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Background: Electronic health record (EHR) nursing summaries have the potential to support nurses in locating and synthesizing patient information. However, nurses’ role-specific perspectives are often excluded in the design of the EHR system. The purpose of this study was to describe nurses’ current use of nursing summaries and vital sign information within them and glean their ideas for design improvements. Methods: This research followed Nielsen and Norman's 4-phased design cycle and focused on the discovery and exploration phases to identify the context of nurses' use of the nursing summary (discovery) and to create prototype design layout using physical materials (exploration). Results: Ten clinical inpatient nurses participated in interviews and co-design activities. Nurses hardly use the nursing summary to overview a comprehensive patient's health status. The current design of a nursing summary lacks comprehensive patient information and contains much irrelevant data. Nurses prefer vital signs to be prominently displayed on the summary screen for easy visibility. Conclusion: Involving nurses in the design process can lead to a nursing summary that better meets their needs.

Objective: A Virtual Patient Tour (VPT) was developed to inform cardiac surgery patients about their hospitalization from the admission to their postoperative stay on the ward. The objective of our study was to assess the feasibility and acceptability of this VPT following the framework of the Virtual Reality Clinical Outcomes Research Experts Committee., Methods: In this single-centre cross-sectional study, adult patients admitted to the hospital for elective cardiac surgery were included. Acceptability, usability, and tolerability were measured by the validated questionnaires Unified Theory of Acceptance and Use of Technology (acceptability), System Usability Scale (usability), and Virtual Reality Sickness Questionnaire (tolerability). Descriptive statistics were used for the analysis., Results: Twenty-eight participants used the VPT. Results showed high acceptability (mean 16.7 ± 1.5), acceptable usability (mean 86.7 ± 9.3), and high tolerability (sickness score, median 7.1 % [0-17.1 %])., Conclusion: The use of the VPT is a feasible and promising technique. The next step is to optimize the content and technique of the VPT based on the suggestions of the participants., Practice Implications: We recommend incorporating the VPT into preoperative patient education in addition to the routine information in cardiac surgery patients., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Objectives: To (1) construct a virtual patient (VP) using facial scan, intraoral scan, and low-dose computed tomography scab based on an Artificial intelligence (AI)-approach, (2) quantitatively compare it with AI-refined and semi-automatic registration, and (3) qualitatively evaluate user satisfaction when using virtual patient as a communication tool in clinical practice., Materials and Methods: A dataset of 20 facial scans, intraoral scans, and low-dose computed tomography scans was imported into the Virtual Patient Creator platform to create an automated virtual patient. The accuracy of the virtual patients created using different approaches was further analyzed in the Mimics software. The accuracy (% of corrections required), consistency, and time efficiency of the AI-driven virtual patient registration were then compared with the AI-refined and semi-automatic registration (clinical reference). User satisfaction was assessed through a survey of 35 dentists and 25 laypersons who rated the virtual patient's realism and usefulness for treatment planning and communication on a 5-point scale., Results: The accuracy for AI-driven, AI-refined, and semi-automatic registration virtual patient was 85 %, 85 %, and 100 % for the upper and middle thirds of the face, and 30 %, 30 %, and 35 % for the lower third. Registration consistency was 1, 1 and 0.99, and the average time was 26.5, 30.8, and 385 s, respectively (18-fold time reduction with AI). The inferior facial third exhibited the highest registration mismatch between facial scan and computed tomography. User satisfaction with the virtual patient was consistently high among both dentists and laypersons, with most responses indicating very high satisfaction regarding realism and usefulness as a communication tool., Conclusion: The AI-driven registration can provide clinically accurate, fast, and consistent virtual patient creation using facial scans, intraoral scans, and low-dose computed tomography scans, enabling interpersonal communication., Clinical Significance: Using AI for automated segmentation and registration of maxillofacial structures leads to clinically efficient and accurate VP creation, opening the doors for its widespread use in diagnosis, treatment planning, and interprofessional and professional-patient communication., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Objective: We hypothesized that learning through multiple sensory modalities would improve knowledge recall and recognition in orthopedic surgery residents and medical students., Design: We developed a virtual study assistant, named Socratic Artificial Intelligence Learning (SAIL), based on a custom-built natural language processing algorithm. SAIL draws from practice questions approved by the American Board of Orthopaedic Surgery and quizzes users through a conversational, voice-enabled Web interface. We performed a randomized controlled study using a within-subjects, repeated measures design., Setting: Participants first took a pretest to assess their baseline knowledge. They then underwent 10 days of spaced repetition training with practice questions using 3 modalities: oral response, typed response, and multiple-choice. Recall and recognition of the practiced knowledge were assessed via a post-test administered on the first day, first week, and 2 months after the training period., Participants: Twenty-four volunteers, who were medical students and orthopedic surgery residents at multiple US medical institutions., Results: The oral, typed, and multiple-choice modalities produced similar recall and recognition rates. Although participants preferred using the traditional multiple-choice modality to study for standardized examinations, many were interested in supplementing their study routine with SAIL and believe that SAIL may improve their performance on written and oral examinations., Conclusions: SAIL is not inferior to the multiple-choice modality for learning orthopedic core knowledge. These results indicate that SAIL can be used to supplement traditional study methods. COMPETENCIES: medical knowledge; practice-based learning and improvement., (Copyright © 2024 Association of Program Directors in Surgery. Published by Elsevier Inc. All rights reserved.)

