Recently we have created a new open‐source technology called BodyLight.js which combines the current standard for web‐based applications including Web‐Assembly for fast computation of mathematical models, web components for building independent transferable objects, and WebGL for involving 3D graphic [1, 2]. We have published methodology and implementation of several models of the cardiovascular system [3]. We have been creating graphical components in 2D and recently in 3D which can be used as animations in simulation application. There are numerous steps involved when going from an idea to a finished project. In this contribution we describe the workflow going from concept to mathematical model and computer graphics to an educational simulation application. This workflow is presented by building a simulation game of hemodynamics of the cardiovascular system. Each step of the workflow can be executed by different domain expert. Physiologist designs concept, mathematician writes and implements mathematical models and executable model simulation programs, graphical designer produces graphical objects based on design concepts which can be animated in simulation programs, and software engineer maintains the system. However, all these professionals need to be able to understand each other and work together. Physiologists must be able to grasp the physiological significance and to tell mathematician how to formalize the problem. Mathematicians and software engineers need to understand physiology in order to produce plausible and verifiable model simulation application. Computer graphics designers must be skilled in programming as they need to produce animated objects that can be used in simulation applications. This connection of expertise is crucial, and was already proposed e.g. by N. Wiener in his works about Cybernetics [4]. We demonstrate that such creative connection of individuals with combined and shared expertise can produce high quality educational materials. Support or Funding Information MPO TRIO FV20628, MPO TRIO FV30195 Collaborative workflow designing simulator of pressure‐volume loop cycle of hemodynamics model using Bodylight.js software tools. Development of In-Browser Simulators for Medical Education: Introduction of a Novel Software ToolchainJŠilarJ Med Internet Res217e14160 https://bodylight.physiome.cz/-Bodylight.jsframeworkLumped models of the cardiovascular system of various complexityFilipJežekBiocybernetics and Biomedical Engineering374666678Cybernetics or control and communication in the animal and the machineNorbertWienerThe M.I.T. Press

BACKGROUND:Machine learning (ML) approaches can significantly improve the classical Rout-based evaluation of the lumbar infusion test (LIT) and the clinical management of the normal pressure hydrocephalus.OBJECTIVE:To develop a ML model that accurately identifies patients as candidates for permanent cerebral spinal fluid shunt implantation using only intracranial pressure and electrocardiogram signals recorded throughout LIT.METHODS:This was a single-center cohort study of prospectively collected data of 96 patients who underwent LIT and 5-day external lumbar cerebral spinal fluid drainage (external lumbar drainage) as a reference diagnostic method. A set of selected 48 intracranial pressure/electrocardiogram complex signal waveform features describing nonlinear behavior, wavelet transform spectral signatures, or recurrent map patterns were calculated for each patient. After applying a leave-one-out cross-validation training–testing split of the data set, we trained and evaluated the performance of various state-of-the-art ML algorithms.RESULTS:The highest performing ML algorithm was the eXtreme Gradient Boosting. This model showed a good calibration and discrimination on the testing data, with an area under the receiver operating characteristic curve of 0.891 (accuracy: 82.3%, sensitivity: 86.1%, and specificity: 73.9%) obtained for 8 selected features. Our ML model clearly outperforms the classical Rout-based manual classification commonly used in clinical practice with an accuracy of 62.5%.CONCLUSION:This study successfully used the ML approach to predict the outcome of a 5-day external lumbar drainage and hence which patients are likely to benefit from permanent shunt implantation. Our automated ML model thus enhances the diagnostic utility of LIT in management.

This review evaluates the current evidence for the clinical management of congenital internal carotid artery hypoplasia (CICAH). We summarise clinical presentations diagnostic standards, imaging recommendations, treatment and follow-up. The review was prompted by a case of CICAH in a 50-year-old female who presented to our neurosurgery clinic with an acute episode of vertigo. The patient underwent CT angiogram, which showed an unusually low right carotid bifurcation. The right internal carotid artery (ICA) was hypoplastic, and the A1 segment of the anterior cerebral artery (ACA) was absent. Skull base CT showed an ipsilateral hypoplastic carotid canal. To summarise current evidence for clinical management of CICAH we followed PRISMA guidelines to identify papers meeting our predefined inclusion criteria. We searched three databases using the terms ‘ICA’ and ‘Hypoplasia’. We reviewed 41 papers meeting our criteria. 34 were clinical reports. We performed a data extraction and quality appraisal on these reports. We found that CICAH may be less rare than previously described. Blood pressure control in CICAH is crucial due to the increased risk of stroke and aneurysm formation. Follow-up imaging is strongly recommended. Carotid doppler sonography is a powerful and underutilised diagnostic tool, and carotid canal hypoplasia is not a pathognomic sign. In conclusion, clinicians should be alert to anatomic variations such as CICAH because these produce haemodynamic changes that may have serious clinical consequences. We recommend a central registry of patients with CICAH in order to understand the longer-term natural history of the condition.