"Right on all Occasions?" - On the Feasibility of Laterality Research Using a Smartphone Dichotic Listening Application

Most psychological experimentation takes place in laboratories aiming to maximize experimental control; however, this creates artificial environments that are not representative of real-life situations. Since cognitive processes usually take place in noisy environments, they should also be tested in these contexts. The recent advent of smartphone technology provides an ideal medium for such testing. In order to examine the feasibility of mobile devices (MD) in psychological research in general, and laterality research in particular, we developed a MD version of the widely used speech laterality test, the consonant-vowel dichotic listening (DL) paradigm, for use with iPhones/iPods. First, we evaluated the retest reliability and concurrent validity of the DL paradigm in its MD version in two samples tested in controlled, laboratory settings (Experiment 1). Second, we explored its ecological validity by collecting data from the general population by means of a free release of the MD version (iDichotic) to the iTunes App Store (Experiment 2). The results of Experiment 1 indicated high reliability (r_ICC = 0.78) and validity (r_ICC = 0.76–0.82) of the MD version, which consistently showed the expected right ear advantage (REA). When tested in real-life settings (Experiment 2), participants (N = 167) also showed a significant REA. Importantly, the size of the REA was not dependent on whether the participants chose to listen to the syllables in their native language or not. Together, these results establish the current MD version as a valid and reliable method for administering the DL paradigm both in experimentally controlled as well as uncontrolled settings. Furthermore, the present findings support the feasibility of using smartphones in conducting large-scale field experiments.

Augmented reality in computer-assisted interventions based on patient-specific 3D printed reference

Augmented reality (AR) can be an interesting technology for clinical scenarios as an alternative to conventional surgical navigation. However, the registration between augmented data and real-world spaces is a limiting factor. In this study, the authors propose a method based on desktop three-dimensional (3D) printing to create patient-specific tools containing a visual pattern that enables automatic registration. This specific tool fits on the patient only in the location it was designed for, avoiding placement errors. This solution has been developed as a software application running on Microsoft HoloLens. The workflow was validated on a 3D printed phantom replicating the anatomy of a patient presenting an extraosseous Ewing's sarcoma, and then tested during the actual surgical intervention. The application allowed physicians to visualise the skin, bone and tumour location overlaid on the phantom and patient. This workflow could be extended to many clinical applications in the surgical field and also for training and simulation, in cases where hard body structures are involved. Although the authors have tested their workflow on AR head mounted display, they believe that a similar approach can be applied to other devices such as tablets or smartphones.

A randomized controlled trial of a mobile application-assisted nurse-led model used to improve treatment outcomes in children with asthma

AIM

To evaluate the effectiveness of a mobile application-assisted nurse-led management model in childhood asthma.

BACKGROUND

Studies have shown that a nurse-led asthma management model can improve asthma outcomes. However, the role of a mobile application-assisted nurse-led model in paediatric asthma management has not been studied well.

DESIGN

A multi-centre randomized clinical trial.

METHODS

The trial was conducted between March 2017-March 2018. A total of 152 children (6 to 11.9 years old) were enrolled, with 77 children in the experimental group and 75 in the control group. All children received nurse-led asthma management and other routine treatment measures, including inhaled corticosteroids. Meanwhile, a mobile application was used to manage asthma only for children in the experimental group. Primary outcome was frequency of asthma exacerbations. All outcomes were evaluated twice a month for 12 months.

RESULTS

Compared with the pre-enrollment period, frequency of asthma exacerbations decreased in the post-enrollment period in the two groups, with a greater decrease in the experimental group. Compared with children in the control group, children in the experimental group had better secondary outcomes, such as improved adherence, higher Childhood Asthma Control Test scores, decreased respiratory tract infections, days of antibiotic use, days of school absence, parental work loss, and medical expenses.

CONCLUSION

A mobile application-assisted nurse-led management model decreased asthma exacerbations and improved secondary outcomes in children with asthma. Further research is needed to verify its validity in larger population samples.