Objectives: This in vitro study was formulated to clarify how mandibular partial edentulous distal extension involving three missing teeth affects the virtual occlusal record (VOR) accuracy, in terms of both trueness and precision, when using two different intraoral scanners (IOSs) - the Primescan (PS) and Trios 4 (TR) scanners., Methods: A typodont model missing the left mandibular second premolar, first molar, and second molar as well as the right mandibular first molar was mounted on a semi-adjustable articulator. Four implants were placed at the sites of the missing mandibular teeth. Six pairs of stainless steel markers (diameter: 0.5 mm) were affixed to the maxillary and mandibular casts in the buccal gingiva adjacent to the implants and on the facial surfaces of teeth as reference positions for measurements. The model was digitized with an inEox X5 laboratory scanner to create a reference dataset. Intraoral scans were performed with the PS and TR scanners, with each scan duplicated 10 times to generate 20 paired IOS files. Automatic VOR generation followed the bilateral buccal scan protocol, divided into PS and TR groups (n = 10). Six subgroups of linear distances between interarch markers were assessed with Geomagic Control software, comparing deviations from the reference scan. Data normality was confirmed with the Shapiro-Wilk test. Trueness was evaluated with two-way ANOVAs and pairwise comparisons performed with Tukey's test, whereas precision was assessed with the Levene test (α=0.05)., Results: The mean linear deviation (Δd) and standard deviation (SD) of VOR were both significantly affected by marker position (P < 0.001), and a significant position × scanner interaction was detected (P < 0.001). Negative mean deviations were observed for the distally extended edentulous areas in both groups. PS scans exhibited trueness that was significantly better than that for TR scans in the D16-46, D13-43, D23-33, and D27-37 subgroups (P < 0.05), whereas there were no significant differences in the D25-35 or D26-36 subgroups. PS scanning was associated with significantly better precision than TR scanning (P < 0.001), and worse precision was observed at D27-37 for both tested IOSs., Conclusions: Mandibular partial edentulous distal extension can significantly affect VOR accuracy. The type of IOS could also affect VOR accuracy depending on the area being scanned, with better overall performance observed for the Primescan device as compared to the Trios 4 scanner. Both of these IOSs tended to underestimate VOR occlusal dimensions in mandibular distally extended edentulous areas., Clinical Significance: Mandibular distal edentulous areas can contribute to occlusal dimensions that are underestimated in digital workflows, which may result in infraocclusal discrepancies that arise when performing restorations. IOSs and VOR scanning protocols should thus be carefully considered in order to minimize these risks., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Mass spectrometry (MS) has been one of the most widely used tools for bioanalytical analysis due to its high sensitivity, capability of quantitative analysis, and compatibility with biomolecules. Among various MS techniques, single cell mass spectrometry (SCMS) is an advanced approach to molecular analysis of cellular contents in individual cells. In tandem with the creation of novel experimental techniques, the development of new SCMS data analysis tools is equally important. As most published software packages are not specifically designed for pretreatment of SCMS data, including peak alignment and background removal, their applicability on processing SCMS data is generally limited. Hereby we introduce a Python platform, MassLite, specifically designed for rapid SCMS metabolomics data pretreatment. This platform is made user-friendly with graphical user interface (GUI) and exports data in the forms of each individual cell for further analysis. A core function of this tool is to use a novel peak alignment method that avoids the intrinsic drawbacks of traditional binning method, allowing for more effective handling of MS data obtained from high resolution mass spectrometers. Other functions, such as void scan filtering, dynamic grouping, and advanced background removal, are also implemented in this tool to improve pretreatment efficiency., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Metabolomics using mass spectrometry-only (MS) analysis either by continuous or intermittent direct infusion (DIMS) and ambient ionization techniques (AMS) has grown in popularity due to their rapid, high-throughput nature and the advantage of performing fast analysis with minimal or no sample pretreatments. But currently, end-users without programming knowledge do not find applications with Graphical User Interface (GUI) specialized in processing DIMS or AMS data. Specifically, there is a lack of standardized workflow for processing data from limited sample sizes and scans from different total ion chronograms (TIC).To address this gap, we present rIDIMS, a browser-based application that offers a straightforward and fast workflow focusing on high-quality scan selection, grouping of isotopologues and adducts, data alignment, binning, and filtering. We also introduce a novel function for selecting TIC scans that is reproducible and statistically reliable, which is a feature particularly useful for studies with limited sample sizes. After processing in rIDIMS, the result is exported in an HTML report document that presents publication-quality figures, statistical