The Impact of Information Relevancy and Interactivity on Intensivists' Trust in a Machine Learning-Based Bacteremia Prediction System: Simulation Study.

Background: The exponential growth in computing power and the increasing digitization of information have substantially advanced the machine learning (ML) research field. However, ML algorithms are often considered "black boxes," and this fosters distrust. In medical domains, in which mistakes can result in fatal outcomes, practitioners may be especially reluctant to trust ML algorithms. Objective: The aim of this study is to explore the effect of user-interface design features on intensivists' trust in an ML-based clinical decision support system. Methods: A total of 47 physicians from critical care specialties were presented with 3 patient cases of bacteremia in the setting of an ML-based simulation system. Three conditions of the simulation were tested according to combinations of information relevancy and interactivity. Participants' trust in the system was assessed by their agreement with the system's prediction and a postexperiment questionnaire. Linear regression models were applied to measure the effects. Results: Participants' agreement with the system's prediction did not differ according to the experimental conditions. However, in the postexperiment questionnaire, higher information relevancy ratings and interactivity ratings were associated with higher perceived trust in the system (P<.001 for both). The explicit visual presentation of the features of the ML algorithm on the user interface resulted in lower trust among the participants (P=.05). Conclusions: Information relevancy and interactivity features should be considered in the design of the user interface of ML-based clinical decision support systems to enhance intensivists' trust. This study sheds light on the connection between information relevancy, interactivity, and trust in human-ML interaction, specifically in the intensive care unit environment.

Servitization level, digital transformation, and enterprise performance of sporting goods manufacturing enterprises in China.

To identify the effect and mechanism of servitization level and digital transformation on the performance of listed sporting goods manufacturing enterprises in China, we construct an index to measure the degree of digital transformation using data from 31 sporting goods manufacturing firms listed on Shanghai and Shenzhen A-shares and the New Over-the-Counter Market in China. The study uses the proportion of service business income in enterprise operating income to quantify servitization level, by analyzing the semantic expression of national digital economy policy and collecting digital category keywords from enterprise annual reports using crawler technology. Thereafter, we analyze the impact of servitization extent and digital transformation on company outcomes as well as if digital evolution acts as a moderating factor between servitization and company outcomes. The findings indicate that the extent of servitization reduces the outcomes of publicly traded sporting commodities production companies, showing a servitization paradox occurrence. Digital transformation degree has a positive U-shaped impact on enterprise performance and a weak positive moderating effect on servitization level and enterprise performance.

Advanced Monitoring of Manufacturing Process through Video Analytics.

The digitization of production systems has revolutionized industrial monitoring. Analyzing real-time bottom-up data enables the dynamic monitoring of industrial processes. Data are collected in various types, like video frames and time signals. This article focuses on leveraging images from a vision system to monitor the manufacturing process on a computer numerical control (CNC) lathe machine. We propose a method for designing and integrating these video modules on the edge of a production line. This approach detects the presence of raw parts, measures process parameters, assesses tool status, and checks roughness in real time using image processing techniques. The efficiency is evaluated by checking the deployment, the accuracy, the responsiveness, and the limitations. Finally, a perspective is offered to use the metadata off the edge in a more complex artificial-intelligence (AI) method for predictive maintenance.

SurvdigitizeR: an algorithm for automated survival curve digitization.

BACKGROUND: Decision analytic models and meta-analyses often rely on survival probabilities that are digitized from published Kaplan-Meier (KM) curves. However, manually extracting these probabilities from KM curves is time-consuming, expensive, and error-prone. We developed an efficient and accurate algorithm that automates extraction of survival probabilities from KM curves. METHODS: The automated digitization algorithm processes images from a JPG or PNG format, converts them in their hue, saturation, and lightness scale and uses optical character recognition to detect axis location and labels. It also uses a k-medoids clustering algorithm to separate multiple overlapping curves on the same figure. To validate performance, we generated survival plots form random time-to-event data from a sample size of 25, 50, 150, and 250, 1000 individuals split into 1,2, or 3 treatment arms. We assumed an exponential distribution and applied random censoring. We compared automated digitization and manual digitization performed by well-trained researchers. We calculated the root mean squared error (RMSE) at 100-time points for both methods. The algorithm's performance was also evaluated by Bland-Altman analysis for the agreement between automated and manual digitization on a real-world set of published KM curves. RESULTS: The automated digitizer accurately identified survival probabilities over time in the simulated KM curves. The average RMSE for automated digitization was 0.012, while manual digitization had an average RMSE of 0.014. Its performance was negatively correlated with the number of curves in a figure and the presence of censoring markers. In real-world scenarios, automated digitization and manual digitization showed very close agreement. CONCLUSIONS: The algorithm streamlines the digitization process and requires minimal user input. It effectively digitized KM curves in simulated and real-world scenarios, demonstrating accuracy comparable to conventional manual digitization. The algorithm has been developed as an open-source R package and as a Shiny application and is available on GitHub: https://github.com/Pechli-Lab/SurvdigitizeR and https://pechlilab.shinyapps.io/SurvdigitizeR/ .