IMPACT

Children with asthma benefited from a nurse-led asthma management model when combined with mobile application. This trial suggested that computer and Internet technologies should be incorporated into nurse-led asthma strategy in paediatric asthma management.

TRIAL REGISTRATION

The current trial was registered online with the Chinese Clinical Trial Registry (registration number: ChiCTR1800016726).

Health Equity Assessment Toolkit Plus (HEAT Plus): software for exploring and comparing health inequalities using uploaded datasets

As a key step in advancing the sustainable development goals, the World Health Organisation (WHO) has placed emphasis on building capacity for measuring and monitoring health inequalities. A number of resources have been developed, including the Health Equity Assessment Toolkit (HEAT), a software application that facilitates the assessment of within-country health inequalities. Following user demand, an Upload Database Edition of HEAT, HEAT Plus, was developed. Launched in July 2017, HEAT Plus allows users to upload their own databases and assess inequalities at the global, national or subnational level for a range of (health) indicators and dimensions of inequality. The software is open-source, operates on Windows and Macintosh platforms and is readily available for download from the WHO website. The flexibility of HEAT Plus makes it a suitable tool for both global and national inequality assessments. Further developments will include interactive graphs, maps and translation into different languages.

Simulation training for medical emergencies in the dental setting using an inexpensive software application

INTRODUCTION

Every dental provider needs to be educated about medical emergencies to provide safe dental care. Simulation training is available with simulators such as advanced life support manikins and robot patients. However, the purchase and development costs of these simulators are high. We have developed a simulation training course on medical emergencies using an inexpensive software application. The purpose of this study was to evaluate the educational effectiveness of this course.

MATERIALS AND METHODS

Fifty-one dental providers participated in this study from December 2014 to March 2015. Medical simulation software was used to simulate a patient's vital signs. We evaluated participants' ability to diagnose and treat vasovagal syncope or anaphylaxis with an evaluation sheet and conducted a questionnaire before and after the scenario-based simulation training.

RESULTS

The median evaluation sheet score for vasovagal syncope increased significantly from 7/9 before to 9/9 after simulation training. The median score for anaphylaxis also increased significantly from 8/12 to 12/12 (P < .01). For the item "I can treat vasovagal syncope/anaphylaxis adequately," the percentage responding "Strongly agree" or "Agree" increased from 14% to 56% for vasovagal syncope and from 6% to 42% for anaphylaxis with simulation training.

CONCLUSIONS

This simulation course improved participants' ability to diagnose and treat medical emergencies and improved their confidence. This course can be offered inexpensively using a software application.

Introduction to methodology of dose-response meta-analysis for binary outcome: With application on software

OBJECTIVE

Dose-response meta-analysis (DRMA) is widely applied to investigate the dose-specific relationship between independent and dependent variables. Such methods have been in use for over 30 years and are increasingly employed in healthcare and clinical decision-making. In this article, we give an overview of the methodology used in DRMA.

METHODS

We summarize the commonly used regression model and the pooled method in DRMA. We also use an example to illustrate how to employ a DRMA by these methods.

RESULTS

Five regression models, linear regression, piecewise regression, natural polynomial regression, fractional polynomial regression, and restricted cubic spline regression, were illustrated in this article to fit the dose-response relationship. And two types of pooling approaches, that is, one-stage approach and two-stage approach are illustrated to pool the dose-response relationship across studies. The example showed similar results among these models.

CONCLUSION

Several dose-response meta-analysis methods can be used for investigating the relationship between exposure level and the risk of an outcome. However the methodology of DRMA still needs to be improved.

How to optimize radiological images captured from digital cameras, using the Adobe Photoshop 6.0 program

Over the past decade, the technology that permits images to be digitized and the reduction in the cost of digital equipment allows quick digital transfer of any conventional radiological film. Images then can be transferred to a personal computer, and several software programs are available that can manipulate their digital appearance. In this article, the fundamentals of digital imaging are discussed, as well as the wide variety of optional adjustments that the Adobe Photoshop 6.0 (Adobe Systems, San Jose, CA) program can offer to present radiological images with satisfactory digital imaging quality.

