scholarly journals Evaluating Usability of a Touchless Image Viewer in the Operating Room

2020 ◽  
Vol 11 (01) ◽  
pp. 088-094
Author(s):  
Markus Bockhacker ◽  
Hannah Syrek ◽  
Max Elstermann von Elster ◽  
Sebastian Schmitt ◽  
Henning Roehl
Keyword(s):  
Neural Network ◽  
Operating Room ◽  
Real Life ◽  
Image Data ◽  
Patient Specific ◽  
Image Viewer ◽  
System Usability Scale ◽  
Key Factor ◽  
Real Life Situation ◽  
Easy Integration

Abstract Background Availability of patient-specific image data, gathered from preoperatively conducted studies, like computed tomography scans and magnetic resonance imaging studies, during a surgical procedure is a key factor for surgical success and patient safety. Several alternative input methods, including recognition of hand gestures, have been proposed for surgeons to interact with medical image viewers during an operation. Previous studies pointed out the need for usability evaluation of these systems. Objectives We describe the accuracy and usability of a novel software system, which integrates gesture recognition via machine learning into an established image viewer. Methods This pilot study is a prospective, observational trial, which asked surgeons to interact with software to perform two standardized tasks in a sterile environment, modeled closely to a real-life situation in an operating room. To assess usability, the validated “System Usability Scale” (SUS) was used. On a technical level, we also evaluated the accuracy of the underlying neural network. Results The neural network reached 98.94% accuracy while predicting the gestures during validation. Eight surgeons with an average of 6.5 years of experience participated in the usability study. The system was rated on average with 80.25 points on the SUS. Conclusion The system showed good overall usability; however, additional areas of potential improvement were identified and further usability studies are needed. Because the system uses standard PC hardware, it made for easy integration into the operating room.

scholarly journals Investigation of the Association between Video Game Usage, Personality, Psychological Needs, and Wellbeing

2021 ◽  
Author(s):  
◽  
Florian Flueggen
Keyword(s):  
Video Game ◽  
Computer Games ◽  
Real Life ◽  
Psychological Needs ◽  
Basic Psychological Needs ◽  
Game Studies ◽  
Future Research ◽  
Key Factor ◽  
Real Life Situation ◽  
Key Aspects

<p>Playing computer games has often been theorised to be linked to the wellbeing of users. However, the variables involved and the relationships and interactions between them have not been established. The purpose of the present study was to investigate, whether there are core aspects of game usage that are related to increased or decreased wellbeing, and the extent to which these depend on players’ real-life situations. The project comprised three studies and used an exploratory sequential mixed-methods design. In the first study, the ways in which players use games were investigated. To identify the key aspects of game usage for distinguishing and describing how players use games, in-depth interviews were conducted with 23 players of different games. This data and two subsequent quantitative tests, the first with 314 participants and the second with 770 participants, were used to develop a game-usage questionnaire and a framework with eight factors. The relationship between game usage and wellbeing was investigated in a longitudinal study conducted over nine months with 531 participants. Personality – as proxy for internal characteristics – and basic psychological needs – as proxy for participants’ situations in life – were taken into account as potential moderators of that relationship. Results showed that the overall correlations between game usage and wellbeing are weak and subsumed by players’ needs and personality. However, there were interactions between game usage and needs: Some game usage factors seem to directly reflect real-life situations and wellbeing; others seem to be common responses to real-life situations with no impact on wellbeing; and others again appear to impact wellbeing depending on the real-life situation. Social game usage seems to be a key factor with relevance for wellbeing. The contribution of this thesis is twofold. It provides a general framework of game usage that can be used in the field of game studies to interpret and compare findings more meaningfully, and it was shown that it is important to consider a person’s game usage in context of their real-life situations. In addition, main game usage factors for future research on wellbeing and digital games are suggested.</p>

scholarly journals ВЫБОР МОДЕЛИ БИОЛОГИЧЕСКОЙ НЕЙРОННОЙ СЕТИ ДЛЯ СЕГМЕНТАЦИИ ИЗОБРАЖЕНИЯ БИОЖИДКОСТНОЙ ПОВЕРХНОСТИ

2019 ◽  
pp. 78-93
Author(s):  
М.Е. Семенов ◽  
Т.Ю. Заблоцкая
Keyword(s):  
Neural Network ◽  
Pattern Recognition ◽  
Network Architecture ◽  
Real Life ◽  
Image Data ◽  
Network Models ◽  
Digital Processing ◽  
Neural Network Models ◽  
Biological Neural Network ◽  
Delay Differential

