family members, and other health care professionals during their clinical postings. METHODS: The stages of the creation of VPs included preparation, design, and development, followed by a testing phase before the official implementation. An initial voice chatbot was trained using a natural language processing engine, Google Cloud's Dialogflow, and was later visualized into a three-dimensional (3D) avatar form using Unity 3D. RESULTS: The VPs included four case scenarios that were congruent with the nursing undergraduates' semesters' learning objectives: (1) assessing the pain experienced by a pregnant woman, (2) taking the history of a depressed patient, (3) escalating a bleeding episode of a postoperative patient to a physician, and (4) showing empathy to a stressed-out fellow final-year nursing student. Challenges arose in terms of content development, technological limitations, and expectations management, which can be resolved by contingency planning, open communication, constant program updates, refinement, and training. CONCLUSIONS: The creation of VPs to assist in nursing students' communication skills training may provide authentic learning environments that enhance students' perceived self-efficacy and confidence in effective communication skills. However, given the infancy stage of this project, further refinement and constant enhancements are needed to train the VPs to simulate real-life conversations before the official implementation.

Residual Physics and Post-Posed Shielding for Safe Deep Reinforcement Learning Method.

Deep reinforcement learning (DRL) has been researched for computer room air conditioning unit control problems in data centers (DCs). However, two main issues limit the deployment of DRL in actual systems. First, a large amount of data is needed. Next, as a mission-critical system, safe control needs to be guaranteed, and temperatures in DCs should be kept within a certain operating range. To mitigate these issues, this article proposes a novel control method RP-SDRL. First, Residual Physics, built using the first law of thermodynamics, is integrated with the DRL algorithm and a Prediction Model. Subsequently, a Correction Model adapted from gradient descent is combined with the Prediction Model as Post-Posed Shielding to enforce safe actions. The RP-SDRL method was validated using simulation. Noise is added to the states of the model to further test its performance under state uncertainty. Experimental results show that the combination of Residual Physics and DRL can significantly improve the initial policy, sample efficiency, and robustness. Residual Physics can also improve the sample efficiency and the accuracy of the prediction model. While DRL alone cannot avoid constraint violations, RP-SDRL can detect unsafe actions and significantly reduce violations. Compared to the baseline controller, about 13% of electricity usage can be saved.

Integrated ablation and division device for liver resection.

BACKGROUND: Intraoperative blood loss during liver resection may be minimized by ablating the liver parenchyma using radiofrequency (RF) energy. However, it is difficult to estimate the depth of the avascular plane and more RF energy than necessary may be inadvertently used as a result of lack of feedback. METHODS: Laparoscopic liver resection was performed on a live porcine model to determine the feasibility and applicability of a model which integrates ablation and division in a single device. RESULTS: Liver resection was uncomplicated with minimal bleeding. The integration of the ablation and division mechanism resolved the difficulty of estimating the depth of the avascular plane after coagulation. The real-time feedback mechanism minimized liver damage by eliminating the application of unnecessary RF. CONCLUSIONS: The proposed model is functionally acceptable and represents a possible method of determining the depth of the avascular plane and the amount of RF energy required during liver resection.

Blockchain in manufacturing quality control: A computer simulation study.

Blockchain has been applied to quality control in manufacturing, but the problems of false defect detections and lack of data transparency remain. This paper proposes a framework, Blockchain Quality Controller (BCQC), to overcome these limitations while fortifying data security. BCQC utilizes blockchain and Internet-of-Things to form a peer-to-peer supervision network. This paper also proposes a consensus algorithm, Quality Defect Tolerance (QDT), to adopt blockchain for during-production quality control. Simulation results show that BCQC enhances data security and improves defect detections. Although the time taken for the quality control process increases with the number of nodes in blockchain, the application of QDT allows multiple inspections on a workpiece to be consolidated at a faster pace, effectively speeding up the entire quality control process. The BCQC and QDT can improve the quality of parts produced for mass personalization manufacturing.

Robot-assisted flexible needle insertion using universal distributional deep reinforcement learning.