Validation of ACCESS: an automated tool to support self-management of COPD exacerbations

Background

To support patients with COPD in their self-management of symptom worsening, we developed Adaptive Computerized COPD Exacerbation Self-management Support (ACCESS), an innovative software application that provides automated treatment advice without the interference of a health care professional. Exacerbation detection is based on 12 symptom-related yes-or-no questions and the measurement of peripheral capillary oxygen saturation (SpO₂), forced expiratory volume in one second (FEV₁), and body temperature. Automated treatment advice is based on a decision model built by clinical expert panel opinion and Bayesian network modeling. The current paper describes the validity of ACCESS.

Methods

We performed secondary analyses on data from a 3-month prospective observational study in which patients with COPD registered respiratory symptoms daily on diary cards and measured SpO₂, FEV₁, and body temperature. We examined the validity of the most important treatment advice of ACCESS, ie, to contact the health care professional, against symptom- and event-based exacerbations.

Results

Fifty-four patients completed 2,928 diary cards. One or more of the different pieces of ACCESS advice were provided in 71.7% of all cases. We identified 115 symptom-based exacerbations. Cross-tabulation showed a sensitivity of 97.4% (95% CI 92.0-99.3), specificity of 65.6% (95% CI 63.5-67.6), and positive and negative predictive value of 13.4% (95% CI 11.2-15.9) and 99.8% (95% CI 99.3-99.9), respectively, for ACCESS' advice to contact a health care professional in case of an exacerbation.

Conclusion

In many cases (71.7%), ACCESS gave at least one self-management advice to lower symptom burden, showing that ACCES provides self-management support for both day-to-day symptom variations and exacerbations. High sensitivity shows that if there is an exacerbation, ACCESS will advise patients to contact a health care professional. The high negative predictive value leads us to conclude that when ACCES does not provide the advice to contact a health care professional, the risk of an exacerbation is very low. Thus, ACCESS can safely be used in patients with COPD to support self-management in case of an exacerbation.

Validation and application of the Non-Verbal Behavior Analyzer: An automated tool to assess non-verbal emotional expressions in psychotherapy

BACKGROUND

Emotions play a key role in psychotherapy. However, a problem with examining emotional states via self-report questionnaires is that the assessment usually takes place after the actual emotion has been experienced which might lead to biases and continuous human ratings are time and cost intensive. Using the AI-based software package Non-Verbal Behavior Analyzer (NOVA), video-based emotion recognition of arousal and valence can be applied in naturalistic psychotherapeutic settings. In this study, four emotion recognition models (ERM) each based on specific feature sets (facial: OpenFace, OpenFace-Aureg; body: OpenPose-Activation, OpenPose-Energy) were developed and compared in their ability to predict arousal and valence scores correlated to PANAS emotion scores and processes of change (interpersonal experience, coping experience, affective experience) as well as symptoms (depression and anxiety in HSCL-11).

MATERIALS AND METHODS

A total of 183 patient therapy videos were divided into a training sample (55 patients), a test sample (50 patients), and a holdout sample (78 patients). The best ERM was selected for further analyses. Then, ERM based arousal and valence scores were correlated with patient and therapist estimates of emotions and processes of change. Furthermore, using regression models arousal and valence were examined as predictors of symptom severity in depression and anxiety.

RESULTS

The ERM based on OpenFace produced the best agreement to the human coder rating. Arousal and valence correlated significantly with therapists' ratings of sadness, shame, anxiety, and relaxation, but not with the patient ratings of their own emotions. Furthermore, a significant negative correlation indicates that negative valence was associated with higher affective experience. Negative valence was found to significantly predict higher anxiety but not depression scores.

