Rapid prototyping of multi-compartment models for urea kinetics in hemodialysis: a System Dynamics approach.

Models of urea kinetics facilitate a mechanistic understanding of urea transfer and provide a tool for optimizing dialysis efficacy. Dual-compartment models have largely replaced single-compartment models as they are able to accommodate the urea rebound on the cessation of dialysis. Modeling the kinetics of urea and other molecular species is frequently regarded as a rarefied academic exercise with little relevance at the bedside. We demonstrate the utility of System Dynamics in creating multi-compartment models of urea kinetics by developing a dual-compartment model that is efficient, intuitive, and widely accessible to a range of practitioners. Notwithstanding its simplicity, we show that the System Dynamics model compares favorably with the performance of a more complex volume-average model in terms of calibration to clinical data and parameter estimation. Its intuitive nature, ease of development/modification, and excellent performance with real-world data may make System Dynamics an invaluable tool in widening the accessibility of hemodialysis modeling.

Evaluating the Clinical Realism of Synthetic Chest X-Rays Generated Using Progressively Growing GANs.

Chest X-rays are a vital diagnostic tool in the workup of many patients. Similar to most medical imaging modalities, they are profoundly multi-modal and are capable of visualising a variety of combinations of conditions. There is an ever pressing need for greater quantities of labelled images to drive forward the development of diagnostic tools; however, this is in direct opposition to concerns regarding patient confidentiality which constrains access through permission requests and ethics approvals. Previous work has sought to address these concerns by creating class-specific generative adversarial networks (GANs) that synthesise images to augment training data. These approaches cannot be scaled as they introduce computational trade offs between model size and class number which places fixed limits on the quality that such generates can achieve. We address this concern by introducing latent class optimisation which enables efficient, multi-modal sampling from a GAN and with which we synthesise a large archive of labelled generates. We apply a Progressive Growing GAN (PGGAN) to the task of unsupervised X-ray synthesis and have radiologists evaluate the clinical realism of the resultant samples. We provide an in depth review of the properties of varying pathologies seen on generates as well as an overview of the extent of disease diversity captured by the model. We validate the application of the Fréchet Inception Distance (FID) to measure the quality of X-ray generates and find that they are similar to other high-resolution tasks. We quantify X-ray clinical realism by asking radiologists to distinguish between real and fake scans and find that generates are more likely to be classed as real than by chance, but there is still progress required to achieve true realism. We confirm these findings by evaluating synthetic classification model performance on real scans. We conclude by discussing the limitations of PGGAN generates and how to achieve controllable, realistic generates going forward. We release our source code, model weights, and an archive of labelled generates.

Facilitating Understanding, Modeling and Simulation of Infectious Disease Epidemics in the Age of COVID-19.

Interest in the mathematical modeling of infectious diseases has increased due to the COVID-19 pandemic. However, many medical students do not have the required background in coding or mathematics to engage optimally in this approach. System dynamics is a methodology for implementing mathematical models as easy-to-understand stock-flow diagrams. Remarkably, creating stock-flow diagrams is the same process as creating the equivalent differential equations. Yet, its visual nature makes the process simple and intuitive. We demonstrate the simplicity of system dynamics by applying it to epidemic models including a model of COVID-19 mutation. We then discuss the ease with which far more complex models can be produced by implementing a model comprising eight differential equations of a Chikungunya epidemic from the literature. Finally, we discuss the learning environment in which the teaching of the epidemic modeling occurs. We advocate the widespread use of system dynamics to empower those who are engaged in infectious disease epidemiology, regardless of their mathematical background.

Gaze tracking dataset for comparison of smooth and saccadic eye tracking.

Pupil tracking data are collected through the use of an infrared camera, and a head-mounted system [1]. The head-mounted system detects the relative pupil position and adjusts the mouse cursor position accordingly. The data are available for comparison of eye tracking with saccadic movements (with the head fixed in space) versus those from smooth movements (with the head moving in space). The analysis comprises two experiments for both types of eye tracking, which are performed with ten trials each for two participants. In the first experiment, the participant attempts to place the cursor into a target boundary of varying sizes. In the second experiment, the participant attempts to move the cursor to a target location within the shortest time.

Data from wearable wireless instrumentation device taken during simulated CPR with and without intentional error toward a quality feedback device.

Data of Cardiopulmonary Resuscitation performed on a mannequin was collected via wearable instrumentation (using the MYO device). The data were collected for both "good" CPR and for performance of CPR with common errors introduced intentionally for this study. The data are labelled according to the error, and contain a variety of derived measurements. Data collected were used toward "Development of a novel cardiopulmonary resuscitation measurement tool using real-time feedback from wearable wireless instrumentation' (Ward et al., 2019) in which full context is available'. The data are available at Mendeley Data, doi:10.17632/pvjghfjmy4.1 (Ward et al., 2019).

Development of a novel cardiopulmonary resuscitation measurement tool using real-time feedback from wearable wireless instrumentation.

AIM: The design and implementation of a wearable training device to improve cardiopulmonary resuscitation (CPR) is presented. METHODS: The MYO contains both Electromyography (EMG) and Inertial Measurement Unit (IMU) sensors which are used to detect effective CPR, and the four common incorrect hand and arm positions viz. relaxed fingers; hands too low on the sternum; patient too close; or patient too far. The device determines the rate and depth of compressions calculated using a Fourier transform and dual-quaternions respectively. In addition, common positional mistakes are determined using classification algorithms (six machine learning algorithms are considered and tested). Feedback via Graphical User Interface (GUI) and audio is integrated. RESULTS: The system is tested by performing CPR on a mannequin and comparing real-time results to theoretical values. Tests show that although the classification algorithm performed well in testing (98%), in real time, it had low accuracy for certain categories (60%), which are attributable to the MYO calibration, sampling rate and misclassification of similar hand positions. Combining these similar incorrect positions into more general categories significantly improves accuracy, and produces the same improved outcome of improved CPR. The rate and depth measures have a general accuracy of 97%. CONCLUSION: The system allows for portable, real-time feedback for use in training and in the field, and shows promise toward classifying and improving the administration of CPR.

Towards Automated Three-Dimensional Tracking of Nephrons through Stacked Histological Image Sets.

An automated approach for tracking individual nephrons through three-dimensional histological image sets of mouse and rat kidneys is presented. In a previous study, the available images were tracked manually through the image sets in order to explore renal microarchitecture. The purpose of the current research is to reduce the time and effort required to manually trace nephrons by creating an automated, intelligent system as a standard tool for such datasets. The algorithm is robust enough to isolate closely packed nephrons and track their convoluted paths despite a number of nonideal, interfering conditions such as local image distortions, artefacts, and interstitial tissue interference. The system comprises image preprocessing, feature extraction, and a custom graph-based tracking algorithm, which is validated by a rule base and a machine learning algorithm. A study of a selection of automatically tracked nephrons, when compared with manual tracking, yields a 95% tracking accuracy for structures in the cortex, while those in the medulla have lower accuracy due to narrower diameter and higher density. Limited manual intervention is introduced to improve tracking, enabling full nephron paths to be obtained with an average of 17 manual corrections per mouse nephron and 58 manual corrections per rat nephron.