PURPOSE: Flexible needle insertion is an important minimally invasive surgery approach for biopsy and radio-frequency ablation. This approach can minimize intraoperative trauma and improve postoperative recovery. We propose a new path planning framework using multi-goal deep reinforcement learning to overcome the difficulties in uncertain needle-tissue interactions and enhance the robustness of robot-assisted insertion process. METHODS: This framework utilizes a new algorithm called universal distributional Q-learning (UDQL) to learn a stable steering policy and perform risk management by visualizing the learned Q-value distribution. To further improve the robustness, universal value function approximation is leveraged in the training process of UDQL to maximize generalization and connect to diagnosis by adapting fast re-planning and transfer learning. RESULTS: Computer simulation and phantom experimental results show our proposed framework can securely steer flexible needles with high insertion accuracy and robustness. The framework also improves robustness by providing distribution information to clinicians for diagnosis and decision making during surgery. CONCLUSIONS: Compared with previous methods, the proposed framework can perform multi-target needle insertion through single insertion point qunder continuous state space model with higher accuracy and robustness.

Communication skills training using virtual reality: A descriptive qualitative study.

BACKGROUND: Modern medical pedagogical strategies are shifting toward the use of virtual patient simulations. OBJECTIVE: This study aims to examine students' users' attitudes and experiences and clinical facilitators' perspectives on student performances in the clinical setting post-virtual patient training. DESIGN: A descriptive qualitative study design was used. SETTING: Nursing faculty at a local university in Singapore. PARTICIPANTS: 24 nursing undergraduates and six clinical facilitators. METHODS: This study is a follow-up of an experimental study on the Virtual Counseling Application Using Artificial Intelligence (VCAAI). The study took place from the academic year 2017/2018 ended in November 2019. Focus group discussions and individual interviews were conducted. All interviews and focus group discussions were audiotaped, transcribed verbatim, and analyzed using thematic analysis. RESULTS: Two overarching themes (students' virtual patient user experience and clinical facilitators' evaluations of students' clinical communication skills) comprising six themes were generated. Themes under students' user experience included: 1) attitudes toward virtual patient training, 2) virtual patient's role in student development, and 3) enhanced features and implementation suggestions. Themes under clinical facilitators' evaluations included: 1) insights on students' communication skills and 2) approaches to improve communication skills. An overlapping theme titled 'value of technology in teaching communication' comprised of mutual feedback from both students and clinical facilitators. Early implementation, continued accessibility, enhancing realism and technological improvements to the VCAAI were listed as key areas for program improvement, while increased situational sensitivity and language training are recommended to further enhance students' communication skills. CONCLUSION: The mixed attitudes toward virtual patient interactions and recognitions of the benefits of virtual patient simulations suggest the potential effectiveness of the use of virtual patients in teaching effective nursing communication skills. However, the lack of authenticity and other limitations need to be addressed before official implementations of such trainings with virtual patients to undergraduate nursing curricula.

A Versatile Soft Crawling Robot with Rapid Locomotion.

This article presents a versatile soft crawling robot capable of rapid and effective locomotion. The robot mainly consists of two vacuum-actuated spring actuators and two electrostatic actuators. By programming the actuation sequences of different actuators, the robot is able to achieve two basic modes of locomotion: linear motion and turning. Subsequently, we have developed analytical models to interpret the static actuation performance of the robot body, including linear and bending motions. Moreover, an empirical dynamic model is also developed to optimize the locomotion speed in terms of frequency and duty cycle of the actuation signal. Furthermore, with the help of the strong electroadhesion force and fast response of the deformable body, the soft robot achieves a turning speed of 15.09°/s, which is one of the fastest among existing soft crawling robots to the best of our knowledge. In addition to the rapid and effective locomotion, the soft crawling robot can also achieve multiple impressive functions, including obstacle navigation in confined spaces, climbing a vertical wall with a speed of 6.67 mm/s (0.049 body length/s), carrying a payload of 69 times its self-weight on a horizontal surface, crossing over a 2 cm (0.15 body length) gap, and kicking a ball.

Overlapping radiofrequency ablation planning and robot-assisted needle insertion for large liver tumors.

BACKGROUND: We aimed at facilitating percutaneous radiofrequency ablation (RFA) for large tumors with accurate overlapping ablation planning and robot-assisted needle insertion from a single incision port (SIP). METHODS: We developed a personalized and quantitative RFA planning method to obtain multiple needle overlapping ablation planning through a single incision. A robot with a remote center of motion mechanism was designed to perform needle insertions through a SIP according to the planning. RESULTS: Numerical and visual evaluation showed that the planning could yield nearly full coverage over large tumors and greatly reduce both ablation times and the ablations of normal tissues. Ex vivo and in vivo experiments showed that our robot-assisted needle insertion system was capable of conducting the RFA procedure in large liver tumors from a SIP. CONCLUSIONS: Robot-assisted RFA needle insertions from a SIP makes RFA treatment for large tumors more minimally invasive, predictable, and repeatable.

Robust path planning for flexible needle insertion using Markov decision processes.