[The impact of digital technologies on labor organization in health care scope].

Enhancement of the health care system in Russia continues to be one of key directions of National development. In conditions of deficiency of personnel, systemic changes are needed to transit to qualitatively new level. The application of digital platforms permits to resolve a number of issues related to accessibility and quality of medical services. The paper characterizes current state of health care digitization and level of competitiveness of medical institutions. On the basis of analysis of corporate culture of medical institutions, conclusions are made that it contributes to successful implementation of unified medical information system and development of best corporate standards and traditions.

[DEVELOPMENT OF THE SECTOR of R&D development in Russian HIGHER medical education in digitization conditions].

The article considers results of study of the R&D sector directions development in system of Russian medical higher education in the context of digitization. The analysis made it possible to substantiate timeliness and importance of solving problems of improving medical services quality against the background of necessity for medical workers to systematically master breakthrough digital technologies in current medical practice. This conditions importance to ensure efficiency of R&D in Russian higher medical education in the context of digitization. The main models of organization of activities in the field of R&D are characterized, taking into account sources of financing and staffing. Based on on-line surveys of students in Google docs about their attitude to their further participation in R&D during education and after graduation, weak motivation to be engaged in R&D during their university education was revealed that is justified by non-obviousness of prospects of financial remuneration. Along with this, study established high degree of interest among students to mastering breakthrough digital technologies in order to further apply them in medical practice together with the latest medical technologies.

3D dataset of a twisted bending-active beam element digitized using structure-from-motion photogrammetry.

The current work presents the generation of a comprehensive spatial dataset of a lightweight beam element composed of four twisted plywood strips, achieved through the application of Structure-from-Motion (SfM) - Multi-view Stereo (MVS) photogrammetry techniques in controlled laboratory conditions. The data collection process was meticulously conducted to ensure accuracy and precision, employing scale bars of varying lengths. The captured images were then processed using photogrammetric software, leading to the creation of point clouds, meshes, and texture files. These data files represent the 3D model of the beam at different mesh sizes (raw, high-poly, medium-poly, and low-poly), adding a high level of detail to the 3D visualization. The dataset holds significant reuse potential and offers essential resources for further studies in numerical modeling, simulations of complex structures, and training machine learning algorithms. This data can also serve as validation sets for emerging photogrammetry methods and form-finding techniques, especially ones involving large deformations and geometric nonlinearities, particularly within the structural engineering field.

WiPFIM: A digital platform for interlinking biocollections of wild plants, fruits, associated insects, and their molecular barcodes.

The current knowledge on insects feeding on fruits is limited, and some of the scarce existing data on the fruit-associated insects are secluded within the host institutions. Consequently, their value is not fully realized. Moreover, in countries like Kenya, the integration of biocollections data within a digital framework has not been fully exploited. To address these gaps, this article presents a description of the development of a web-based platform for data sharing and integrating biodiversity historical data of wild plants, fruits, associated insects, and their molecular barcodes (WiPFIM) while leveraging data science technologies. The barcodes corresponding to the biocollections data were retrieved from BOLD database. The platform is an online resource about fruit-insect interactions that can be of interest to a worldwide community of users and can be useful in building innovative tools. The platform is accessible online at https://test-dmmg.icipe.org/wpfhi.

Digitizing tools for post introduction evaluation of rotavirus vaccine introduction in India.

