Serious game for radiotherapy training.

BACKGROUND: Cancer patients are often treated with radiation, therefore increasing their exposure to high energy emissions. In such cases, medical errors may be threatening or fatal, inducing the need to innovate new methods for maximum reduction of irreversible events. Training is an efficient and methodical tool to subject professionals to the real world and heavily educate them on how to perform with minimal errors. An evolving technique for this is Serious Gaming that can fulfill this purpose, especially with the rise of COVID-19 and the shift to the online world, by realistic and visual simulations built to present engaging scenarios. This paper presents the first Serious Game for Lung Cancer Radiotherapy training that embodies Biomedical Engineering principles and clinical experience to create a realistic and precise platform for coherent training. METHODS: To develop the game, thorough 3D modeling, animation, and gaming fundamentals were utilized to represent the whole clinical process of treatment, along with the scores and progress of every player. The model's goal is to output coherency and organization for students' ease of use and progress tracking, and to provide a beneficial educational experience supplementary to the users' training. It aims to also expand their knowledge and use of skills in critical cases where they must perform crucial decision-making and procedures on patients of different cases. RESULTS: At the end of this research, one of the accomplished goals consists of building a realistic model of the different equipment and tools accompanied with the radiotherapy process received by the patient on Maya 2018, including the true beam table, gantry, X-ray tube, CT Scanner, and so on. The serious game itself was then implemented on Unity Scenes with the built models to create a gamified authentic environment that incorporates the 5 main series of steps; Screening, Contouring, External Beam Planning, Plan Evaluation, Treatment, to simulate the practical workflow of an actual Oncology treatment delivery for lung cancer patients. CONCLUSION: This serious game provides an educational and empirical space for training and practice that can be used by students, trainees, and professionals to expand their knowledge and skills in the aim of reducing potential errors.

Electrodermal Activity Based Pre-surgery Stress Detection Using a Wrist Wearable.

Surgery is a particularly potent stressor and the detrimental effects of stress on people undergoing any surgery is indisputable. When left unchecked, the pre-surgery stress adversely impacts people's physical and psychological well-being, and may even evolve into severe pathological states. Therefore, it is essential to identify levels of preoperative stress in surgical patients. This paper focuses on developing an automatic pre-surgery stress detection scheme based on electrodermal activity (EDA). The measurement set up involves a wrist wearable that monitors EDA of a subject continuously in the most non-invasive and unobtrusive manner. Data were collected from 41 subjects [17 females and 24 males, age: 54.8 ± 16.8 years (mean ± SD)], who subsequently underwent different surgical procedures at the Sri Ramakrishna Hospital, Coimbatore, India. A supervised machine learning algorithm that detects motion artifacts in the recorded EDA data was developed. It yielded an accuracy of 97.83% on a new user dataset. The clean EDA data were further analyzed to determine low, moderate, and high levels of stress. A novel localized supervised learning scheme based on the adaptive partitioning of the dataset was adopted for stress detection. Consequently, the interindividual variability in the EDA due to person-specific factors such as the sweat gland density and skin thickness, which may lead to erroneous classification, could be eliminated. The scheme yielded a classification accuracy of 85.06% on a new user dataset and proved to be more effective than the general supervised classification model.

Wireless monitoring of liver hemodynamics in vivo.

Liver transplants have their highest technical failure rate in the first two weeks following surgery. Currently, there are limited devices for continuous, real-time monitoring of the graft. In this work, a three wavelengths system is presented that combines near-infrared spectroscopy and photoplethysmography with a processing method that can uniquely measure and separate the venous and arterial oxygen contributions. This strategy allows for the quantification of tissue oxygen consumption used to study hepatic metabolic activity and to relate it to tissue stress. The sensor is battery operated and communicates wirelessly with a data acquisition computer which provides the possibility of implantation provided sufficient miniaturization. In two in vivo porcine studies, the sensor tracked perfusion changes in hepatic tissue during vascular occlusions with a root mean square error (RMSE) of 0.135 mL/min/g of tissue. We show the possibility of using the pulsatile wave to measure the arterial oxygen saturation similar to pulse oximetry. The signal is also used to extract the venous oxygen saturation from the direct current (DC) levels. Arterial and venous oxygen saturation changes were measured with an RMSE of 2.19% and 1.39% respectively when no vascular occlusions were induced. This error increased to 2.82% and 3.83% when vascular occlusions were induced during hypoxia. These errors are similar to the resolution of a commercial oximetry catheter used as a reference. This work is the first realization of a wireless optical sensor for continuous monitoring of hepatic hemodynamics.