data and tables, ready to be customized and exported. We demonstrate rIDIMS functionality in three cases: (i) Classification of coffee bean species through the chemical profile obtained with Mass Spec Pen; (ii) Public repository DIMS data from lipid profiling in monogenic insulin resistance syndromes, and (iii) Lipids for lung cancer classification. We show that our implementation facilitates the processing of AMS and DIMS data through an easy and intuitive interface, contributing to reproducible and reliable metabolomic investigations. Indeed, rIDIMS function asa user-friendly GUI based Shiny web application for intuitive use by end-users (available at https://github.com/BioinovarLab/rIDIMS)., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Objective: This study aimed to identify communication strategies that virtual humans (VHs) discussing clinical trials can use to foster positive relationships with older women to better deliver research education., Methods: A template thematic analysis and matrix analysis of 10 interviews and 3 focus groups with 37 older women identifying as White non-Hispanic, Black, or Hispanic/Latinx were conducted., Results: VHs can foster positive relationships with older women during clinical trial education by communicating social support. At the same time, VHs should convey credibility. Factors shaping experiences of the VHs' support and credibility include race and ethnicity, comfort with research and technology, and current health status., Conclusion: Deploying communication strategies that foster positive relationships between VHs and older women are crucial for optimizing the use of VHs during clinical trial education for populations historically excluded from research., Practice Implications: This study provides a cohesive framework guiding the development of VH clinical trial educators for older women to meet their communication needs. The framework may extend to other populations historically excluded from research and real human clinical trial educators., Funding: This work was supported by the National Institute on Aging National Institutes of Health, Award R24AG074867 (PIs: Krieger and Anton)., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Intravoxel incoherent motion (IVIM) imaging is increasingly recognised as an important tool in clinical MRI, where tissue perfusion and diffusion information can aid disease diagnosis, monitoring of patient recovery, and treatment outcome assessment. Currently, the discovery of biomarkers based on IVIM imaging, similar to other medical imaging modalities, is dependent on long preclinical and clinical validation pathways to link observable markers derived from images with the underlying pathophysiological mechanisms. To speed up this process, virtual IVIM imaging is proposed. This approach provides an efficient virtual imaging tool to design, evaluate, and optimise novel approaches for IVIM imaging. In this work, virtual IVIM imaging is developed through a new finite element solver, SpinDoctor-IVIM, which extends SpinDoctor, a diffusion MRI simulation toolbox. SpinDoctor-IVIM simulates IVIM imaging signals by solving the generalised Bloch-Torrey partial differential equation. The input velocity to SpinDoctor-IVIM is computed using HemeLB, an established Lattice Boltzmann blood flow simulator. Contrary to previous approaches, SpinDoctor-IVIM accounts for volumetric microvasculature during blood flow simulations, incorporates diffusion phenomena in the intravascular space, and accounts for the permeability between the intravascular and extravascular spaces. The above-mentioned features of the proposed framework are illustrated with simulations on a realistic microvasculature model., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Objective: The study aimed to develop and validate a conversational agent (chatbot) designed to support Food and Nutrition Surveillance (FNS) practices in primary health care settings., Methods: This mixed-methods research was conducted in three stages. Initially, the study identified barriers and challenges in FNS practices through a literature review and feedback from 655 health professionals and FNS experts across Brazil. Following this, a participatory design approach was employed to develop and validate the chatbot's content. The final stage involved evaluating the chatbot's user experience with FNS experts., Results: The chatbot could accurately understand and respond to 60 different intents or keywords related to FNS. Themes such as training, guidance, and access emerged as crucial for guiding FNS initiatives and addressing implementation challenges, primarily related to human resources. The chatbot achieved a Global Content Validation Index of 0.88., Conclusion: The developed chatbot represents a significant advancement in supporting FNS practices within primary health care., Practice Implication: By providing an innovative, interactive, educational tool that is both accessible and reliable, this digital assistant has the potential to facilitate the operationalization of FNS practices, addressing the critical need for effective training and counseling in developing countries., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: ESB, MKR, VRK, ACBM and GVSL declare that they have no known competing interests that could have appeared to influence the work reported in this paper. Mr. Bellei has worked for Novartis, AbbVie, and Johnson & Johnson; however, these companies did not provide endorsement or participation., (Copyright © 2024 Elsevier B.V. All rights reserved.)