Background and aims: The Rotavirus vaccine (RVV) introduction is a landmark event in the history of Indian public health as for the first time a novel, low-cost indigenous vaccine was introduced in a short timeline between 2016 and 2019. As per WHO mandate, post-introduction evaluation (PIE) be conducted within 6 to 12 months of vaccine introduction to provide an understanding of the operational aspects of the program. For RVV PIE, an innovative approach to developing and deploying a digitized tool was employed. The present study aims to document the processes followed for digitizing the data collection and analysis tools. Methods: The development of the RVV-PIE digital tool was undertaken in two phases. In the first phase, conceptualization and iteration of the modified WHO PIE tool were undertaken. Questions were organized sequentially to ensure natural progression in responses. The finalized questionnaire was converted to a digital version and extensive dummy data was entered to improve automated qualitative data analysis. Phase 2 involved updating the draft tool and incorporating changes to provide a field-tested version for deployment. Results: The digital version of the tool was successfully developed. The GPS functionality of the tool allowed live tracking of data collection making the process more accountable. The tool was prepopulated with reference materials and data points for easy reference and retrieval by the evaluators. The digitization of the tool also allowed easy visualization of data through maps, charts, and graphs on a real-time user-friendly dashboard. Conclusions: The digitization of the PIE tool for RVV in India has been a great learning experience where the dire situation of an ongoing pandemic catapulted us towards a more efficient and comprehensive process innovation. The RVV PIE tool could serve as a customizable digital PIE tool for other health programs heralding an era of a more effective and proficient process of PIE.

Accurate digitization of EEG electrode locations by electromagnetic tracking system: The proposed head rotation method and comparison against optical system.

Electroencephalogram (EEG) electrode digitization is crucial for accurate EEG source estimation, and several commercial systems are available for this purpose. The present study aimed to evaluate the digitizing accuracy of electromagnetic and optical systems. Additionally, we introduced a novel rotation method for the electromagnetic system and compared its accuracy with the conventional method of electromagnetic and optical systems. In the conventional method, the operator moves around a stationary participant to digitize, while the participant does not move their head or body. In contrast, in our proposed rotation method with an electromagnetic system, the operator rotates the participant sitting on a swivel chair to digitize in a consistent position. We showed high localization accuracy in both the optical and electromagnetic systems, with an average localization error of less than 3.6 mm. Comparisons of the digitization methods revealed that the electromagnetic system demonstrates superior digitizing accuracy compared to the optical system. Notably, the proposed rotational method is the most accurate among the three methods, which can be attributed to the consistent positioning of EEG electrode digitization within the electromagnetic field. Considering the affordability of the electromagnetic system, our findings provide valuable insights for researchers aiming for precise EEG source estimation.•The study compares the accuracy of electromagnetic and optical systems for EEG electrode digitization, introducing a novel rotation method for improved consistency and precision.•The electromagnetic system, especially with the proposed rotation method, achieves superior digitizing accuracy over the optical system.•Highlighting the cost-effectiveness and precision of the electromagnetic system with the rotation method, this research offers significant insights for achieving precise EEG source estimation.

Memetics and the Parallel Architecture.

The evolution of human communication and culture is among the most significant-and challenging-questions we face in attempting to understand the evolution of our species. This article takes up two frameworks for theorizing about human communication and culture, namely, Jackendoff's Parallel Architecture of the human language faculty, and the cultural evolutionary framework of Memetics. The aim is to show that the two frameworks uniquely complement one another in some theoretically important ways. In particular, the Parallel Architecture's account of the lexicon significantly expands the range of linguistic phenomena that are plausibly covered by Memetics (e.g., from words to constructions and pure rules of syntax). At the same time, taking a "meme's-eye-view" of the lexicon retools the Parallel Architecture's treatment of the origins and subsequent cultural evolution of language.

Comparison of failure modes and effects analyses and time for brachytherapy ring and tandem applicator digitization between manual and solid applicator source placement methods.

