Electronic Documentation of Intraoperative Observation of Cystoscopic Procedures Using the cMDX Information System.

PURPOSE: Accurate documentation of lesions during transurethral resection of bladder tumors (TURBT) is essential for precise diagnosis, treatment planning, and follow-up care. However, optimizing schematic documentation techniques for bladder lesions has received limited attention. MATERIALS AND METHODS: This prospective observational study used a cMDX-based documentation system that facilitates graphical representation, a lesion-specific questionnaire, and heatmap analysis with a posterization effect. We designed a graphical scheme for bladder covering bladder landmarks to visualize anatomic features and to document the lesion location. The lesion-specific questionnaire was integrated for comprehensive lesion characterization. Finally, spatial analyses were applied to investigate the anatomic distribution patterns of bladder lesions. RESULTS: A total of 97 TURBT cases conducted between 2021 and 2023 were included, identifying 176 lesions. The lesions were distributed in different bladder areas with varying frequencies. The distribution pattern, sorted by frequency, was observed in the following areas: posterior, trigone, lateral right and anterior, and lateral left and dome. Suspicious levels were assigned to the lesions, mostly categorized either as indeterminate or moderate. Lesion size analysis revealed that most lesions fell between 5 and 29 mm. CONCLUSION: The study highlights the potential of schematic documentation techniques for informed decision making, quality assessment, primary research, and secondary data utilization of intraoperative data in the context of TURBT. Integrating cMDX and heatmap analysis provides valuable insights into lesion distribution and characteristics.

Examining Mental Workload Relating to Digital Health Technologies in Health Care: Systematic Review.

BACKGROUND: The workload in health care is increasing and hence, mental health issues are on the rise among health care professionals (HCPs). The digitization of patient care could be related to the increase in stress levels. It remains unclear whether the health information system or systems and digital health technologies (DHTs) being used in health care relieve the professionals or whether they represent a further burden. The mental construct that best describes this burden of technologies is mental workload (MWL). The measurement methods of MWL are particularly relevant in this sensitive setting. OBJECTIVE: This review aimed to address 2 different but related objectives: identifying the factors that contribute to the MWL of HCPs when using DHT and examining and exploring the applied assessments for the measurement of MWL with a special focus on eye tracking. METHODS: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) 2020 statement, we conducted a systematic review and processed a literature search in the following databases: MEDLINE (PubMed), Web of Science, Academic Search Premier and CINAHL (EBSCO), and PsycINFO. Studies were eligible if they assessed the MWL of HCPs related to DHT. The review was conducted as per the following steps: literature search, article selection, data extraction, quality assessment (using the Standard Quality Assessment Criteria for Evaluation Primary Research Papers From a Variety of Fields [QualSyst]), data analysis, and data synthesis (narrative and tabular). The process was performed by 2 reviewers (in cases of disagreement, a third reviewer was involved). RESULTS: The literature search process resulted in 25 studies that fit the inclusion criteria and examined the MWL of health care workers resulting from the use of DHT in health care settings. Most studies had sample sizes of 10-50 participants, were conducted in the laboratory, and had quasi-experimental or cross-sectional designs. The main results can be grouped into two categories: assessment methods and factors related to DHT that contribute to MWL. Most studies applied subjective methods for the assessment of MWL. Eye tracking did not play a major role in the selected studies. The factors contributing to a higher MWL were clustered into organizational and systemic factors. CONCLUSIONS: Our review of 25 papers shows a diverse assessment approach toward the MWL of HCPs related to DHT as well as 2 groups of relevant contributing factors to MWL. Our results are limited in terms of interpretability and causality due to methodological weaknesses of the included studies and may be limited by some shortcomings in the search process. Future research should concentrate on adequate assessments of the MWL of HCPs dependent on the setting, the evaluation of quality criteria, and further assessment of the contributing factors to MWL. TRIAL REGISTRATION: PROSPERO (International Prospective Register of Systematic Reviews) CRD42021233271; https://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42021233271.

Artificial Intelligence-Based Prognostic Model for Urologic Cancers: A SEER-Based Study.