CONCLUSION

This study shows that emotion recognition with NOVA can be used to generate ERMs associated with patient emotions, affective experiences and symptoms. Nevertheless, limitations were obvious. It seems necessary to improve the ERMs using larger databases of sessions and the validity of ERMs needs to be further investigated in different samples and different applications. Furthermore, future research should take ERMs to identify emotional synchrony between patient and therapists into account.

Wire Placement in the Sustentaculum Tali Using a 2D Projection-Based Software Application for Mobile C-Arms: Cadaveric Study

BACKGROUND

Indirect screw fixation of the sustentaculum tali in the lateral-medial direction can be challenging due to the complex calcaneal anatomy. A novel 2-dimensional (2D) projection-based software application detects Kirschner wires (K-wires) and visualizes their intended direction as a colored trajectory. The aim of this prospectively randomized cadaver study was to investigate whether the software would facilitate the indirect K-wire placement in the sustentaculum tali.

METHODS

In 20 cadaver foot specimens, K-wires were placed indirectly in the sustentaculum tali by an experienced and an inexperienced surgeon, with and without using the application. Number of placement attempts, duration of procedure, fluoroscopy time, and number of individual fluoroscopy images were recorded. Each wire's position was analyzed in a 3-dimensional (3D) C-arm scan by an experienced blinded investigator.

RESULTS

Use of the software by the inexperienced surgeon significantly reduced the number of placement attempts from 3.2 to 1.2 ( P = .006). The application also reduced operating time, from 273 s to 199 s ( P = .15), and fluoroscopy time, from 41 s to 29 s ( P = .15). Using the software, the experienced surgeon had a longer operating time (139 s to 183 s; P = .30), longer fluoroscopy time (5.6 s to 9.2 s; P = .17), and more individual fluoroscopy images (11.6 to 14.8; P = .30). Wire position did not show significant differences in both cases.

CONCLUSION

During indirect K-wire placement in the sustentaculum tali, the software appeared to be a useful tool for the inexperienced surgeon. In our chosen study setting, the experienced surgeon did not benefit from the software.

CLINICAL RELEVANCE

Possible indications for the software would be fractures of the proximal femur, sacrum, sacroiliac instabilities, vertebral bodies, scaphoid, Lisfranc joint, talus and calcaneus.

Farm Owners and Workers as Key Informants in User-Centered Occupational Health Prototype Development: A Stakeholder-Engaged Project

BACKGROUND

The cost of workplace injuries and illnesses significantly impacts the overall cost of health care and is a significant annual economic burden in the United States. Many dairy and pork farm owners in the Upper Midwest have expanded operations and taken on the role of manager and employer yet receive little training in injury prevention, farm safety, or workers' compensation programs and processes. Clinicians play a key role in the return to work of injured and ill farmers and farmworkers to their jobs, though little to no formal training is offered by medical schools.

OBJECTIVE

This stakeholder-engaged project aimed to develop a prototype application designed to assist clinicians in returning injured farmworkers to light-duty job assignments with their current employers and to assess farm owners' and managers' attitudes toward and barriers to adopting mobile health tools for themselves or their employees.

METHODS

We conducted 12 semistructured interviews with English-speaking farm owners and farmworkers from the Upper Midwest: 5 English-speaking and Spanish-speaking farmworker focus groups and 8 postproject interviews with farm owners that focused on attitudes and barriers to adoption of the developed software. Interviews and focus groups were audio recorded, and data were analyzed and thematically coded using audio coding.

RESULTS

Interviews and worker focus groups guided an iterative design and development cycle, which informed workflow design, button placement, and output sheets that offer specific light-duty farm work recommendations for the injured worker to discuss with his or her employer.

CONCLUSIONS

The development of a complex prototype intended to impact patient care is a significant undertaking. Reinventing a paper-based process that can eventually integrate with an electronic health record or a private company's human resources system requires substantial stakeholder input from each facet including patients, employers, and clinical care teams. The prototype is available for testing, but further research is needed in the form of clinical trials to assess the effectiveness of the process and the software's impact on patients and employers.