PURPOSE: Flexible needle has the potential to accurately navigate to a treatment region in the least invasive manner. We propose a new planning method using Markov decision processes (MDPs) for flexible needle navigation that can perform robust path planning and steering under the circumstance of complex tissue-needle interactions. METHODS: This method enhances the robustness of flexible needle steering from three different perspectives. First, the method considers the problem caused by soft tissue deformation. The method then resolves the common needle penetration failure caused by patterns of targets, while the last solution addresses the uncertainty issues in flexible needle motion due to complex and unpredictable tissue-needle interaction. RESULTS: Computer simulation and phantom experimental results show that the proposed method can perform robust planning and generate a secure control policy for flexible needle steering. Compared with a traditional method using MDPs, the proposed method achieves higher accuracy and probability of success in avoiding obstacles under complicated and uncertain tissue-needle interactions. Future work will involve experiment with biological tissue in vivo. CONCLUSION: The proposed robust path planning method can securely steer flexible needle within soft phantom tissues and achieve high adaptability in computer simulation.

Optimization of cell seeding in a 2D bio-scaffold system using computational models.

The cell expansion process is a crucial part of generating cells on a large-scale level in a bioreactor system. Hence, it is important to set operating conditions (e.g. initial cell seeding distribution, culture medium flow rate) to an optimal level. Often, the initial cell seeding distribution factor is neglected and/or overlooked in the design of a bioreactor using conventional seeding distribution methods. This paper proposes a novel seeding distribution method that aims to maximize cell growth and minimize production time/cost. The proposed method utilizes two computational models; the first model represents cell growth patterns whereas the second model determines optimal initial cell seeding positions for adherent cell expansions. Cell growth simulation from the first model demonstrates that the model can be a representation of various cell types with known probabilities. The second model involves a combination of combinatorial optimization, Monte Carlo and concepts of the first model, and is used to design a multi-layer 2D bio-scaffold system that increases cell production efficiency in bioreactor applications. Simulation results have shown that the recommended input configurations obtained from the proposed optimization method are the most optimal configurations. The results have also illustrated the effectiveness of the proposed optimization method. The potential of the proposed seeding distribution method as a useful tool to optimize the cell expansion process in modern bioreactor system applications is highlighted.

Shared control of a medical robot with haptic guidance.

PURPOSE: Tele-operation of robotic surgery reduces the radiation exposure during the interventional radiological operations. However, endoscope vision without force feedback on the surgical tool increases the difficulty for precise manipulation and the risk of tissue damage. The shared control of vision and force provides a novel approach of enhanced control with haptic guidance, which could lead to subtle dexterity and better maneuvrability during MIS surgery. METHODS: The paper provides an innovative shared control method for robotic minimally invasive surgery system, in which vision and haptic feedback are incorporated to provide guidance cues to the clinician during surgery. The incremental potential field (IPF) method is utilized to generate a guidance path based on the anatomy of tissue and surgical tool interaction. Haptic guidance is provided at the master end to assist the clinician during tele-operative surgical robotic task. RESULTS: The approach has been validated with path following and virtual tumor targeting experiments. The experiment results demonstrate that comparing with vision only guidance, the shared control with vision and haptics improved the accuracy and efficiency of surgical robotic manipulation, where the tool-position error distance and execution time are reduced. CONCLUSIONS: The validation experiment demonstrates that the shared control approach could help the surgical robot system provide stable assistance and precise performance to execute the designated surgical task. The methodology could also be implemented with other surgical robot with different surgical tools and applications.

Flow visualization for interactive simulation of drugs injection during chemoembolization.

Chemoembolization is an important therapeutic procedure. A catheter was navigated to the artery that feeds the tumor, and chemotherapy drugs and embolus are injected directly into the tumor. There is a risk that embolus may lodge incorrectly and deprive normal tissue of its blood supply. This paper focuses on visualization of the flow particles in simulation of chemotherapy drugs injection for training of hand-eye coordination skills. We assume that the flow follows a defined path in the hepatic vascular system from the catheter tip. The vascular model is constructed using sweeping and blending operations. Quadrilaterals which are aligned to face the viewer are drawn for the trail of each particle. The quadrilateral in the trail is determined using bilinear interpolation. On simulated fluoroscopic image, the flow is rendered as overlaying and semitransparent quadrilaterals representing the particles' trails. This visualization model achieves a good visual approximation of the flow of particles inside the vessels under fluoroscopic imaging.

Integrative haptic and visual interaction for simulation of PMMA injection during vertebroplasty.