BACKGROUND: Prognostication is essential to determine the risk profile of patients with urologic cancers. METHODS: We utilized the SEER national cancer registry database with approximately 2 million patients diagnosed with urologic cancers (penile, testicular, prostate, bladder, ureter, and kidney). The cohort was randomly divided into the development set (90%) and the out-held test set (10%). Modeling algorithms and clinically relevant parameters were utilized for cancer-specific mortality prognosis. The model fitness for the survival estimation was assessed using the differences between the predicted and observed Kaplan-Meier estimates on the out-held test set. The overall concordance index (c-index) score estimated the discriminative accuracy of the survival model on the test set. A simulation study assessed the estimated minimum follow-up duration and time points with the risk stability. RESULTS: We achieved a well-calibrated prognostic model with an overall c-index score of 0.800 (95% CI: 0.795-0.805) on the representative out-held test set. The simulation study revealed that the suggestions for the follow-up duration covered the minimum duration and differed by the tumor dissemination stages and affected organs. Time points with a high likelihood for risk stability were identifiable. CONCLUSIONS: A personalized temporal survival estimation is feasible using artificial intelligence and has potential application in clinical settings, including surveillance management.

[Perception and mindfulness-based occupational therapy in people with mental disorders-A retrospective observational study on the SELWA-treatment]. / Wahrnehmungs- und achtsamkeitsbasierte Ergotherapie bei Menschen mit psychischen Störungen ­ Eine retrospektive Beobachtungsstudie zur Anwendung der SELWA-Behandlung.

During occupational therapeutic treatment of clients with mental disorders, perception and mindfulness-based techniques are used. However, little is known regarding relevant outcomes. Aim of the present study is to describe the results of a perception and mindfulness-based occupational therapeutic intervention (self-control techniques using perception-based methods (SELWA®) by S. Thielen) regarding the outcomes occupational performance and satisfaction in self-care, productivity and leisure, as well as concentration. The data of 28 clients (22 â™€, 6 â™‚; mean age = 42.8 (±SD 14.7) years) with mental disorders, that were collected before and after prescribed occupational therapeutic treatment, were analyzed. The outcomes were quantified using the Canadian Occupational Performance Measure (COPM) and the revision test, respectively. Significance of changes after the intervention was tested using the Wilcoxon-Signed Rank Test (p < 0.05). Effect sizes Cohen's dz and r were determined to evaluate the meaningfulness of changes. The occupational performance as well as the satisfaction in the COPM improved significantly after the therapeutic intervention (p < 0.001; dz = 2.37, r = 0.77 and dz = 2.24, r = 0.75). Moreover, the clients improved significantly in the revision test after the therapeutic intervention (p < 0.001; dz = 0.65, r = 0.31). Clients with mental disorders seem to benefit meaningfully from the SELWA®-treatment by S. Thielen regarding occupational performance and satisfaction in self-care, productivity and leisure. Furthermore, a moderate improvement of concentration seems to occur after the therapeutic intervention.

How Does Mental Workload Influence the Adoption of Clinical Information Systems: An Exploratory Study.

Mental workload and technology acceptance are relevant factors that relate to use behavior and performance. Studies show a potential moderating effect of mental workload on predictors of technology acceptance. Aim of this study was the investigation of predictors of technology acceptance (UTAUT) related to clinical information systems and their relation to mental workload. This quasi-experimental study with 48 participants used the following measures: NASA TLX and UTAUT questionnaire. Participants had to perform three tasks on a clinical information system as well as four task-levels of the n-back task with increasing difficulty. Analyses show a high level of technology acceptance (M=3.82, SD=.76) and confirm performance expectancy as the most relevant predictor of behavioral intention (ß=.48, p<.001). A linear regression showed that a high level of mental workload has an influence on performance expectancy (F1,46=8.438, p<.05). The study shows an influence of mental workload on acceptance, the strength and role of which (e.g. moderation) needs to be further investigated, especially in the context of other determinants.

Comparing the Acceptance of Mobile Hypertension Apps for Disease Management Among Patients Versus Clinical Use Among Physicians: Cross-sectional Survey.