The DockThor-VS platform (https://dockthor.lncc.br/v2/) is a free protein-ligand docking server conceptualized to facilitate and assist drug discovery projects to perform docking-based virtual screening experiments accurately and using high-performance computing. The DockThor docking engine is a grid-based method designed for flexible-ligand and rigid-receptor docking. It employs a multiple-solution genetic algorithm and the MMFF94S molecular force field scoring function for pose prediction. This engine was engineered to handle highly flexible ligands, such as peptides. Affinity prediction and ranking of protein-ligand complexes are performed with the linear empirical scoring function DockTScore. The main steps of the ligand and protein preparation are available on the DockThor Portal, making it possible to change the protonation states of the amino acid residues, and include cofactors as rigid entities. The user can also customize and visualize the main parameters of the grid box. The results of docking experiments are automatically clustered and ordered, providing users with a diverse array of meaningful binding modes. The platform DockThor-VS offers a user-friendly interface and powerful algorithms, enabling researchers to conduct virtual screening experiments efficiently and accurately. The DockThor Portal utilizes the computational strength of the Brazilian high-performance platform SDumont, further amplifying the efficiency and speed of docking experiments. Additionally, the web server facilitates and enhances virtual screening experiments by offering curated structures of potential targets and compound datasets, such as proteins related to COVID-19 and FDA-approved drugs for repurposing studies. In summary, DockThor-VS is a dynamic and evolving solution for docking-based virtual screening to be applied in drug discovery projects., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Crosslinking mass spectrometry (MS) has emerged as an important technique for elucidating the in-solution structures of protein complexes and the topology of protein-protein interaction networks. However, the expanding user community lacked an integrated visualisation tool that helped them make use of the crosslinking data for investigating biological mechanisms. We addressed this need by developing xiVIEW, a web-based application designed to streamline crosslinking MS data analysis, which we present here. xiVIEW provides a user-friendly interface for accessing coordinated views of mass spectrometric data, network visualisation, annotations extracted from trusted repositories like UniProtKB, and available 3D structures. In accordance with recent recommendations from the crosslinking MS community, xiVIEW (i) provides a standards compliant parser to improve data integration and (ii) offers accessible visualisation tools. By promoting the adoption of standard file formats and providing a comprehensive visualisation platform, xiVIEW empowers both experimentalists and modellers alike to pursue their respective research interests. We anticipate that xiVIEW will advance crosslinking MS-inspired research, and facilitate broader and more effective investigations into complex biological systems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Molecular modeling and simulation serve an important role in exploring biological functions of proteins at the molecular level, which is complementary to experiments. CHARMM-GUI (https://www.charmm-gui.org) is a web-based graphical user interface that generates complex molecular simulation systems and input files, and we have been continuously developing and expanding its functionalities to facilitate various complex molecular modeling and make molecular dynamics simulations more accessible to the scientific community. Currently, covalent drug discovery emerges as a popular and important field. Covalent drug forms a chemical bond with specific residues on the target protein, and it has advantages in potency for its prolonged inhibition effects. Even though there are higher demands in modeling PDB protein structures with various covalent ligand types, proper modeling of covalent ligands remains challenging. This work presents a new functionality in CHARMM-GUI PDB Reader & Manipulator that can handle a diversity of ligand-amino acid linkage types, which is validated by a careful benchmark study using over 1,000 covalent ligand structures in RCSB PDB. We hope that this new functionality can boost the modeling and simulation study of covalent ligands., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: W.I. is the co-founder and CEO of MolCube INC., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

With radiology shortages affecting over half of the global population, the potential of artificial intelligence to revolutionize medical diagnosis and treatment is ever more important. However, lacking trust from medical professionals hinders the widespread adoption of AI models in health sciences. Explainable AI (XAI) aims to increase trust and understanding of black box models by identifying biases and providing transparent explanations. This is the first survey that explores explainable user interfaces (XUI) from a medical domain perspective, analysing the visualization and interaction methods employed in current medical XAI systems. We analysed 42 explainable interfaces following the PRISMA methodology, emphasizing the critical role of effectively conveying information to users as part of the explanation process. We contribute a taxonomy of interface design properties and identify five distinct clusters of research papers. Future research directions include contestability in medical decision support, counterfactual explanations for images, and leveraging Large Language Models to enhance XAI interfaces in healthcare., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Objectives: Tooth preparation is complicated because it requires the preparation of an abutment while simultaneously predicting the ideal shape of the tooth. This study aimed to develop and evaluate a system using augmented reality (AR) head-mounted displays (HMDs) that provide dynamic navigation capabilities for tooth preparation., Methods: The proposed system utilizes optical see-through HMDs to overlay digital information onto the real world and enrich the user's environment. By integrating tracking algorithms and three-dimensional modeling, the system provides real-time visualization and navigation capabilities during tooth preparation by using two different visualization techniques. The experimental setup involved a comprehensive analysis of the distance to the surface and cross-sectional angles between the ideal and prepared teeth using three scenarios: traditional (without AR), overlay (AR-assisted visualization of the ideal prepared tooth), and cross-sectional (AR-assisted visualization with cross-sectional views and angular displays)., Results: A user study (N = 24) revealed that the cross-sectional approach was more effective for angle adjustment and reduced the occurrence of over-reduction. Additional questionnaires revealed that the AR-assisted approaches were perceived as less difficult, with the cross-sectional approach excelling in terms of performance., Conclusions: Visualization and navigation using cross-sectional approaches have the potential to support safer tooth preparation with less overreduction than traditional and overlay approaches do. The angular displays provided by the cross-sectional approach are considered helpful for tooth preparation., Clinical Significance: The AR navigation system can assist dentists during tooth preparation and has the potential to enhance the accuracy and safety of prosthodontic treatment., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

