simulation training

BACKGROUND AND OBJECTIVE: Mechanical ventilation is a life-saving treatment for critically-ill patients. During treatment, patient-ventilator asynchrony (PVA) can occur, which can lead to pulmonary damage, complications, and higher mortality. While traditional detection methods for PVAs rely on visual inspection by clinicians, in recent years, machine learning models are being developed to detect PVAs automatically. However, training these models requires large labeled datasets, which are difficult to obtain, as labeling is a labour-intensive and time-consuming task, requiring clinical expertise...

PURPOSE: To explore diagnostic deep learning for optimizing the prostate MRI protocol by assessing the diagnostic efficacy of MRI sequences. METHOD: This retrospective study included 840 patients with a biparametric prostate MRI scan. The MRI protocol included a T2-weighted image, three DWI sequences (b50, b400, and b800 s/mm2 ), a calculated ADC map, and a calculated b1400 sequence. Two accelerated MRI protocols were simulated, using only two acquired b-values to calculate the ADC and b1400...

INTRODUCTION: A dental humanoid robot, SIMROID® , is able to replicate the actions characteristic of human beings and enable training for communicating with patients and coping with unexpected situations. This study assessed user experiences via a survey questionnaire following hands-on training on the SIMROID® . MATERIALS AND METHODS: A total of 112 participants, consisting of 50 high school students who visited AUSD (Asahi University School of Dentistry) to participate in open campus events, 42 fourth-year students at AUSD and 20 dental students from Mexico State Autonomy University, University of Siena and Peking University took the survey...

Trial-averaged metrics, e.g. tuning curves or population response vectors, are a ubiquitous way of characterizing neuronal activity. But how relevant are such trial-averaged responses to neuronal computation itself? Here we present a simple test to estimate whether average responses reflect aspects of neuronal activity that contribute to neuronal processing. The test probes two assumptions implicitly made whenever average metrics are treated as meaningful representations of neuronal activity: Reliability: Neuronal responses repeat consistently enough across trials that they convey a recognizable reflection of the average response to downstream regions...

This paper presents a novel method based on leveraging physics-informed neural networks for magnetic resonance electrical property tomography (MREPT). MREPT is a noninvasive technique that can retrieve the spatial distribution of electrical properties (EPs) of scanned tissues from measured transmit radiofrequency (RF) in magnetic resonance imaging (MRI) systems. The reconstruction of EP values in MREPT is achieved by solving a partial differential equation derived from Maxwell's equations that lacks a direct solution...

ReaxFF is a computationally efficient model for reactive molecular dynamics simulations that has been applied to a wide variety of chemical systems. When ReaxFF parameters are not yet available for a chemistry of interest, they must be (re)optimized, for which one defines a set of training data that the new ReaxFF parameters should reproduce. ReaxFF training sets typically contain diverse properties with different units, some of which are more abundant (by orders of magnitude) than others. To find the best parameters, one conventionally minimizes a weighted sum of squared errors over all of the data in the training set...

Using knowledge from statistical thermodynamics and crystallography, we develop an image-image translation model, called SorbIIT, that uses three-dimensional grids of adsorbate-adsorbent interaction energies as input to predict the spatially resolved loading surface of nanoporous materials over a broad range of temperatures and pressures. SorbIIT consists of a closed-form differential model for loading-surface prediction and a U-Net to generate spatial differential distributions from the energy grids. SorbIIT is trained using the energy grids and adsorbate distributions (obtained from high-throughput simulations) of 50 synthesized and 70 hypothetical zeolites and applied for predicting the adsorption of carbon dioxide, hydrogen sulfide, n -butane, 2-methylpropane, krypton, and xenon in other zeolites from 256 to 400 K...

OBJECTIVE: Cleft lip and palate is the most common craniofacial birth anomaly and requires surgery in the first year of life. However, craniofacial surgery training opportunities are limited. The aim of this study was to present and evaluate an open-source cleft lip and palate hybrid (casting and three-dimensional (3D) printing) simulation model which can be replicated at low cost to facilitate the teaching and training of cleft surgery anatomy and techniques. DESIGN: The soft tissue component of the cleft surgery training model was casted using a 3D printed 5-component mold and silicone...

Reconstructing 3D shapes from images are becoming popular, but such methods usually estimate relative depth maps with ambiguous scales. A method for reconstructing a scale-preserving 3D shape from monocular endoscope image sequences through training an absolute depth prediction network is proposed. First, a dataset of synchronized sequences of RGB images and depth maps is created using an endoscope simulator. Then, a supervised depth prediction network is trained that estimates a depth map from a RGB image minimizing the loss compared to the ground-truth depth map...