BACKGROUND: High blood pressure or hypertension is a vastly prevalent chronic condition among adults that can, if not appropriately treated, contribute to several life-threatening secondary diseases and events, such as stroke. In addition to first-line medication, self-management in daily life is crucial for tertiary prevention and can be supported by mobile health apps, including medication reminders. However, the prescription of medical apps is a relatively novel approach. There is limited information regarding the determinants of acceptance of such mobile health (mHealth) apps among patients as potential users and physicians as impending prescribers in direct comparison. OBJECTIVE: The present study aims to investigate the determinants of the acceptance of health apps (in terms of intention to use) among patients for personal use and physicians for clinical use in German-speaking countries. Moreover, we assessed patients' preferences regarding different delivery modes for self-care service (face-to-face services, apps, etc). METHODS: Based on an extended model of the unified theory of acceptance and use of technology (UTAUT2), we performed a web-based cross-sectional survey to explore the acceptance of mHealth apps for self-management of hypertension among patients and physicians in Germany. In addition to UTAUT2 variables, we measured self-reported self-efficacy, eHealth literacy, previous experiences with health apps, perceived threat to privacy, and protection motivation as additional determinants of mHealth acceptance. Data from 163 patients and 46 physicians were analyzed using hierarchical regression and mediation analyses. RESULTS: As expected, a significant influence of the unified theory of acceptance and use of technology (UTAUT) predictors on intentions to use hypertension apps was confirmed, especially for performance expectancy. Intention to use was moderate in patients (mean 3.5; SD 1.1; range 1-5) and physicians (mean 3.4, SD 0.9), and did not differ between both groups. Among patients, a higher degree of self-reported self-efficacy and protection motivation contributed to an increased explained variance in acceptance with R2=0.09, whereas eHealth literacy was identified as exerting a positive influence on physicians (increased R2=0.10). Furthermore, our findings indicated mediating effects of performance expectancy on the acceptance among patients but not among physicians. CONCLUSIONS: In summary, this study has identified performance expectancy as the most important determinant of the acceptance of mHealth apps for self-management of hypertension among patients and physicians. Concerning patients, we also identified mediating effects of performance expectancy on the relationships between effort expectancy and social influence and the acceptance of apps. Self-efficacy and protection motivation also contributed to an increase in the explained variance in app acceptance among patients, whereas eHealth literacy was a predictor in physicians. Our findings on additional determinants of the acceptance of health apps may help tailor educational material and self-management interventions to the needs and preferences of prospective users of hypertension apps in future research.

Examining the Mental Workload Associated With Digital Health Technologies in Health Care: Protocol for a Systematic Review Focusing on Assessment Methods.

BACKGROUND: The workload in health care is high; physicians and nurses report high stress levels due to a demanding environment where they often have to perform multiple tasks simultaneously. As a result, mental health issues among health care professionals (HCPs) are on the rise and the prevalence of errors in their daily tasks could increase. Processes of demographic change are partly responsible for even higher stress levels among HCPs. The digitization of patient care is intended to counteract these processes. However, it remains unclear whether these health information systems (HIS) and digital health technologies (DHT) support the HCPs and relieve stress, or if they represent a further burden. The mental construct that describes this burden of technologies is mental workload (MWL). Work in the clinic can be viewed as working in safety-critical environments. Particularly in this sensitive setting, the measurement methods of MWL are relevant, mainly due to their strongly differing levels of intrusiveness and sensitivity. The method of eye tracking could be a useful way to measure MWL directly in the field. OBJECTIVE: The systematic review aims to address the following questions: (1) In which manner do DHT contribute to the overall MWL of HCPs? (2) Can we observe a direct or indirect effect of DHT on MWL? (3) Which aspects or factors of DHT contribute to an increase in MWL? (4) Which methods/assessments are applied to measure MWL related to HIS/DHT? (5) What role does eye tracking/pupillometry play in the context of measuring MWL? (6) Which outcomes are being assessed via eye tracking? METHODS: Following the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) statement, we will conduct a systematic review. Based on the research questions, we define keywords that we then combine in search terms. The review follows the following steps: literature search, article selection, data extraction, risk of bias assessment, data analysis, and data synthesis. RESULTS: We expect results as well as a finalization of the review in the summer of 2021. CONCLUSIONS: This review will evaluate the impact of DHT on the MWL of HCPs. In addition, assessment methods of MWL in the context of digital technologies will be systematically analyzed. TRIAL REGISTRATION: PROSPERO (International Prospective Register of Systematic Reviews) CRD42021233271; https://www.crd.york.ac.uk/PROSPERO/display_record.php?ID=CRD42021233271. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/29126.