The PPInterface dataset contains 815,082 interface structures, providing the most comprehensive structural information on protein-protein interfaces. This resource is extracted from over 215,000 three-dimensional protein structures stored in the Protein Data Bank (PDB). The dataset contains a wide range of protein complexes, providing a wealth of information for researchers investigating the structural properties of protein-protein interactions. The accompanying web server has a user-friendly interface that allows for efficient search and download functions. Researchers can access detailed information on protein interface structures, visualize them, and explore a variety of features, increasing the dataset's utility and accessibility. The dataset and web server can be found at https://3dpath.ku.edu.tr/PPInt/., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Body-machine interfaces (BoMIs)-systems that control assistive devices (e.g., a robotic manipulator) with a person's movements-offer a robust and non-invasive alternative to brain-machine interfaces for individuals with neurological injuries. However, commercially-available assistive devices offer more degrees of freedom (DOFs) than can be efficiently controlled with a user's residual motor function. Therefore, BoMIs often rely on nonintuitive mappings between body and device movements. Learning these mappings requires considerable practice time in a lab/clinic, which can be challenging. Virtual environments can potentially address this challenge, but there are limited options for high-DOF assistive devices, and it is unclear if learning with a virtual device is similar to learning with its physical counterpart. We developed a novel virtual robotic platform that replicated a commercially-available 6-DOF robotic manipulator. Participants controlled the physical and virtual robots using four wireless inertial measurement units (IMUs) fixed to the upper torso. Forty-three neurologically unimpaired adults practiced a target-matching task using either the physical (sample size n = 25) or virtual device (sample size n = 18) involving pre-, mid-, and post-tests separated by four training blocks. We found that both groups made similar improvements from pre-test in movement time at mid-test (Δvirtual: 9.9 ± 9.5 s; Δphysical: 11.1 ± 9.9 s) and post-test (Δvirtual: 11.1 ± 9.1 s; Δphysical: 11.8 ± 10.5 s) and in path length at mid-test (Δvirtual: 6.1 ± 6.3 m/m; Δphysical: 3.3 ± 3.5 m/m) and post-test (Δvirtual: 6.6 ± 6.2 m/m; Δphysical: 3.5 ± 4.0 m/m). Our results indicate the feasibility of using virtual environments for learning to control assistive devices. Future work should determine how these findings generalize to clinical populations., Competing Interests: Declaration of competing interest None of the authors have any conflict of interest., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Purpose: Impacted teeth are abnormal tooth disorders under the gums or jawbone that cannot take their normal position even though it is time to erupt. This study aims to detect all impacted teeth and to classify impacted third molars according to the Winter method with an artificial intelligence model on panoramic radiographs., Methods: In this study, 1197 panoramic radiographs from the dentistry faculty database were collected for all impacted teeth, and 1000 panoramic radiographs were collected for Winter classification. Some pre-processing methods were performed and the images were doubled with data augmentation. Both datasets were randomly divided into 80% training, 10% validation, and 10% testing. After transfer learning and fine-tuning processes, the two datasets were trained with the YOLOv8 deep learning algorithm, a high-performance artificial intelligence model, and the detection of impacted teeth was carried out. The results were evaluated with precision, recall, mAP, and F1-score performance metrics. A graphical user interface was designed for clinical use with the artificial intelligence weights obtained as a result of the training., Results: For the detection of impacted third molar teeth according to Winter classification, the average precision, average recall, and average F1 score were obtained to be 0.972, 0.967, and 0.969, respectively. For the detection of all impacted teeth, the average precision, average recall, and average F1 score were obtained as 0.991, 0.995, and 0.993, respectively., Conclusion: According to the results, the artificial intelligence-based YOLOv8 deep learning model successfully detected all impacted teeth and the impacted third molar teeth according to the Winter classification system., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Objectives: To evaluate and improve "Making Alternative Treatment Choices Intuitive and Trustworthy" (MATCH-IT)-a digital, interactive decision support tool displaying structured evidence summaries for multiple comparisons-to help physicians interpret and apply evidence from network meta-analysis (NMA) for their clinical decision-making., Study Design and Setting: We conducted a qualitative user testing study, applying principles from user-centered design in an iterative development process. We recruited a convenience sample of practicing physicians in Norway, Belgium, and Canada, and asked them to interpret structured evidence summaries for multiple comparisons-linked to clinical guideline recommendations-displayed in MATCH-IT. User testing included (a) introduction of a clinical scenario, (b) a think-aloud session with participant-tool interaction, and (c) a semistructured interview. We video recorded, transcribed, and analyzed user tests using directed content analysis. The results informed new updates in MATCH-IT., Results: Distributed across 5 development cycles we tested MATCH-IT with 26 physicians. Of these, 24 (94%) reported either no or sparse prior experience with interpretation of NMA. Physicians perceived MATCH-IT as easy to interpret and navigate, and appreciated its ability to provide an overview of the evidence. Visualization of effects in pictograms and inclusion of information on burden of treatment ("practical issues") were highlighted as potentially useful features in interacting with patients. We also identified problems, including undiscovered functionalities (drag and drop), suboptimal tutorial, and cumbersome navigation of the tool. In addition, physicians wanted definition/explanation of key terms (eg, outcomes and "certainty"), and there were concerns that overwhelming evidence from a large NMA would complicate applicability to clinical practice. This led to several updates with development of a new start page, tutorial, updated user interface for more efficient maneuvering, solutions to display definition of key terms and a "frequently asked questions" section. To facilitate interpretation of