In the paper, the biological neural network models are analyzed with a purpose to solve the problems of segmentation and pattern recognition when applied to the bio-liquid facies obtained by the cuneiform dehydration method. The peculiarities of the facies’ patterns and the key steps of their digital processing are specified in the frame of the pattern recognition. Feasibility of neural network techniques for the different image data level digital processing is reviewed as well as for image segmentation. The real-life biological neural network architecture concept is described using the mechanisms of the electrical input-output membrane voltage and both induced and endogenic (spontaneous) activities of the neural clusters when spiking. The mechanism of spike initiation is described for metabotropic and ionotropic receptive clusters with the nature of environmental exciting impact specified. Also, the mathematical models of biological neural networks that comprise ot only functional nonlinearities but the hysteretic ones are analyzed and the reasons are given for preference of the mathematical model with delay differential equations is chosen providing its applicability for modeling a single neuron and neural network as well. В работе рассматривается применение моделей биологической нейронной сети для сегментации изображения фации биожидкости, полученной методом клиновидной дегидратации. Выделены основные характерные особенности, присущие паттернам фаций биожидкостей, а также основные этапы их цифровой обработки в рамках задачи распознавания образов. Проведен анализ использования искусственных нейронных сетей для цифровой обработки изображений для разных уровней представления данных; сделан обзор основных нейросетевых методов сегментации. Описан принцип построения биологически достоверных искусственных нейронных сетей, использующих механизмы изменения мембранного потенциала нейронов и учитывающих при генерации спайка как вызванную активность, так и эндогенную (спонтанную) активность нейронных кластеров. Описан механизм инициации спайка для метаботропных и ионотропных рецептивных кластеров с указанием природы запускающего внешнего воздействия. Проведен анализ существующих математических моделей биологических нейросетей, содержащих помимо обычных функциональных нелинейностей нелинейности гистерезисной природы. Сделан выбор в пользу математической модели, использующей дифференциальные уравнения с запаздыванием, которые могут быть применены как для описания отдельного биологического нейрона, так и для описания работы нейронной сети.

scholarly journals Investigation of the Association between Video Game Usage, Personality, Psychological Needs, and Wellbeing

2021 ◽  
Author(s):  
◽  
Florian Flueggen
Keyword(s):  
Video Game ◽  
Computer Games ◽  
Real Life ◽  
Psychological Needs ◽  
Basic Psychological Needs ◽  
Game Studies ◽  
Future Research ◽  
Key Factor ◽  
Real Life Situation ◽  
Key Aspects

<p>Playing computer games has often been theorised to be linked to the wellbeing of users. However, the variables involved and the relationships and interactions between them have not been established. The purpose of the present study was to investigate, whether there are core aspects of game usage that are related to increased or decreased wellbeing, and the extent to which these depend on players’ real-life situations. The project comprised three studies and used an exploratory sequential mixed-methods design. In the first study, the ways in which players use games were investigated. To identify the key aspects of game usage for distinguishing and describing how players use games, in-depth interviews were conducted with 23 players of different games. This data and two subsequent quantitative tests, the first with 314 participants and the second with 770 participants, were used to develop a game-usage questionnaire and a framework with eight factors. The relationship between game usage and wellbeing was investigated in a longitudinal study conducted over nine months with 531 participants. Personality – as proxy for internal characteristics – and basic psychological needs – as proxy for participants’ situations in life – were taken into account as potential moderators of that relationship. Results showed that the overall correlations between game usage and wellbeing are weak and subsumed by players’ needs and personality. However, there were interactions between game usage and needs: Some game usage factors seem to directly reflect real-life situations and wellbeing; others seem to be common responses to real-life situations with no impact on wellbeing; and others again appear to impact wellbeing depending on the real-life situation. Social game usage seems to be a key factor with relevance for wellbeing. The contribution of this thesis is twofold. It provides a general framework of game usage that can be used in the field of game studies to interpret and compare findings more meaningfully, and it was shown that it is important to consider a person’s game usage in context of their real-life situations. In addition, main game usage factors for future research on wellbeing and digital games are suggested.</p>

scholarly journals Complex wall modeling for hemodynamic simulations of intracranial aneurysms based on histologic images

2021 ◽  
Vol 16 (4) ◽  
pp. 597-607
Author(s):  
Annika Niemann ◽  
Samuel Voß ◽  
Riikka Tulamo ◽  
Simon Weigand ◽  
Bernhard Preim ◽  
...  
Keyword(s):  
Wall Thickness ◽  
Vessel Wall ◽  
Intracranial Aneurysms ◽  
Geometric Model ◽  
Image Data ◽  
Outer Wall ◽  
3D Models ◽  
Patient Specific ◽  
Intracranial Vessel ◽  
Hemodynamic Simulations