Reliability and validity of smartphone applications to measure the spinal range of motion: A systematic review

Introduction: The objective of this study is to conduct a systematic review on the reliability and validity of various smartphone applications for spinal range of motion (ROM) measurements.Methods: Eleven studies were selected following an electronic search of PubMed, CINAHAL, Medline, Embase and SPORTDiscus. Quality appraisals of selected studies were conducted using a standardized appraisal tool.Results: Most studies demonstrated a good intra- and inter-rater reliability, as well as validity in more than 50% of joint movements. At the same time, relative reliability/validity outcomes (e.g. interclass correlation co-efficient) were stronger than absolute reliability/validity outcomes (e.g. mean differences, limits of agreement). Spinal rotation movement showed less reliability and validity when compared to other spinal movements.ConclusionsːResult of the study supports the use of smartphone applications for ROM measurements of spinal joints. However, we cannot advocate the most appropriate application for spinal ROM measurement or suggest which application is superior to all others. As clinicians have multiple options in selecting applications, it is recommended they use applications that have proven reliable and valid for that particular joint. Data from this study provides clinicians with evidence-based research on smartphone devices for measuring spinal joint ROM in clinical settings.

Comparison of postural assessment and awareness in individuals receiving posture training using the digital AI posture assessment and correction system

Objectives. This study aimed to compare postural assessment and postural awareness using the artificial intelligence posture evaluation and correction system (APECS) in individuals receiving posture training. Methods. Participants' physical characteristics were recorded. The participants' posture was evaluated with APECS and the New York posture rating chart test (NYPR), and their body and postural awareness was evaluated with the body awareness questionnaire (BAQ), postural awareness form (PAF) and postural habits and awareness scale (PHAS). Results. In the study, results of the PAF (p = 0.000), BAQ (p = 0.013) and PHAS (p = 0.033) were found to be different between the groups. While the groups were similar in the PHAS sub-dimension of postural habit (p = 0.331), there was a significant difference between the groups in postural awareness (p = 0.04). NYPR results of the participants in the group receiving posture training were similar to those in the group not receiving posture training (p = 0.45). Conclusion. Postural deviations measured by digital posture assessment in individuals receiving posture training were significantly different in the group receiving posture training. Postural awareness and body awareness were better in individuals who received posture training than in individuals who did not receive posture training.

A Configurable Monitoring, Testing, and Diagnosis System for Electric Power Plants

The specific equipment, installation and machinery infrastructure of an electric power system have always required specially designed data acquisition systems and devices to ensure their safe operation and monitoring. Besides maintenance, periodical upgrade must be ensured for these systems, to meet the current practical requirements. Monitoring, testing, and diagnosis altogether represent key activities in the development process of electric power elements. This work presents the detailed structure and implementation of a complex, configurable system which can assure efficient monitoring, testing, and diagnosis for various electric power infrastructures, with proven efficiency through a comprehensive set of experimental results obtained in real running conditions. The developed hardware and software implementation is a robust structure, optimized for acquiring a large variety of electrical signals, also providing easy and fast connection within the monitored environment. Its high level of configurability and very good price-performance ratio makes it an original and handy solution for electric power infrastructures.

Impact of a New Gynecologic Oncology Hashtag During Virtual-Only ASCO Annual Meetings: An X (Twitter) Social Network Analysis

Background

Official conference hashtags are commonly used to promote tweeting and social media engagement. The reach and impact of introducing a new hashtag during an oncology conference have yet to be studied. The American Society of Clinical Oncology (ASCO) conducts an annual global meeting, which was entirely virtual due to the COVID-19 pandemic in 2020 and 2021.