large networks, we improved categorization of results using color coding and added filtering functionality. These modifications allowed physicians to focus on interventions of interest and reduce information overload., Conclusion: This study provides proof of concept that physicians can use MATCH-IT to understand NMA evidence. Key features of MATCH-IT in a clinical context include providing an overview of the evidence, visualization of effects, and the display of information on burden of treatments. However, unfamiliarity with the Grading of Recommendations Assessment, Development and Evaluation concepts, time constraints, and accessibility at the point of care may be challenges for use. To what extent our results are transferable to real-world clinical contexts remains to be explored., Competing Interests: Declaration of competing interest T.A. is the Chair of the board and Deputy CEO of the nonprofit organization MAGIC Evidence Ecosystem Foundation (https://www.magicevidence.org). F.A. is a paid consultant for the MAGIC Evidence Ecosystem Foundation and is a part of the research team in MAGIC conducting the research project “Making Alternative Treatment Choices Intuitive and Trustworthy” (MATCH-IT). P.O.V is the CEO of the nonprofit organization MAGIC Evidence Ecosystem Foundation (https://www.magicevidence.org). P.O.L, B.S.H, and A.F.H are all part of the research team in MAGIC conducting the research project Making Alternative Treatment Choices Intuitive and Trustworthy (MATCH-IT). J.W.B. is partly supported by the Canadian Institutes of Health Research Canada Research Chair in the prevention and management of chronic pain. There are no competing interests for any other author., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Objectives: To assess quality, clinical acceptance, time-efficiency, and consistency of a novel artificial intelligence (AI)-driven tool for automated presurgical implant planning for single tooth replacement, compared to a human intelligence (HI)-based approach., Materials and Methods: To validate a novel AI-driven implant placement tool, a dataset of 10 time-matching cone beam computed tomography (CBCT) scans and intra-oral scans (IOS) previously acquired for single mandibular molar/premolar implant placement was included. An AI pre-trained model for implant planning was compared to human expert-based planning, followed by the export, evaluation and comparison of two generic implants-AI-generated and human-generated-for each case. The quality of both approaches was assessed by 12 calibrated dentists through blinded observations using a visual analogue scale (VAS), while clinical acceptance was evaluated through an AI versus HI battle (Turing test). Subsequently, time efficiency and consistency were evaluated and compared between both planning methods., Results: Overall, 360 observations were gathered, with 240 dedicated to VAS, of which 95 % (AI) and 96 % (HI) required no major, clinically relevant corrections. In the AI versus HI Turing test (120 observations), 4 cases had matching judgments for AI and HI, with AI favoured in 3 and HI in 3. Additionally, AI completed planning more than twice as fast as HI, taking only 198 ± 33 s compared to 435 ± 92 s (p < 0.05). Furthermore, AI demonstrated higher consistency with zero-degree median surface deviation (MSD) compared to HI (MSD=0.3 ± 0.17 mm)., Conclusion: AI demonstrated expert-quality and clinically acceptable single-implant planning, proving to be more time-efficient and consistent than the HI-based approach., Clinical Significance: Presurgical implant planning often requires multidisciplinary collaboration between highly experienced specialists, which can be complex, cumbersome and time-consuming. However, AI-driven implant planning has the potential to allow clinically acceptable planning, significantly more time-efficient and consistent than the human expert., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: The different tumor appearance of head and neck cancer across imaging modalities, scanners, and acquisition parameters accounts for the highly subjective nature of the manual tumor segmentation task. The variability of the manual contours is one of the causes of the lack of generalizability and the suboptimal performance of deep learning (DL) based tumor auto-segmentation models. Therefore, a DL-based method was developed that outputs predicted tumor probabilities for each PET-CT voxel in the form of a probability map instead of one fixed contour. The aim of this study was to show that DL-generated probability maps for tumor segmentation are clinically relevant, intuitive, and a more suitable solution to assist radiation oncologists in gross tumor volume segmentation on PET-CT images of head and neck cancer patients., Method: A graphical user interface (GUI) was designed, and a prototype was developed to allow the user to interact with tumor probability maps. Furthermore, a user study was conducted where nine experts in tumor delineation interacted with the interface prototype and its functionality. The participants' experience was assessed qualitatively and quantitatively., Results: The interviews with radiation oncologists revealed their preference for using a rainbow colormap to visualize tumor probability maps during contouring, which they found intuitive. They also appreciated the slider feature, which facilitated interaction by allowing the selection of threshold values to create single contours for editing and use as a starting point. Feedback on the prototype highlighted its excellent usability and positive integration into clinical workflows., Conclusions: This study shows that DL-generated tumor probability maps are explainable, transparent, intuitive and a better alternative to the single output of tumor segmentation models., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Peter M A van Ooijen reports financial support was provided by Hanarth Fund Foundation., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Teleoperation under human guidance has become an effective solution to extend human's reach in various environments. However, the teleoperation system still faces challenges of insufficient sense of both visual and haptic feedback from remote environments, which results in the inadequate guidance for the operator. In this paper, a visual/haptic integrated perception and reconstruction system (VHI-PRS) is developed to provide the operator with 3D visual information and effective haptic guidance. Specifically, a visual-based telepresence augmentation method is proposed to provide the operator with virtual-reality combined visual telepresence, where the real point cloud model is directly superimposed on virtual manipulator to avoid the time-consuming process of mesh model rendering. With the utilization of visual information, a haptic-based telepresence augmentation method is proposed to provide the operator with comprehensive force feedback, including the virtual guiding force, virtual repulsive force and real-time interactive force, which greatly helps reduce the workload of operator. Finally, a user study on grab-place task is carried out to verify the effectiveness of proposed system., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 ISA. Published by Elsevier Ltd. All rights reserved.)