Which Factors of Digitisation Bias the Work-Related Stress of Healthcare Employees? A Systematic Review.

BACKGROUND: Digitisation affects our working environment. It demands new cognitive and digital skills of healthcare employees. Technostress and burnout are more likely to occur due to the additional workload. AIM: Objective is the identification of determinants affecting work related technostress. METHODOLOGY: We carried out a systematic review according to the PRISMA statement. For the identification of the digital factors, we applied an inductive content analysis based on Mayring's theory. RESULTS: Included studies showed the following factors to be relevant for coping with technostress: autonomy, competence, understanding of roles, time pressure, attitude, security and ergonomics. The emerging factors serve the regulation of stress in the healthcare system and contribute to better healthcare and higher occupational safety.

[Awareness and Use of Digital Health Services in Germany: A Cross-sectional Study Representative of the Population]. / Bekanntheit und Nutzung von digitalen Gesundheitsangeboten in Deutschland: eine bevölkerungsrepräsentative Querschnittsuntersuchung.

OBJECTIVES: In light of the current efforts of health policy to implement eHealth, the question arises which sections of the population already use online self-help in order to tailor them to users' needs. The present study aims to determine the differences in the use of health information and psychological online counseling based on socio-demographic variables, health status and previous illnesses. METHODS: The basis for the cross-sectional data analyses using logistic regression analysis was the innovation sample of the German socio-economic panel. Data were collected from September 2016 to February 2017, with 4802 participants aged between 17-95 years. RESULTS: Fifty-five percent of the sample searched for health information on the Internet, while 1.1% had experience with online counseling. Logistic regression analyses showed that online search for information was significantly determined by age (Odds Ratio (OR)=0.96; 95-%-CI=0.96-0.97), gender (OR=1.20; 95-%-CI=1.05-1.36), awareness of Internet therapy (OR=2.57; 95-%-CI=2.20-3.00), experience with psychotherapy (OR=1.40; 95-%-CI=1.16-1.69) and the diagnosis of asthma (OR=1.14; 95-%-CI=1.01-1.29) or stroke (OR=0.66; 95-%-CI=0.52-0.84). Regarding the use of online counseling, awareness of Internet therapy and experience with face-to-face psychotherapy proved to be significant determinants. CONCLUSION: For the first time, a reliable picture has become available of the determinants of the awareness of internet therapy and online self-help utilization among the German public that should enable target-group-specific strategies to improve the care situation.

Acceptance and Commitment Therapy for Major Depressive Disorder: Insights into a New Generation of Face-to-Face Treatment and Digital Self-Help Approaches.

Major depressive disorder (MDD) represents a key contributor to the global burden of mental illness given its relatively high lifetime prevalence, frequent comorbidity, and disability rates. Evidence-based treatment options for depression include pharmacotherapy and psychotherapy, such as cognitive behavioral therapy (CBT). Beyond traditional CBT, over 15 years ago, Hayes proclaimed a new generation of contextualistic and process-orientated so-called third wave of CBT interventions, including acceptance and commitment therapy (ACT). Using mindfulness and acceptance as well as commitment and behavior change processes, the transdiagnostic ACT approach aims to increase psychological flexibility as universal mechanism of behavior change and to build a value-driven orientation in life. ACT for MDD can be provided as either stand-alone individual, group, or self-help formats (e.g., apps) or combined with other approaches like behavioral activation. To date, a steadily growing empirical support from outcome and process research suggests the efficacy of ACT, which appears to work specifically through the six proposed core processes involved in psychological flexibility, such as defusion. In view of an ongoing interest of clinicians in "third-wave" CBTs and the important role of clients' preferences in providing therapy choices that work, the purpose of this chapter is to give a brief overview on the application of ACT in the treatment of MDD in adults.

Exploring User-Related Drivers of the Early Acceptance of Certified Digital Stress Prevention Programs in Germany.