Kidney stones require surgical removal when they grow too large to be broken up externally or to pass on their own. Upper tract urothelial carcinoma is also sometimes treated endoscopically in a similar procedure. These surgeries are difficult, particularly for trainees who often miss tumours, stones or stone fragments, requiring re-operation. Furthermore, there are no patient-specific simulators to facilitate training or standardized visualization tools for ureteroscopy despite its high prevalence. Here a system ASSIST-U is proposed to create realistic ureteroscopy images and videos solely using preoperative computerized tomography (CT) images to address these unmet needs...

This paper describes a methodology for the assessment of training simulator-based computer-assisted intervention skills on an AR/VR-guided procedure making use of CT axial slice views for a neurosurgical procedure: external ventricular drain (EVD) placement. The task requires that trainees scroll through a stack of axial slices and form a mental representation of the anatomical structures in order to subsequently target the ventricles to insert an EVD. The process of observing the 2D CT image slices in order to build a mental representation of the 3D anatomical structures is the skill being taught, along with the cognitive control of the subsequent targeting, by planned motor actions, of the EVD tip to the ventricular system to drain cerebrospinal fluid (CSF)...

In stroke rehabilitation, simple robotic devices hold the potential to increase the training dosage in group therapies and to enable continued therapy at home after hospital discharge. However, we identified a lack of portable and cost-effective devices that not only focus on improving motor functions but also address sensory deficits. Thus, we designed a minimally-actuated hand training device that incorporates active grasping movements and passive pronosupination, complemented by a rehabilitative game with meaningful haptic feedback...

Immersive technology, such as extended reality, holds promise as a tool for educating ophthalmologists about the effects of low vision and for enhancing visual rehabilitation protocols. However, immersive simulators have not been evaluated for their ability to induce changes in the oculomotor system, which is crucial for understanding the visual experiences of visually impaired individuals. This study aimed to assess the REALTER (Wearable Egocentric Altered Reality Simulator) system's capacity to induce specific alterations in healthy individuals' oculomotor systems under simulated low-vision conditions...

BACKGROUND: Previous research in nursing has found favorable results from the use of teaching methodologies alternative to lectures. One of the complementary methodologies used for university teaching is gamification, or the inclusion of game elements, creating a dynamic learning environment that allows the acquisition of knowledge and the development of other skills necessary for nursing students. The purpose of this study was to evaluate the effect of a gamification session on student satisfaction and knowledge scores in nursing students in simulated laboratory practice...

OBJECTIVE: To evaluate whether child pedestrian safety training in a smartphone-based virtual reality (VR) environment is not inferior to training in a large, semi-immersive VR environment with demonstrated effectiveness. METHODS: Five hundred 7- and 8-year-old children participated; 479 were randomized to one of two conditions: Learning to cross streets in a smartphone-based VR or learning in a semi-immersive kiosk VR. The systems used identical virtual environments and scenarios...

With the development of new radiopharmaceutical therapies, quantitative SPECT/CT has progressively emerged as a crucial tool for dosimetry. One major obstacle of SPECT is its poor resolution, which results in blurring of the activity distribution. Especially for small objects, this so-called partial-volume effect limits the accuracy of activity quantification. Numerous methods for partial-volume correction (PVC) have been proposed, but most methods have the disadvantage of assuming a spatially invariant resolution of the imaging system, which does not hold for SPECT...

BACKGROUND AND PURPOSE: Transvenous embolization has emerged as a novel technique for treating selected brain AVMs with high reported occlusion rates. However, it requires anatomic and technical skills to be successful and to ensure patient safety. Therefore, training and testing are essential for preparing clinicians to perform these procedures. Our aim was to develop and test a novel, patient-specific brain AVM in vitro model for transvenous embolization by using 3D printing technology...

Inferring past demographic history of natural populations from genomic data is of central concern in many studies across research fields. Previously, our group had developed dadi, a widely used demographic history inference method based on the allele frequency spectrum (AFS) and maximum composite likelihood optimization. However, dadi's optimization procedure can be computationally expensive. Here, we present donni (demography optimization via neural network inference), a new inference method based on dadi that is more efficient while maintaining comparable inference accuracy...

Medical image segmentation is a crucial field of computer vision. Obtaining correct pathological areas can help clinicians analyze patient conditions more precisely. We have observed that both CNN-based and attention-based neural networks often produce rough segmentation results around the edges of the regions of interest. This significantly impacts the accuracy of obtaining the pathological areas. Without altering the original data and model architecture, further refining the initial segmentation outcomes can effectively address this issue and lead to more satisfactory results...

The reduced-order lumped parameter model (LPM) has great computational efficiency in real-time numerical simulations of haemodynamics but is limited by the accuracy of patient-specific computation. This study proposed a method to achieve the individual LPM modeling with high accuracy to improve the practical clinical applicability of LPM. Clinical data was collected from two medical centres comprising haemodynamic indicators from 323 individuals, including brachial artery pressure waveforms, cardiac output data, and internal carotid artery flow waveforms...