PURPOSE: Ring and tandem (R&T) applicator digitization is currently performed at our institution by manually defining the extent of the applicators. Digitization can also be achieved using solid applicators: predefined, 3D models with geometric constraints. This study compares R&T digitization using manual and solid applicator methods through Failure Modes and Effects Analyses (FMEAs) and comparative time studies. We aim to assess the suitability of solid applicator method implementation for R&T cases METHODS: Six qualified medical physicists (QMPs) and two medical physics residents scored potential modes of failure of manual digitization in an FMEA as recommended by TG-100. Occurrence, severity, and detectability (OSD) values were averaged across respondents and then multiplied to form combined Risk Priority Numbers (RPNs) for analysis. Participants were trained to perform treatment planning using a developed solid applicator protocol and asked to score a second FMEA on the distinct process steps from the manual method. For both methods, participant digitization was timed. FMEA and time data were analyzed across methods and participant samples RESULTS: QMPs rated the RPNs of the current, manual method of digitization statistically lower than residents did. When comparing the unique FMEA steps between the two digitization methods, QMP respondents found no significant difference in RPN means. Residents, however, rated the solid applicator method as higher risk. Further, after the solid applicator method was performed twice by participants, the time to digitize plans was not significantly different from manual digitization CONCLUSIONS: This study indicates the non-inferiority of the solid applicator method to manual digitization in terms of risk, according to QMPs, and time, across all participants. Differences were found in FMEA evaluation and solid applicator technique adoption based on years of brachytherapy experience. Further practice with the solid applicator protocol is recommended because familiarity is expected to lower FMEA occurrence ratings and further reduce digitization times.

Mega-Barcoding Projects: Delivering National DNA Barcoding Initiatives for Plants.

Plant DNA barcoding has a multitude of applications ranging from species detection and biomonitoring to investigating ecological networks and checking food quality. The ability to accurately identify species, using DNA barcoding, depends on the quality and comprehensiveness of the reference library that is used. This chapter describes how to create plant reference libraries using the rbcL, matK, and ITS2 DNA barcode regions. It covers the creation of species lists, the collection of specimens from the field and herbarium, DNA extraction, PCR amplification, and DNA sequencing. This methodology gives special attention to using samples from herbaria, as they represent important collections of easily accessible, taxonomically verified plant material.

Fusion of Multimodal Imaging and 3D Digitization Using Photogrammetry.

Multimodal sensors capture and integrate diverse characteristics of a scene to maximize information gain. In optics, this may involve capturing intensity in specific spectra or polarization states to determine factors such as material properties or an individual's health conditions. Combining multimodal camera data with shape data from 3D sensors is a challenging issue. Multimodal cameras, e.g., hyperspectral cameras, or cameras outside the visible light spectrum, e.g., thermal cameras, lack strongly in terms of resolution and image quality compared with state-of-the-art photo cameras. In this article, a new method is demonstrated to superimpose multimodal image data onto a 3D model created by multi-view photogrammetry. While a high-resolution photo camera captures a set of images from varying view angles to reconstruct a detailed 3D model of the scene, low-resolution multimodal camera(s) simultaneously record the scene. All cameras are pre-calibrated and rigidly mounted on a rig, i.e., their imaging properties and relative positions are known. The method was realized in a laboratory setup consisting of a professional photo camera, a thermal camera, and a 12-channel multispectral camera. In our experiments, an accuracy better than one pixel was achieved for the data fusion using multimodal superimposition. Finally, application examples of multimodal 3D digitization are demonstrated, and further steps to system realization are discussed.

The impact of digital technology use on nurses' professional identity and relations of power: a literature review.