In vertebroplasty, physician relies on both sight and feel to properly place the bone needle through various tissue types and densities, and to help monitor the injection of PMMA or cement into the vertebra. Incorrect injecting and reflux of the PMMA into areas where it should not go can result in detrimental clinical complication. This paper focuses on the human-computer interaction for simulating PMMA injection in our virtual spine workstation. Fluoroscopic images are generated from the CT patient volume data and simulated volumetric flow using a time varying 4D volume rendering algorithm. The user's finger movement is captured by a data glove. Immersion CyberGrasp is used to provide the variable resistance felt during injection by constraining the user's thumb. Based on our preliminary experiments with our interfacing system comprising simulated fluoroscopic imaging and haptic interaction, we found that the former has a larger impact on the user's control during injection.

Probabilistic finite element method for large tumor radiofrequency ablation simulation and planning.

A challenging problem of radiofrequency ablation (RFA) in liver surgery is to accurately estimate the shapes and sizes of RFA lesions whose formation depends on intrinsic variations of the thermal-electrical properties of soft tissue. Large tumors, which can be as long as 10 cm or more, further complicate the problem. In this paper, a probabilistic bio-heating finite element (FE) model is proposed and developed to predict RFA lesions. Uncertainties of RFA lesions are caused by the probabilistic nature of five thermal-electrical liver properties: thermal conductivity, liver tissue density, specific heat, blood perfusion rate and electrical conductivity. Confidence levels of shapes and sizes of lesions are generated by the FE model incorporated with the mean-value first-order second-moment (MVFOSM) method. Based on the probabilistic FE method, a workflow of RFA planning is introduced to enable clinicians to preoperatively view the predicted RFA lesions in three-dimension (3D) within a hepatic environment. Accurate planning of the RFA needle placements can then be achieved based on the interactive simulation and confidence level selection.

Volume Preserved Mass-Spring Model with Novel Constraints for Soft Tissue Deformation.

An interactive surgical simulation system needs to meet three main requirements, speed, accuracy, and stability. In this paper, we present a stable and accurate method for animating mass-spring systems in real time. An integration scheme derived from explicit integration is used to obtain interactive realistic animation for a multiobject environment. We explore a predictor-corrector approach by correcting the estimation of the explicit integration in a poststep process. We introduce novel constraints on positions into the mass-spring model (MSM) to model the nonlinearity and preserve volume for the realistic simulation of the incompressibility. We verify the proposed MSM by comparing its deformations with the reference deformations of the nonlinear finite-element method. Moreover, experiments on porcine organs are designed for the evaluation of the multiobject deformation. Using a pair of freshly harvested porcine liver and gallbladder, the real organ deformations are acquired by computed tomography and used as the reference ground truth. Compared to the porcine model, our model achieves a 1.502 mm mean absolute error measured at landmark locations for cases with small deformation (the largest deformation is 49.109 mm) and a 3.639 mm mean absolute error for cases with large deformation (the largest deformation is 83.137 mm). The changes of volume for the two deformations are limited to 0.030% and 0.057%, respectively. Finally, an implementation in a virtual reality environment for laparoscopic cholecystectomy demonstrates that our model is capable to simulate large deformation and preserve volume in real-time calculations.

Probabilistic predictive modelling of carbon nanocomposites for medical implants design.

Modelling of the mechanical properties of carbon nanocomposites based on input variables like percentage weight of Carbon Nanotubes (CNT) inclusions is important for the design of medical implants and other structural scaffolds. Current constitutive models for the mechanical properties of nanocomposites may not predict well due to differences in conditions, fabrication techniques and inconsistencies in reagents properties used across industries and laboratories. Furthermore, the mechanical properties of the designed products are not deterministic, but exist as a probabilistic range. A predictive model based on a modified probabilistic surface response algorithm is proposed in this paper to address this issue. Tensile testing of three groups of different CNT weight fractions of carbon nanocomposite samples displays scattered stress-strain curves, with the instantaneous stresses assumed to vary according to a normal distribution at a specific strain. From the probabilistic density function of the experimental data, a two factors Central Composite Design (CCD) experimental matrix based on strain and CNT weight fraction input with their corresponding stress distribution was established. Monte Carlo simulation was carried out on this design matrix to generate a predictive probabilistic polynomial equation. The equation and method was subsequently validated with more tensile experiments and Finite Element (FE) studies. The method was subsequently demonstrated in the design of an artificial tracheal implant. Our algorithm provides an effective way to accurately model the mechanical properties in implants of various compositions based on experimental data of samples.