A Neonatal Phantom for Vital Signs Simulation

A Physical Phantom for the Simulation of Neonatal Thermoregulation

Preterm infants are at an increased health risk due to their low maturity. To monitor their health, vital signs are measured using contact-based methods. The adhesive sensors used to detect body temperature can damage the sensitive skin of neonates. Thus, a subject of current research is non-invasive measurement methods based on infrared thermography. In this context, thermal phantoms can be used to develop contactless temperature measurement systems and, furthermore, investigate the thermal behavior of preterm infants. In this work, an improved thermal phantom is introduced to simulate the thermoregulation of a premature infant. The shape and size are adapted to the body of a premature infant in the 29th week of pregnancy. The phantom consists of a 3D-printed frame to which carbon fiber heating elements and Pt1000 temperature sensors are attached. The frame is enclosed by a thermally conductive skin layer made of a silicone boron nitride mixture. Ball joints allow the body parts to tilt and rotate, enabling the phantom to model different body postures. Using PI controllers, the thermal phantom can achieve desired temperatures in 13 different areas of the body while maintaining a homogeneous temperature distribution on the skin surface. In addition, pathological temperature scenarios such as a central-peripheral temperature difference or a change in body temperature can be simulated with a maximum deviation of ± 0.4 °C.

FEATURES OF THE PROCESS OF TRAINING IN EDUCATIONAL MEDICAL INSTITUTIONS OF UKRAINE AT THE PRESENT STAGE. PART 2. REACTION OF HIGHER EDUCATIONAL INSTITUTIONS TO DISTANCE LEARNING

Higher education is one of the areas most affected by the Covid-19 pandemic and martial law. Against the backdrop of severe restrictions, universities faced the issue of the existing opportunities for the implementation of educational programs, the need to change the format of the educational process with the transition mainly to electronic educational technologies. Under these conditions, it was necessary to consolidate all the forces and resources of the university community. The governments of many countries have recognized the need to provide infrastructural and technological support to educational institutions. Thanks to the institutional support of the state and relevant ministries, universities managed to reduce financial losses and implement initiatives for continuous education. These measures have contributed to the sustainability of universities. In response to the COVID-19 pandemic, educational institutions all over the world have adopted different approaches and made significant changes to the education system itself in accordance with their resources and capabilities.

A Setup for Camera-Based Detection of Simulated Pathological States Using a Neonatal Phantom

Premature infants are among the most vulnerable patients in a hospital. Due to numerous complications associated with immaturity, a continuous monitoring of vital signs with a high sensitivity and accuracy is required. Today, wired sensors are attached to the patient's skin. However, adhesive electrodes can be potentially harmful as they can damage the very thin immature skin. Although unobtrusive monitoring systems using cameras show the potential to replace cable-based techniques, advanced image processing algorithms are data-driven and, therefore, need much data to be trained. Due to the low availability of public neonatal image data, a patient phantom could help to implement algorithms for the robust extraction of vital signs from video recordings. In this work, a camera-based system is presented and validated using a neonatal phantom, which enabled a simulation of common neonatal pathologies such as hypo-/hyperthermia and brady-/tachycardia. The implemented algorithm was able to continuously measure and analyze the heart rate via photoplethysmography imaging with a mean absolute error of 0.91 bpm, as well as the distribution of a neonate's skin temperature with a mean absolute error of less than 0.55 °C. For accurate measurements, a temperature gain offset correction on the registered image from two infrared thermography cameras was performed. A deep learning-based keypoint detector was applied for temperature mapping and guidance for the feature extraction. The presented setup successfully detected several levels of hypo- and hyperthermia, an increased central-peripheral temperature difference, tachycardia and bradycardia.