AIM: This study seeks to review how the use of digital technologies in clinical nursing affects nurses' professional identity and the relations of power within clinical environments. DESIGN: Literature review. DATA SOURCES: PubMed and CINAHL databases were searched in April 2023. METHODS: We screened 874 studies in English and German, of which 15 were included in our final synthesis reflecting the scientific discourse from 1992 until 2023. RESULTS: Our review revealed relevant effects of digital technologies on nurses' professional identity and power relations. Few studies cover outcomes relating to identity, such as moral agency or nurses' autonomy. Most studies describe negative impacts of technology on professional identity, for example, creating a barrier between nurses and patients leading to decreased empathetic interaction. Regarding power relations, technologically skilled nurses can yield power over colleagues and patients, while depending on technology. The investigation of these effects is underrepresented. CONCLUSION: Our review presents insights into the relation between technology and nurses' professional identity and prevalent power relations. For future studies, dedicated and critical investigations of digital technologies' impact on the formation of professional identity in nursing are required. IMPLICATIONS FOR THE PROFESSION: Nurses' professional identity may be altered by digital technologies used in clinical care. Nurses, who are aware of the potential effects of digitized work environments, can reflect on the relationship of technology and the nursing profession. IMPACT: The use of digital technology might lead to a decrease in nurses' moral agency and competence to shape patient-centred care. Digital technologies seem to become an essential measure for nurses to wield power over patients and colleagues, whilst being a control mechanism. Our work encourages nurses to actively shape digital care. REPORTING METHOD: We adhere to the JBI Manual for Evidence Synthesis where applicable. EQUATOR reporting guidelines were not applicable for this type of review. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.

Evaluating free segmentation tools for CBCT-derived models: Cost-effective solutions.

OBJECTIVES: This study evaluated the segmentation accuracy and reliability of free software packages and compared them with commercial alternatives. MATERIAL AND METHODS: A total of 36 stone models were scanned using a desktop scanner and then imaged by cone beam computed tomography (CBCT). The CBCT volumes were segmented using 2 free software packages (3D Slicer and Blue Sky Plan) and 2 commercial software packages (Mimics and OnDemand3D). Stereolithography (STL) files generated by the desktop scanner were used as the control group (reference models). The accuracy of segmentation was evaluated by (1) comparing 6 linear measurements taken from each STL model generated by the 4 software packages with that obtained by the scanner, and (2) deviation analysis of each STL model generated by the 4 software packages with that obtained by the scanner. Absolute error and percentage error, repeated measures anova and Friedman test followed by post hoc analysis, intraclass correlation coefficient (ICC), and Pearson's r were used to evaluate the accuracy of the tested software packages. RESULTS: There was no statistically significant difference in all intra-arch measurements obtained using the four software packages. Measurements obtained using the free software packages and the scanner showed excellent positive correlation, ranging from 0.825 to 0.988, confirming equivalence with commercial software packages. CONCLUSION: Within the settings of the current study, accurate and time-saving segmentations with high positive correlation could be performed using the tested free segmentation software packages (3D Slicer and Blue Sky Plan). Nevertheless, further evaluation is necessary to gage their accuracy using different CBCT modalities.

The impact of digital transformation on resource mismatch of Chinese listed companies.

Based on a microeconomic entity perspective, this paper empirically examines the effect of enterprise digitalization on resource mismatch. We found that, firstly, an increasing in enterprise digitalization reduces resource mismatch. Moreover, the results remain robust after considering endogeneity and changing the variable measurements. Secondly, enterprise digitalization can significantly reduce resource mismatch of private and large-scale enterprises and significantly contribute to reducing enterprise resource mismatch in low marketability regions and eastern regions. Thirdly, enterprise digital transformation can reduce resource mismatch by decreasing operating costs and financing constraints; Executive incentives can help reduce resource mismatch in the digital process of enterprises. Fourthly, the increase in enterprise digitalization contributes to an enhance in corporate social responsibility, and enterprise resource mismatch plays a mediating role in the relationship of enterprise digitalization development improving corporate social responsibility. Finally, in response to the findings of the study, the paper suggests countermeasures for regional and corporate countermeasures regarding digital development.

Cardiac Arrhythmia Classification Using Advanced Deep Learning Techniques on Digitized ECG Datasets.

ECG classification or heartbeat classification is an extremely valuable tool in cardiology. Deep learning-based techniques for the analysis of ECG signals assist human experts in the timely diagnosis of cardiac diseases and help save precious lives. This research aims at digitizing a dataset of images of ECG records into time series signals and then applying deep learning (DL) techniques on the digitized dataset. State-of-the-art DL techniques are proposed for the classification of the ECG signals into different cardiac classes. Multiple DL models, including a convolutional neural network (CNN), a long short-term memory (LSTM) network, and a self-supervised learning (SSL)-based model using autoencoders are explored and compared in this study. The models are trained on the dataset generated from ECG plots of patients from various healthcare institutes in Pakistan. First, the ECG images are digitized, segmenting the lead II heartbeats, and then the digitized signals are passed to the proposed deep learning models for classification. Among the different DL models used in this study, the proposed CNN model achieves the highest accuracy of ∼92%. The proposed model is highly accurate and provides fast inference for real-time and direct monitoring of ECG signals that are captured from the electrodes (sensors) placed on different parts of the body. Using the digitized form of ECG signals instead of images for the classification of cardiac arrhythmia allows cardiologists to utilize DL models directly on ECG signals from an ECG machine for the real-time and accurate monitoring of ECGs.