Objective

This study aimed to assess the reach and impact (in the form of vertices and edges generated) and X (formerly Twitter) activity of the new hashtags #goASCO20 and #goASCO21 in the ASCO 2020 and 2021 virtual conferences.

Methods

New hashtags (#goASCO20 and #goASCO21) were created for the ASCO virtual conferences in 2020 and 2021 to help focus gynecologic oncology discussion at the ASCO meetings. Data were retrieved using these hashtags (#goASCO20 for 2020 and #goASCO21 for 2021). A social network analysis was performed using the NodeXL software application.

Results

The hashtags #goASCO20 and #goASCO21 had similar impacts on the social network. Analysis of the reach and impact of the individual hashtags found #goASCO20 to have 150 vertices and 2519 total edges and #goASCO20 to have 174 vertices and 2062 total edges. Mentions and tweets between 2020 and 2021 were also similar. The circles representing different users were spatially arranged in a more balanced way in 2021. Tweets using the #goASCO21 hashtag received significantly more responses than tweets using #goASCO20 (75 times in 2020 vs 360 times in 2021; z value=16.63 and P<.001). This indicates increased engagement in the subsequent year.

Conclusions

Introducing a gynecologic oncology specialty-specific hashtag (#goASCO20 and #goASCO21) that is related but different from the official conference hashtag (#ASCO20 and #ASCO21) helped facilitate discussion on topics of interest to gynecologic oncologists during a virtual pan-oncology meeting. This impact was visible in the social network analysis.

DEMIGOD: A Low-Cost Microcontroller-Based Closed-Loop System Integrating Nanoengineered Sweat-Based Glucose Monitoring and Controlled Transdermal Nanoemulsion Release of Hypoglycemic Treatment with a Software Application for Noninvasive Personalized Diabetes Care

This study endeavored to design and develop an innovative closed-loop diagnostic and therapeutic system with the following objectives: (a) the noninvasive detection of glucose concentration in sweat utilizing nanonengineered screen-printed biosensors; (b) the management of measured data through a specialized computer system comprising both hardware and software components, thereby enabling the precise control of therapeutic responses via a patch-based nanomedicine delivery system. This initiative addresses the significant challenges inherent in the management of diabetes mellitus, including the imperative need for glucose-level monitoring to optimize glycemic control. Leveraging chronoamperometric results as a foundational dataset and the in vivo hypoglycemic activity of nanoemulsion formulations, this research underscores the efficacy and accuracy of glucose concentration estimation, decision-making mechanism responses, and transdermal hypoglycemic treatment effects, within the proposed system.

A New Tool for an Awareness Plan Concerning Critical Issues, Needs and Attitudes of Citizens on the Use of Medicines

This article describes a pilot study to test the adequacy of a newly developed tool for an awareness plan on the importance of properly using pharmaceuticals. The new tool consists of face-to-face interviews with adult citizens on their approach to the use of medicines and of the following data analysis with a dedicated software application. The pilot study was carried out in a sample area of Sardinia, in Italy. The data from the interviews collected anonymously and analysed in aggregate actually emphasised the critical issues and needs in the use of pharmaceuticals in the sample area involved, also encouraging communication among different actors. The pilot study revealed that the designed tool could represent a novel strategy to stimulate interchanges of information on the proper use of pharmaceuticals with a potential impact on people's health.

A Compact IIoT System for Remote Monitoring and Control of a Micro Hydropower Plant

Remote monitoring and operation evaluation applications for industrial environments are modern and easy means of exploiting the provided resources of specific systems. Targeted micro hydropower plant functionalities (such as tracking and adjusting the values of functional parameters, real-time fault and cause signalizing, condition monitoring assurance, and assessments of the need for maintenance activities) require the design of reliable and efficient devices or systems. The present work describes the design and implementation procedure of an Industrial Internet of Things (IIoT) system configured for a basic micro hydropower plant architecture and assuring simple means of customization for plant differences in structure and operation. The designed system features a set of commonly used functions specific to micro hydropower exploitation, providing maximum performance and efficiency.