Electronic mental health services represent innovative instruments to increase the dissemination of stress programs in primary prevention. However, little is known about facilitators of their uptake. This study aimed to explore determinants of the acceptance of centrally certified digital stress coping programs and preferences for service delivery modes among adult members of German statutory health insurances. Participants completed a multi-construct 45-item questionnaire covering acceptance of digital stress prevention (behavioral use intention) and potential predictors we assessed using hierarchical regression analysis-(1) socio-demographic variables and time spent online, (2) openness to experience, (3) perceived stress, and (4) attitudes toward e-mental health. Preferences in terms of the willingness to use online, face-to-face and blended programs were analyzed using paired t-tests. Participants (N = 171, 66% female, 18-69 years) reported a moderate acceptance of digital stress management (M = 2.76, SD = 1.16, range: 1-5). We identified younger age (ß = -0.16, P = .009), openness to experience (ß = 0.17, P = .003), and positive attitudes (ß = 0.61, P < .001) as predictors of acceptance (R 2 = .50, P < .001). Face-to-face was preferred over online (d = 0.40) and blended (d = 0.33), and blended over stand-alone online delivery mode (d = 0.19; all P < .001). Our findings indicate that promoting favorable attitudes toward digital stress prevention through tailored information may be a starting point to facilitate their adoption.

Combination possibility and deep learning model as clinical decision-aided approach for prostate cancer.

This study aims to introduce as proof of concept a combination model for classification of prostate cancer using deep learning approaches. We utilized patients with prostate cancer who underwent surgical treatment representing the various conditions of disease progression. All possible combinations of significant variables from logistic regression and correlation analyses were determined from study data sets. The combination possibility and deep learning model was developed to predict these combinations that represented clinically meaningful patient's subgroups. The observed relative frequencies of different tumor stages and Gleason score Gls changes from biopsy to prostatectomy were available for each group. Deep learning models and seven machine learning approaches were compared for the classification performance of Gleason score changes and pT2 stage. Deep models achieved the highest F1 scores by pT2 tumors (0.849) and Gls change (0.574). Combination possibility and deep learning model is a useful decision-aided tool for prostate cancer and to group patients with prostate cancer into clinically meaningful groups.

Determinant Factors of Public Acceptance of Stress Management Apps: Survey Study.

BACKGROUND: Chronic stress is a major public health concern. Mobile health (mHealth) apps can help promote coping skills in daily life and prevent stress-related issues. However, little is known about the determinant factors of public acceptance of stress management in relation to preferences for psychological services. OBJECTIVE: The aim of this survey study was to (1) assess determinant factors of public acceptance (behavioral use intention) of stress management apps based on an adapted and extended version of the Unified Theory of Acceptance and Use of Technology (UTAUT) model and (2) explore preferences for mHealth apps compared with other mental health services. METHODS: Using convenience sampling, participants completed a multiscale 54-item Web-based survey. Based on significant correlations with acceptance, hierarchical stepwise regression analysis was performed within three blocks: (1) background and stress-related control variables, (2) beliefs and attitudes toward using mHealth, and (3) the core UTAUT determinants. The preference for mHealth apps in comparison with nine other mental health services (operationalized as readiness to use) was analyzed using paired t tests. RESULTS: Of 141 participants, nearly half (69/141, 48.9%) indicated prior mHealth use. Acceptance of stress coping apps was moderate (mean 3.10, SD 1.03, range 1-5). Hierarchical stepwise regression including four of 11 variables (R2=.62; P=.01, f2=1.63) identified positive attitudes toward using mHealth for stress coping (beta=0.69, P<.001, 46% R2 increase above block 1, f2=0.85), skepticism/perceived risks (beta=-0.14, P=.01, f2=0.16), and stress symptoms (beta=0.12, P=.03, f2=0.14) as significant predictors of acceptance. UTAUT determinants added no predictive contribution beyond attitudes (all P>.05, R2 increase of 1%), whereas post hoc analysis showed significant R2 increases of attitudes and skepticism/perceived risks beyond UTAUT determinants (all P<.001, R2 increase of 13%). The readiness to use apps was equivalent to or significantly higher than most service types, but lower than information websites. CONCLUSIONS: Attitudes may be at least as predictive for the acceptance of stress management apps as for more elaborated outcome beliefs. Efforts aimed at improving the public adoption of mHealth could put more emphasis on the pleasant aspects of app use, address misconceptions, offer stress screening tools on health websites, and increase options to try high-quality apps.

