Triptychon: Usability evaluation and implementation of a web-based application for patients' lab and vital parameters.

Background: A major challenge in healthcare is the interpretation of the constantly increasing amount of clinical data of interest to inpatients for diagnosis and therapy. It is vital to accurately structure and represent data from different sources to help clinicians make informed decisions. Objective: We evaluated the usability of our tool 'Triptychon' - a three-part visualisation dashboard of essential patients' medical data provided by a direct overview of their hospitalisation information, laboratory, and vital parameters over time. Methods: The study followed a cohort of 20 participants using the mixed-methods approach, including interviews and the usability questionnaires, Health Information Technology Usability Evaluation Scale (Health-ITUES), and User Experience Questionnaire (UEQ). The participant's interactions with the dashboard were also observed. A thematic analysis approach was applied to analyse qualitative data and the quantitative data's task completion time and success rates. Results: The usability evaluation of the visualisation dashboard revealed issues relating to the terminology used in the user interface and colour coding in its left and middle panels. The Health-ITUES score was 3.72 (standard deviation (SD) = 1.0), and the UEQ score was 1.6 (SD = 0.74). The study demonstrated improvements in intuitive dashboard use and overall satisfaction with using the dashboard daily. Conclusion: The Triptychon dashboard is a promising new tool for medical data presentation. We identified design and layout issues of the dashboard for improving its usability in routine clinical practice. According to users' feedback, the three panels on the dashboard provided a holistic view of a patient's hospital stay.

Visualization Techniques of Time-Oriented Data for the Comparison of Single Patients With Multiple Patients or Cohorts: Scoping Review.

BACKGROUND: Visual analysis and data delivery in the form of visualizations are of great importance in health care, as such forms of presentation can reduce errors and improve care and can also help provide new insights into long-term disease progression. Information visualization and visual analytics also address the complexity of long-term, time-oriented patient data by reducing inherent complexity and facilitating a focus on underlying and hidden patterns. OBJECTIVE: This review aims to provide an overview of visualization techniques for time-oriented data in health care, supporting the comparison of patients. We systematically collected literature and report on the visualization techniques supporting the comparison of time-based data sets of single patients with those of multiple patients or their cohorts and summarized the use of these techniques. METHODS: This scoping review used the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist. After all collected articles were screened by 16 reviewers according to the criteria, 6 reviewers extracted the set of variables under investigation. The characteristics of these variables were based on existing taxonomies or identified through open coding. RESULTS: Of the 249 screened articles, we identified 22 (8.8%) that fit all criteria and reviewed them in depth. We collected and synthesized findings from these articles for medical aspects such as medical context, medical objective, and medical data type, as well as for the core investigated aspects of visualization techniques, interaction techniques, and supported tasks. The extracted articles were published between 2003 and 2019 and were mostly situated in clinical research. These systems used a wide range of visualization techniques, most frequently showing changes over time. Timelines and temporal line charts occurred 8 times each, followed by histograms with 7 occurrences and scatterplots with 5 occurrences. We report on the findings quantitatively through visual summarization, as well as qualitatively. CONCLUSIONS: The articles under review in general mitigated complexity through visualization and supported diverse medical objectives. We identified 3 distinct patient entities: single patients, multiple patients, and cohorts. Cohorts were typically visualized in condensed form, either through prior data aggregation or through visual summarization, whereas visualization of individual patients often contained finer details. All the systems provided mechanisms for viewing and comparing patient data. However, explicitly comparing a single patient with multiple patients or a cohort was supported only by a few systems. These systems mainly use basic visualization techniques, with some using novel visualizations tailored to a specific task. Overall, we found the visual comparison of measurements between single and multiple patients or cohorts to be underdeveloped, and we argue for further research in a systematic review, as well as the usefulness of a design space.

Transfer learning for medical image classification: a literature review.

BACKGROUND: Transfer learning (TL) with convolutional neural networks aims to improve performances on a new task by leveraging the knowledge of similar tasks learned in advance. It has made a major contribution to medical image analysis as it overcomes the data scarcity problem as well as it saves time and hardware resources. However, transfer learning has been arbitrarily configured in the majority of studies. This review paper attempts to provide guidance for selecting a model and TL approaches for the medical image classification task. METHODS: 425 peer-reviewed articles were retrieved from two databases, PubMed and Web of Science, published in English, up until December 31, 2020. Articles were assessed by two independent reviewers, with the aid of a third reviewer in the case of discrepancies. We followed the PRISMA guidelines for the paper selection and 121 studies were regarded as eligible for the scope of this review. We investigated articles focused on selecting backbone models and TL approaches including feature extractor, feature extractor hybrid, fine-tuning and fine-tuning from scratch. RESULTS: The majority of studies (n = 57) empirically evaluated multiple models followed by deep models (n = 33) and shallow (n = 24) models. Inception, one of the deep models, was the most employed in literature (n = 26). With respect to the TL, the majority of studies (n = 46) empirically benchmarked multiple approaches to identify the optimal configuration. The rest of the studies applied only a single approach for which feature extractor (n = 38) and fine-tuning from scratch (n = 27) were the two most favored approaches. Only a few studies applied feature extractor hybrid (n = 7) and fine-tuning (n = 3) with pretrained models. CONCLUSION: The investigated studies demonstrated the efficacy of transfer learning despite the data scarcity. We encourage data scientists and practitioners to use deep models (e.g. ResNet or Inception) as feature extractors, which can save computational costs and time without degrading the predictive power.

