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.

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.

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.

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.

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.].

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.

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.

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.

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.

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.

Fast segmentation and modeling of hepatic components for the planning of robot-assisted liver tumor ablation.

Segmentation and modeling of hepatic components from pre-operative images is very important for treatment planning and guidance in robot-assisted liver tumor ablation. An in-house developed system for hepatic component segmentation and modeling using CT data is presented in this paper. This system includes gross liver segmentation by a 3D mesh deformation model, liver vasculature segmentation by a vessel context-based voting and grouping method, liver tumor segmentation by a support vector machine framework, and other segmentation/modeling tool.

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.

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.

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.

A virtual surgical training system that simulates cutting of soft tissue using a modified pre-computed elastic model.

This work presents a surgical training system that incorporates cutting operation of soft tissue simulated based on a modified pre-computed linear elastic model in the Simulation Open Framework Architecture (SOFA) environment. A precomputed linear elastic model used for the simulation of soft tissue deformation involves computing the compliance matrix a priori based on the topological information of the mesh. While this process may require a few minutes to several hours, based on the number of vertices in the mesh, it needs only to be computed once and allows real-time computation of the subsequent soft tissue deformation.

Modeling of human artery tissue with probabilistic approach.

Accurate modeling of biological soft tissue properties is vital for realistic medical simulation. Mechanical response of biological soft tissue always exhibits a strong variability due to the complex microstructure and different loading conditions. The inhomogeneity in human artery tissue is modeled with a computational probabilistic approach by assuming that the instantaneous stress at a specific strain varies according to normal distribution.

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.

Patient-specific carbon nanocomposite tracheal prosthesis.

Purpose: Surgical removal of the trachea is the current gold standard for treating severe airway carcinoma and stenosis. Resection of 6 cm or more of the trachea requires a replacement graft due to anastomotic tension. The high failure rates of current grafts are attributed to a mismatching of mechanical properties and slow epithelium formation on the inner lumen surface.

Changes in mechanical, structural integrity and microbiological properties following cryopreservation of human cadaveric iliac arteries.

Introduction: The study seeks to investigate how the duration of storage of cryopreserved human cadaveric iliac arteries impacts their mechanical, structural and microbiological properties as compared to their fresh sample.

Materials And Methods: Iliac arteries were harvested from 12 human cadavers and divided into 2 groups. One group underwent mechanical stress-strain assessment immediately and another was cryopreserved for a pre-determined time-period (range, 29 to 364 days).

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.

Hand gesture guided robot-assisted surgery based on a direct augmented reality interface.

Radiofrequency (RF) ablation is a good alternative to hepatic resection for treatment of liver tumors. However, accurate needle insertion requires precise hand-eye coordination and is also affected by the difficulty of RF needle navigation. This paper proposes a cooperative surgical robot system, guided by hand gestures and supported by an augmented reality (AR)-based surgical field, for robot-assisted percutaneous treatment.

Modeling and simulation of material degradation in biodegradable wound closure devices.

Biodegradable materials have been used as wound closure materials. It is important for these materials to enhance wound healing when the wound is vulnerable, and maintain wound closure until the wound is heal. This article studies the degradation process of bioresorbable magnesium micro-clips for wound closure in voice/laryngeal microsurgery.

Robotic learning of motion using demonstrations and statistical models for surgical simulation.

Purpose: In robotic-assisted surgical training, the expertise of surgeons in maneuvering surgical instruments may be utilized to provide the motion trajectories for teaching. However, the motion primitives for trajectory planning are not known until the motion trajectory is generalized. We hypothesize that a generic model that encodes surgical skills using demonstrations and statistical models can be used by the surgical training robot to determine the motion primitive base on the motion trajectory.