Acceptance of mHealth Apps for Self-Management Among People with Hypertension.

BACKGROUND: Mobile health applications (mHealth apps) have the potential to help patients with chronic conditions such as hypertension by supporting self-management activities in daily life. However, the uptake of mHealth apps remains poor among patients. To improve the utilization of mHealth apps for hypertension, the analysis of the behavioral intention to use such applications must consider personality traits and illness-related perceptions. METHOD: Adults with hypertension in Germany and Austria filled out a self-administered questionnaire in a cross-sectional study based on the UTAUT-model in order to identify potential predictors for the behavioral intention to use mHealth applications as an indicator for their early acceptance. Beyond the four core determinants of acceptance of the UTAUT (performance expectancy, effort expectancy, social influence and facilitating conditions), self-efficacy, openness to experience and perceived health threat were analyzed as predictors. RESULTS: 145 participants (mean age 52.51 years, SD 14.33; 60% female) completed the survey. Acceptance was moderate on average (M = 3.26, SD = 1.07, min 1 to max 5). In a multiple hierarchical regression, performance expectancy and effort expectancy were confirmed as significant predictors of acceptance (step 1, R2 = .57, p < .001), while self-efficacy could not be confirmed (step 2, p = .87). In addition, perceived health threat (ß = .12, p < .05) and openness to experience (ß = .22, p < .001) had a significant influence on acceptance of mHealth apps for hypertension (step 3, overall model with R2 = .62). Age showed a negative association with the intention to use (ß = .22, p = .005) while no influence of gender could be found (p = .06). CONCLUSION: Above expectations regarding effectiveness and usability, openness to experience and perceived health threat make a significant contribution in predicting the acceptance of mHealth solutions in the field of chronic diseases.

Mental Workload Relating Health Information System - A Literature Review.

BACKGROUND: Assessing Mental Workload related to Health Information Systems can help to analyze weak points of the use of Health Information Systems and in health care work processes. Our objectives were to give an overview of current research and applied measurement methods as well as gaining insights into influencing factors of mental workload on the use of health information systems and vice versa. METHODS: We applied a structured literature research by searching for "mental workload" on PubMed. Studies were included into our review if they assessed related to Health Information Systems. RESULTS: The research in PubMed led to 124 articles, resulting in 17 papers taken into in-depth analyses. We identified three categories referring to different study design types. Additionally, articles showed that mental workload was influenced by using health information systems and vice versa. DISCUSSION: The review was limited to only one database but revealed that future research with sociotechnical focus including mental workload is necessary. CONCLUSION: In contrast to the high relevance only a few articles address mental workload in Health Information systems. The quality of the studies in terms of evidence and external validity appears to be largely in need of development and should be improved in ongoing research.

Relating Factors for Acceptance of Health Care Technology: Focus on Mental Workload.

Medical information systems and care robots are two typical examples of human computer interaction in health care. Although used in a stressful environment, effects on mental workload and acceptance are hardly evaluated. We conducted an experimental design including collaborative robotics and eye tracking in a nursing situation to test the practicability and plausibility of eye tracking as a measuring method for workload. Results showed that eye tracking is feasible if context factors are adjusted. Data reduction and classification of tasks are necessary.

Samply.MDR - A Metadata Repository and Its Application in Various Research Networks.

Collaboration in medical research is becoming common, especially for collecting relevant cases across institutional boundaries. If the data, which is usually very heterogeneously formalized and structured, can be integrated, such a collaboration can facilitate research. An absolute prerequisite for this is an extensive description about the formalization and exact meaning of every data element contained in a dataset. This information is commonly known as metadata. Various research networking projects tackle this challenge with the development of concepts and IT tools. The Samply Metadata Repository (Samply.MDR) is a solution for managing and publishing such metadata in a standardized and reusable way. In this article we present the structure and features of the Samply.MDR as well as its flexible usability by giving an overview about its application in various projects.