Abstract Purpose For the evaluation and rupture risk assessment of intracranial aneurysms, clinical, morphological and hemodynamic parameters are analyzed. The reliability of intracranial hemodynamic simulations strongly depends on the underlying models. Due to the missing information about the intracranial vessel wall, the patient-specific wall thickness is often neglected as well as the specific physiological and pathological properties of the vessel wall. Methods In this work, we present a model for structural simulations with patient-specific wall thickness including different tissue types based on postmortem histologic image data. Images of histologic 2D slices from intracranial aneurysms were manually segmented in nine tissue classes. After virtual inflation, they were combined into 3D models. This approach yields multiple 3D models of the inner and outer wall and different tissue parts as a prerequisite for subsequent simulations. Result We presented a pipeline to generate 3D models of aneurysms with respect to the different tissue textures occurring in the wall. First experiments show that including the variance of the tissue in the structural simulation affect the simulation result. Especially at the interfaces between neighboring tissue classes, the larger influence of stiffer components on the stability equilibrium became obvious. Conclusion The presented approach enables the creation of a geometric model with differentiated wall tissue. This information can be used for different applications, like hemodynamic simulations, to increase the modeling accuracy.

scholarly journals Automatically Determining Lumbar Load during Physically Demanding Work: A Validation Study

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2476
Author(s):  
Charlotte Christina Roossien ◽  
Christian Theodoor Maria Baten ◽  
Mitchel Willem Pieter van der Waard ◽  
Michiel Felix Reneman ◽  
Gijsbertus Jacob Verkerke
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Discriminant Validity ◽  
Real Life ◽  
Working Environment ◽  
Physically Active ◽  
Perceived Workload ◽  
Artificial Neural ◽  
Lumbar Load ◽  
Physically Demanding Work

A sensor-based system using inertial magnetic measurement units and surface electromyography is suitable for objectively and automatically monitoring the lumbar load during physically demanding work. The validity and usability of this system in the uncontrolled real-life working environment of physically active workers are still unknown. The objective of this study was to test the discriminant validity of an artificial neural network-based method for load assessment during actual work. Nine physically active workers performed work-related tasks while wearing the sensor system. The main measure representing lumbar load was the net moment around the L5/S1 intervertebral body, estimated using a method that was based on artificial neural network and perceived workload. The mean differences (MDs) were tested using a paired t-test. During heavy tasks, the net moment (MD = 64.3 ± 13.5%, p = 0.028) and the perceived workload (MD = 5.1 ± 2.1, p < 0.001) observed were significantly higher than during the light tasks. The lumbar load had significantly higher variances during the dynamic tasks (MD = 33.5 ± 36.8%, p = 0.026) and the perceived workload was significantly higher (MD = 2.2 ± 1.5, p = 0.002) than during static tasks. It was concluded that the validity of this sensor-based system was supported because the differences in the lumbar load were consistent with the perceived intensity levels and character of the work tasks.

Examining Sensor Agreement in Neural Network Blood Glucose Prediction

2021 ◽  
pp. 193229682110182
Author(s):  
Aaron P. Tucker ◽  
Arthur G. Erdman ◽  
Pamela J. Schreiner ◽  
Sisi Ma ◽  
Lisa S. Chow
Keyword(s):  
Neural Network ◽  
Blood Glucose ◽  
Patient Specific ◽  
Sensor Location ◽  
Effective Care ◽  
Freestyle Libre ◽  
Patients With Diabetes ◽  
Interstitial Glucose ◽  
Continuous Glucose Monitors ◽  
Glucose Prediction

Successful measurements of interstitial glucose are a key component in providing effective care for patients with diabetes. Recently, there has been significant interest in using neural networks to forecast future glucose values from interstitial measurements collected by continuous glucose monitors (CGMs). While prediction accuracy continues to improve, in this work we investigated the effect of physiological sensor location on neural network blood glucose forecasting. We used clinical data from patients with Type 2 Diabetes who wore blinded FreeStyle Libre Pro CGMs (Abbott) on both their right and left arms continuously for 12 weeks. We trained patient-specific prediction algorithms to test the effect of sensor location on neural network forecasting ( N = 13, Female = 6, Male = 7). In 10 of our 13 patients, we found at least one significant ( P < .05) increase in forecasting error in algorithms which were tested with data taken from a different location than data which was used for training. These reported results were independent from other noticeable physiological differences between subjects (eg, height, age, weight, blood pressure) and independent from overall variance in the data. From these results we observe that CGM location can play a consequential role in neural network glucose prediction.