The open-source console MaRCoS, which stands for "Magnetic Resonance Control System", combines hardware, firmware and software elements for integral control of Magnetic Resonance Imaging (MRI) scanners. Previous developments have focused on making the system robust and reliable, rather than on users, who have been somewhat overlooked. This work describes a Graphical User Interface (GUI) designed for intuitive control of MaRCoS, as well as compatibility with clinical environments. The GUI is based on an arrangement of tabs and a renewed Application Program Interface (API). Compared to the previous versions, the MaRGE package ("MaRCoS Graphical Environment") includes new functionalities such as the possibility to export images to standard DICOM formats, create and manage clinical protocols, or display and process image reconstructions, among other features conceived to simplify the operation of MRI scanners. All prototypes in our facilities are commanded by MaRCoS and operated with the new GUI. Here we report on its performance on an experimental 0.2 T scanner designed for hard-tissue, as well as a 72 mT portable scanner presently installed in the radiology department of a large hospital. The possibility to customize, adapt and streamline processes has substantially improved our workflows and overall experience., Competing Interests: Declaration of competing interest Authors have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Minimally invasive percutaneous insertion procedures are widely used techniques in medicine. Their success is highly dependent on the skills of the practitioner. This paper presents a haptic simulator for training in these procedures, whose key component is a real percutaneous insertion needle with a sensory system incorporated to track its 3D location at every instant. By means of the proposed embedded vision system, the attitude (spatial orientation) and depth of insertion of a real needle are estimated. The proposal is founded on a novel depth estimation procedure based on optical flow techniques, complemented by sensory fusion techniques with the attitude calculated with data from an Inertial Measurement Unit (IMU) sensor. This procedure allows estimating the needle attitude with an accuracy of tenths of a degree and the displacement with an accuracy of millimeters. The computational algorithm runs on an embedded computer with real-time constraints for tracking the movement of a real needle. This haptic needle location data is used to reproduce the movement of a virtual needle within a simulation app. As a fundamental result, an ergonomic and realistic training simulator has been successfully constructed for healthcare professionals to acquire the mental model and motor skills necessary to practice percutaneous procedures successfully., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Our Issue: Researchers often design interventions or experiences to meet the needs of a specific user. However, the user's perspective is often excluded in the process, which can minimize effectiveness due to a lack of understanding about the user, their perspective, and/or their needs. User experience (UX) research methods offer an opportunity to capture additional information about the user that can inform the design of these solutions., Methodological Literature Review: UX research became prominent during the 1990s to describe the complexity of human interactions with technological solutions. UX research methods emerged in other disciplines as strategies for describing user perspectives, needs, challenges, and potential impact of proposed solutions-these may be useful in health professions education research., Our Recommendations and Their Applications: Those interested in UX research methods should define the user, establish focused research questions, and select applicable strategies cognizant of time and resource constraints. UX research often requires a clear outline of research activities, how they address the questions, and techniques to engage representative users for data collection. Researchers can optimize data collection by creating inclusive spaces that emphasize active listening. Researchers should compile insights regularly and remember UX research emphasizes an iterative approach to design., Potential Impact: UX research can support deeper insights into users, their perspectives, their needs, and offers opportunities to co-construct solutions with their experiences in mind. These methods may help educators design impactful experiences that better achieve targeted outcomes in collaboration with their users (e.g., learners, faculty, teams)., Competing Interests: Declaration of Competing Interest None., (Copyright © 2023 Elsevier Inc. All rights reserved.)