Diagnostic Classification of Cystoscopic Images Using Deep Convolutional Neural Networks.

PURPOSE: The recognition of cystoscopic findings remains challenging for young colleagues and depends on the examiner's skills. Computer-aided diagnosis tools using feature extraction and deep learning show promise as instruments to perform diagnostic classification. MATERIALS AND METHODS: Our study considered 479 patient cases that represented 44 urologic findings. Image color was linearly normalized and was equalized by applying contrast-limited adaptive histogram equalization. Because these findings can be viewed via cystoscopy from every possible angle and side, we ultimately generated images rotated in 10-degree grades and flipped them vertically or horizontally, which resulted in 18,681 images. After image preprocessing, we developed deep convolutional neural network (CNN) models (ResNet50, VGG-19, VGG-16, InceptionV3, and Xception) and evaluated these models using F1 scores. Furthermore, we proposed two CNN concepts: 90%-previous-layer filter size and harmonic-series filter size. A training set (60%), a validation set (10%), and a test set (30%) were randomly generated from the study data set. All models were trained on the training set, validated on the validation set, and evaluated on the test set. RESULTS: The Xception-based model achieved the highest F1 score (99.52%), followed by models that were based on ResNet50 (99.48%) and the harmonic-series concept (99.45%). All images with cancer lesions were correctly determined by these models. When the focus was on the images misclassified by the model with the best performance, 7.86% of images that showed bladder stones with indwelling catheter and 1.43% of images that showed bladder diverticulum were falsely classified. CONCLUSION: The results of this study show the potential of deep learning for the diagnostic classification of cystoscopic images. Future work will focus on integration of artificial intelligence-aided cystoscopy into clinical routines and possibly expansion to other clinical endoscopy applications.

From a Content Delivery Portal to a Knowledge Management System for Standardized Cancer Documentation.

Heterogeneous tumor documentation and its challenges of interpretation of medical terms lead to problems in analyses of data from clinical and epidemiological cancer registries. The objective of this project was to design, implement and improve a national content delivery portal for oncological terms. Data elements of existing handbooks and documentation sources were analyzed, combined and summarized by medical experts of different comprehensive cancer centers. Informatics experts created a generic data model based on an existing metadata repository. In order to establish a national knowledge management system for standardized cancer documentation, a prototypical tumor wiki was designed and implemented. Requirements engineering techniques were applied to optimize this platform. It is targeted to user groups such as documentation officers, physicians and patients. The linkage to other information sources like PubMed and MeSH was realized.

Expectancy, usage and acceptance by general practitioners and patients: exploratory results from a study in the German outpatient sector.

OBJECTIVE: The study's objective was to assess factors contributing to the use of smart devices by general practitioners (GPs) and patients in the health domain, while specifically addressing the situation in Germany, and to determine whether, and if so, how both groups differ in their perceptions of these technologies. METHODS: GPs and patients of resident practices in the Hannover region, Germany, were surveyed between April and June 2014. A total of 412 GPs in this region were invited by email to participate via an electronic survey, with 50 GPs actually doing so (response rate 12.1%). For surveying the patients, eight regional resident practices were visited by study personnel (once each). Every second patient arriving there (inclusion criteria: of age, fluent in German) was asked to take part (paper-based questionnaire). One hundred and seventy patients participated; 15 patients who did not give consent were excluded. RESULTS: The majority of the participating patients (68.2%, 116/170) and GPs (76%, 38/50) owned mobile devices. Of the patients, 49.9% (57/116) already made health-related use of mobile devices; 95% (36/38) of the participating GPs used them in a professional context. For patients, age (P < 0.001) and education (P < 0.001) were significant factors, but not gender (P > 0.99). For doctors, neither age (P = 0.73), professional experience (P > 0.99) nor gender (P = 0.19) influenced usage rates. For patients, the primary use case was obtaining health (service)-related information. For GPs, interprofessional communication and retrieving information were in the foreground. There was little app-related interaction between both groups. CONCLUSIONS: GPs and patients use smart mobile devices to serve their specific interests. However, the full potentials of mobile technologies for health purposes are not yet being taken advantage of. Doctors as well as other care providers and the patients should work together on exploring and realising the potential benefits of the technology.