scholarly journals Bayesian adaptive design of early-phase clinical trials for precision medicine based on cancer biomarkers

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Shinjo Yada
Keyword(s):  
Neural Network ◽  
Clinical Trials ◽  
Precision Medicine ◽  
Early Phase ◽  
Patient Specific ◽  
Specific Gene ◽  
Cancer Type ◽  
Background Characteristics ◽  
Number Of Patients ◽  
Early Phase Clinical Trials

Abstract Cancer tissue samples obtained via biopsy or surgery were examined for specific gene mutations by genetic testing to inform treatment. Precision medicine, which considers not only the cancer type and location, but also the genetic information, environment, and lifestyle of each patient, can be applied for disease prevention and treatment in individual patients. The number of patient-specific characteristics, including biomarkers, has been increasing with time; these characteristics are highly correlated with outcomes. The number of patients at the beginning of early-phase clinical trials is often limited. Moreover, it is challenging to estimate parameters of models that include baseline characteristics as covariates such as biomarkers. To overcome these issues and promote personalized medicine, we propose a dose-finding method that considers patient background characteristics, including biomarkers, using a model for phase I/II oncology trials. We built a Bayesian neural network with input variables of dose, biomarkers, and interactions between dose and biomarkers and output variables of efficacy outcomes for each patient. We trained the neural network to select the optimal dose based on all background characteristics of a patient. Simulation analysis showed that the probability of selecting the desirable dose was higher using the proposed method than that using the naïve method.

scholarly journals The International Radiomics Platform – An Initiative of the German and Austrian Radiological Societies

2020 ◽  
Author(s):  
Daniel Overhoff ◽  
Peter Kohlmann ◽  
Alex Frydrychowicz ◽  
Sergios Gatidis ◽  
Christian Loewe ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Image Data ◽  
Quality Parameters ◽  
Image Features ◽  
Performance Criteria ◽  
Data Sets ◽  
Proof Of Concept ◽  
Concept Study ◽  
Artificial Intelligence Methods

Purpose The DRG-ÖRG IRP (Deutsche Röntgengesellschaft-Österreichische Röntgengesellschaft international radiomics platform) represents a web-/cloud-based radiomics platform based on a public-private partnership. It offers the possibility of data sharing, annotation, validation and certification in the field of artificial intelligence, radiomics analysis, and integrated diagnostics. In a first proof-of-concept study, automated myocardial segmentation and automated myocardial late gadolinum enhancement (LGE) detection using radiomic image features will be evaluated for myocarditis data sets. Materials and Methods The DRG-ÖRP IRP can be used to create quality-assured, structured image data in combination with clinical data and subsequent integrated data analysis and is characterized by the following performance criteria: Possibility of using multicentric networked data, automatically calculated quality parameters, processing of annotation tasks, contour recognition using conventional and artificial intelligence methods and the possibility of targeted integration of algorithms. In a first study, a neural network pre-trained using cardiac CINE data sets was evaluated for segmentation of PSIR data sets. In a second step, radiomic features were applied for segmental detection of LGE of the same data sets, which were provided multicenter via the IRP. Results First results show the advantages (data transparency, reliability, broad involvement of all members, continuous evolution as well as validation and certification) of this platform-based approach. In the proof-of-concept study, the neural network demonstrated a Dice coefficient of 0.813 compared to the expert's segmentation of the myocardium. In the segment-based myocardial LGE detection, the AUC was 0.73 and 0.79 after exclusion of segments with uncertain annotation.The evaluation and provision of the data takes place at the IRP, taking into account the FAT (fairness, accountability, transparency) and FAIR (findable, accessible, interoperable, reusable) criteria. Conclusion It could be shown that the DRG-ÖRP IRP can be used as a crystallization point for the generation of further individual and joint projects. The execution of quantitative analyses with artificial intelligence methods is greatly facilitated by the platform approach of the DRG-ÖRP IRP, since pre-trained neural networks can be integrated and scientific groups can be networked.In a first proof-of-concept study on automated segmentation of the myocardium and automated myocardial LGE detection, these advantages were successfully applied.Our study shows that with the DRG-ÖRP IRP, strategic goals can be implemented in an interdisciplinary way, that concrete proof-of-concept examples can be demonstrated, and that a large number of individual and joint projects can be realized in a participatory way involving all groups. Key Points:  Citation Format

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