Purpose: Development of a novel interactive visualization approach for the exploration of radiotherapy treatment plans with a focus on overlap volumes with the aim of healthy tissue sparing., Methods: We propose a visualization approach to include overlap volumes in the radiotherapy treatment plan evaluation process. Quantitative properties can be interactively explored to identify critical regions and used to steer the visualization for a detailed inspection of candidates. We evaluated our approach with a user study covering the individual visualizations and their interactions regarding helpfulness, comprehensibility, intuitiveness, decision-making and speed., Results: A user study with three domain experts was conducted using our software and evaluating five data sets each representing a different type of cancer and location by performing a set of tasks and filling out a questionnaire. The results show that the visualizations and interactions help to identify and evaluate overlap volumes according to their physical and dose properties. Furthermore, the task of finding dose hot spots can also benefit from our approach., Conclusions: The results indicate the potential to enhance the current treatment plan evaluation process in terms of healthy tissue sparing., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Background and Objective: Medical image visualization is an essential tool for conveying anatomical information. Ray-casting-based volume rendering is commonly used for generating visualizations of raw medical images. However, exposing a target area inside the skin often requires manual tuning of transfer functions or segmentation of original images, as preset parameters in volume rendering may not work well for arbitrary scanned data. This process is tedious and unnatural. To address this issue, we propose a volume visualization system that enhances the view inside the skin, enabling flexible exploration of medical volumetric data using virtual reality., Methods: In our proposed system, we design a virtual reality interface that allows users to walk inside the data. We introduce a view-dependent occlusion weakening method based on geodesic distance transform to support this interaction. By combining these methods, we develop a virtual reality system with intuitive interactions, facilitating online view enhancement for medical data exploration and annotation inside the volume., Results: Our rendering results demonstrate that the proposed occlusion weakening method effectively weakens obstacles while preserving the target area. Furthermore, comparative analysis with other alternative solutions highlights the advantages of our method in virtual reality. We conducted user studies to evaluate our system, including area annotation and line drawing tasks. The results showed that our method with enhanced views achieved 47.73% and 35.29% higher accuracy compared to the group with traditional volume rendering. Additionally, subjective feedback from medical experts further supported the effectiveness of the designed interactions in virtual reality., Conclusions: We successfully address the occlusion problems in the exploration of medical volumetric data within a virtual reality environment. Our system allows for flexible integration of scanned medical volumes without requiring extensive manual preprocessing. The results of our user studies demonstrate the feasibility and effectiveness of walk-in interaction for medical data exploration., Competing Interests: Declaration of competing interest All authors disclosed no conflict of interest related to the present work., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Introduction: we aimed to explore dentists' perceptions toward the implementation of a dental informatics risk assessment tool which estimates the risk for a patient to develop peri‑implantitis., Materials and Methods: the Implant Disease Risk Assessment Tool (IDRA) was presented to a convenience sample of seven dentists working in a university clinic, whom were asked to use IDRA with the information of three clinical cases whilst thinking aloud and then fill the System Usability Scale (SUS). A semi-structured interview technique was used with audio record to allow free expression of participants' perceptions related to the IDRA. The interviews information was categorized and analyzed by the authors., Results: to our knowledge, this is the first study conducted to develop a qualitative usability test of IDRA, evaluating the effectiveness, efficiency, and users' satisfaction. There were more variations in responses the greater the degree of complexity of the clinical case. Generally, the participants classified the tool as good, getting usability values of 77,2 (SD 19,8) and learnability 73,2 (SD 24,5)., Conclusion: four additional factors should be considered to improve IDRA tool: 1) considering the relation between contour angle and peri-implant tissue height; 2) automatic periodontal classification in the IDRA tool after completing the periodontogram in the clinical software; 3) presentation of a flowchart to assist therapeutic decisions alongside the final score defined by the IDRA tool; 4) integrating of precision tests such as Implantsafe® DR… (dentognostics gmbh, Jena) and Oralyzer®(dentognostics gmbh, Jena)., Clinical Significance: etiology and pathogenesis of peri‑implant diseases is multifactorial. These tools must follow a natural integration to be easily applied in a clinical setting. It is important to study their usability from the clinicians' point of view, evaluating the effectiveness, efficiency, and users' satisfaction., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Background: The use of respiratory protection remains important in protecting health care workers from airborne pathogens such as viruses. Respirator supply is constantly changing with new models regularly becoming available. Health services should consider a broad range of factors when procuring respirators, including the results of quantitative fit testing in a representative sample of the workforce. Subjective comfort factors and compatibility with a variety of workplace tasks, such as suitability for staff use near magnetic resonance imaging (MRI) environments where relevant, should also be considered. This article compares the quantitative fit factors and user assessments for two styles of flat-fold cup respirators, Care Essentials (CE) MSK-002 P2 and BYD DE2322 N95., Methods: Quantitative fit tests (QNFT) were performed on 300 participants on each model of respirator in this randomised crossover trial. Participants then completed a questionnaire on their assessments of each respirator., Results: The Care Essentials MSK-002 had a significantly higher quantitative fit test pass rate than the BYD DE2322 (57% vs 18%, p < 0.001). There was no concordance between fit test pass rates for each model. Additionally, the Care Essentials MSK-002 achieved significantly higher scores on each of the responses in the subjective usability survey., Conclusion: It is recommended that the Care Essentials MSK-002 be made available for health care use due to higher QNFT pass rates, higher subjective usability assessment scores, plus its potential for use in MRI environments when compared to the BYD DE2322., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)