Comparison study of intraoperative surface acquisition methods on registration accuracy for soft-tissue surgical navigation.

Purpose: To study the difference between rigid registration and nonrigid registration using two forms of digitization (contact and noncontact) in human in vivo liver surgery. Approach: A Conoprobe device attachment and sterilization process was developed to enable prospective noncontact intraoperative acquisition of organ surface data in the operating room (OR). The noncontact Conoprobe digitization method was compared against stylus-based acquisition in the context of image-to-physical registration for image-guided surgical navigation. Data from n=10 patients undergoing liver resection were analyzed under an Institutional Review Board-approved study at Memorial Sloan Kettering Cancer Center. Organ surface coverage of each surface acquisition method was compared. Registration accuracies resulting from the acquisition techniques were compared for (1) rigid registration method (RRM), (2) model-based nonrigid registration method (NRM) using surface data only, and (3) NRM with one subsurface feature (vena cava) from tracked intraoperative ultrasound (NRM-VC). Novel vessel centerline and tumor targets were segmented and compared to their registered preoperative counterparts for accuracy validation. Results: Surface data coverage collected by stylus and Conoprobe were 24.6%±6.4% and 19.6%±5.0%, respectively. The average difference between stylus data and Conoprobe data using NRM was -1.05 mm and using NRM-VC was -1.42 mm, indicating the registrations to Conoprobe data performed worse than to stylus data with both NRM approaches. However, using the stylus and Conoprobe acquisition methods led to significant improvement of NRM-VC over RRM by average differences of 4.48 and 3.66 mm, respectively. Conclusion: The first use of a sterile-field amenable Conoprobe surface acquisition strategy in the OR is reported for open liver surgery. Under clinical conditions, the nonrigid registration significantly outperformed standard-of-care rigid registration, and acquisition by contact-based stylus and noncontact-based Conoprobe produced similar registration results. The accuracy benefits of noncontact surface acquisition with a Conoprobe are likely obscured by inferior data coverage and intrinsic noise within acquisition systems.

Performance evaluation of machine-assisted interpretation of Gram stains from positive blood cultures.

Manual microscopy of Gram stains from positive blood cultures (PBCs) is crucial for diagnosing bloodstream infections but remains labor intensive, time consuming, and subjective. This study aimed to evaluate a scan and analysis system that combines fully automated digital microscopy with deep convolutional neural networks (CNNs) to assist the interpretation of Gram stains from PBCs for routine laboratory use. The CNN was trained to classify images of Gram stains based on staining and morphology into seven different classes: background/false-positive, Gram-positive cocci in clusters (GPCCL), Gram-positive cocci in pairs (GPCP), Gram-positive cocci in chains (GPCC), rod-shaped bacilli (RSB), yeasts, and polymicrobial specimens. A total of 1,555 Gram-stained slides of PBCs were scanned, pre-classified, and reviewed by medical professionals. The results of assisted Gram stain interpretation were compared to those of manual microscopy and cultural species identification by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). The comparison of assisted Gram stain interpretation and manual microscopy yielded positive/negative percent agreement values of 95.8%/98.0% (GPCCL), 87.6%/99.3% (GPCP/GPCC), 97.4%/97.8% (RSB), 83.3%/99.3% (yeasts), and 87.0%/98.5% (negative/false positive). The assisted Gram stain interpretation, when compared to MALDI-TOF MS species identification, also yielded similar results. During the analytical performance study, assisted interpretation showed excellent reproducibility and repeatability. Any microorganism in PBCs should be detectable at the determined limit of detection of 105 CFU/mL. Although the CNN-based interpretation of Gram stains from PBCs is not yet ready for clinical implementation, it has potential for future integration and advancement.