SARS-CoV-2 Antibody Testing - How to Increase the Positive Predictive Value.

BACKGROUND: Despite increasing COVID-19 infection rates, low overall prevalence resulting in a poor positive predictive value (PPV) of serological tests requires strategies to increase specificity. We therefore investigated a dual diagnostic strategy and evaluated the correlation between the severity of a SARS-CoV-2 infection and the detectable immune-response. METHODS: Participants were systematically categorized into positive and control cohorts and a probability score of COVID-19 was calculated based on clinical symptoms. Six hundred eighty-two serum samples were analyzed using a highly specific high-throughput system. Combining the serological test result and probability score was performed as a dual diagnostic strategy. RESULTS: Specificity of 99.61% and sensitivity of 86.0% were the basis of our approach. A dual diagnostic strategy led to increased pre-test probability and thus to a test specificity of 100%. In a flu-like symptomatic population, we estimated a COVID-prevalence of 4.79%. Moreover, we detected significantly higher antibody values in patients with fever than without fever. CONCLUSIONS: Based on sensitivity and specificity results of our study being in line with previous findings, we demonstrated a dual assessment strategy including a symptom-based probability score and serological testing to increase the PPV. Moreover, the presence of fever seems to trigger a stronger immune-response.

[Evaluation of a SARS-CoV-2 test strategy in a southwestern university hospital at the beginning of the COVID-19 pandemic]. / Evaluation einer SARS-CoV-2-Teststrategie zu Beginn der COVID-19-Pandemie in einem südwestdeutschen Universitätsklinikum.

BACKGROUND: At the beginning of the COVID-19 pandemic, the German Robert Koch Institute (RKI) published several guidelines addressing the medical health services helping to detect SARS CoV­2. Needing an available and specific test strategy regarding SARS-CoV­2, our own test strategy strictly followed these testing criteria. MATERIALS AND METHODS: Using a retrospective analysis, we verified if such a test strategy was an effective tool in the context of infection prevention control and as reliable SARS-CoV­2 detection. Therefore, we analysed our own test results of suspected SARS-CoV­2 cases between 26 February and 6 April 2020. Additionally, we used a geovisualisation tool to visualise test frequencies and positive test results within different districts of Mannheim based on people's addresses. RESULTS: There were on average 7% positive test results of SARS-CoV­2 within a population with typical symptoms of COVID-19 (n = 2808). There was no positive test result within an asymptomatic population (n = 448). However, one positive test result turned out to be a nosocomial infection. Finally, geovisualisation highlighted a shift of test frequencies and local positive rates for SARS-CoV­2 from one district of Mannheim to another. DISCUSSION: In conclusion, our test strategy strictly based on testing criteria suggested by the Robert Koch Institute resulted in a steady rate of positive tests and allowed us to increase test capacity without causing numbers of nosocomial infections of COVID-19. Geovisualisation tools can offer support in analysing an ongoing spread of transmissible diseases. In the future, they could be used as helpful tools for infection prevention control, for example in the context of vaccination programs.

Tracking stem cell differentiation in the setting of automated optogenetic stimulation.

Membrane depolarization has been shown to play an important role in the neural differentiation of stem cells and in the survival and function of mature neurons. Here, we introduce a microbial opsin into ESCs and develop optogenetic technology for stem cell engineering applications, with an automated system for noninvasive modulation of ESC differentiation employing fast optogenetic control of ion flux. Mouse ESCs were stably transduced with channelrhodopsin-2 (ChR2)-yellow fluorescent protein and purified by fluorescence activated cell sorting (FACS). Illumination of resulting ChR2-ESCs with pulses of blue light triggered inward currents. These labeled ESCs retained the capability to differentiate into functional mature neurons, assessed by the presence of voltage-gated sodium currents, action potentials, fast excitatory synaptic transmission, and expression of mature neuronal proteins and neuronal morphology. We designed and tested an apparatus for optically stimulating ChR2-ESCs during chronic neuronal differentiation, with high-speed optical switching on a custom robotic stage with environmental chamber for automated stimulation and imaging over days, with tracking for increased expression of neural and neuronal markers. These data point to potential uses of ChR2 technology for chronic and temporally precise noninvasive optical control of ESCs both in vitro and in vivo, ranging from noninvasive control of stem cell differentiation to causal assessment of the specific contribution of transplanted cells to tissue and network function.