Automation and Optimization of Rat Heart Decellularization Using a Vibrating Fluid Column

This paper presents the validation of a software application to optimize the discoloration process in simulated hearts and to automate and determine the final moment of decellularization in rat hearts using a vibrating fluid column. The implemented algorithm specifically for the automated verification of a simulated heart's discoloration process was optimized in this study. Initially, we used a latex balloon containing enough dye to reach the opacity of a heart. The complete discoloration process corresponds to complete decellularization. The developed software automatically detects the complete discoloration of a simulated heart. Finally, the process stops automatically. Another goal was to optimize the Langendorff-type experimental apparatus, which is pressure-controlled and equipped with a vibrating fluid column that shortens the decellularization time by mechanically acting directly on cell membranes. Control experiments were performed with the designed experimental device and the vibrating liquid column using different decellularization protocols for hearts taken from rats. In this work, we used a commonly utilized solution based on sodium dodecyl sulfate. Ultraviolet spectrophotometry was used to measure the evolution of the dye concentration in the simulated hearts and, similarly, to determine the concentrations of deoxyribonucleic acid (DNA) and proteins in the rat hearts.

Toward quantitative intrafractional monitoring in paraspinal SBRT using a proprietary software application: clinical implementation and patient results

Objective.We report on paraspinal motion and the clinical implementation of our proprietary software that leverages Varian's intrafraction motion review (IMR) capability for quantitative tracking of the spine during paraspinal SBRT. The work is based on our prior development and analysis on phantoms.Approach.To address complexities in patient anatomy, digitally reconstructed radiographs (DRR's) that highlight only the spine or hardware were constructed as tracking reference. Moreover, a high-pass filter and first-pass coarse search were implemented to enhance registration accuracy and stability. For evaluation, 84 paraspinal SBRT patients with sites spanning across the entire vertebral column were enrolled with prescriptions ranging from 24 to 40 Gy in one to five fractions. Treatments were planned and delivered with 9 IMRT beams roughly equally distributed posteriorly. IMR was triggered every 200 or 500 MU for each beam. During treatment, the software grabbed the IMR image, registered it with the corresponding DRR, and displayed the motion result in near real-time on auto-pilot mode. Four independent experts completed offline manual registrations as ground truth for tracking accuracy evaluation.Main results.Our software detected ≥1.5 mm and ≥2 mm motions among 17.1% and 6.6% of 1371 patient images, respectively, in either lateral or longitudinal direction. In the validation set of 637 patient images, 91.9% of the tracking errors compared to manual registration fell within ±0.5 mm in either direction. Given a motion threshold of 2 mm, the software accomplished a 98.7% specificity and a 93.9% sensitivity in deciding whether to interrupt treatment for patient re-setup.Significance.Significant intrafractional motion exists in certain paraspinal SBRT patients, supporting the need for quantitative motion monitoring during treatment. Our improved software achieves high motion tracking accuracy clinically and provides reliable guidance for treatment intervention. It offers a practical solution to ensure accurate delivery of paraspinal SBRT on a conventional Linac platform.

Design and Implementation of an AI-Enabled Sensor for the Prediction of the Behaviour of Software Applications in Industrial Scenarios

In the era of Industry 4.0 and 5.0, a transformative wave of softwarisation has surged. This shift towards software-centric frameworks has been a cornerstone and has highlighted the need to comprehend software applications. This research introduces a novel agent-based architecture designed to sense and predict software application metrics in industrial scenarios using AI techniques. It comprises interconnected agents that aim to enhance operational insights and decision-making processes. The forecaster component uses a random forest regressor to predict known and aggregated metrics. Further analysis demonstrates overall robust predictive capabilities. Visual representations and an error analysis underscore the forecasting accuracy and limitations. This work establishes a foundational understanding and predictive architecture for software behaviours, charting a course for future advancements in decision-making components within evolving industrial landscapes.

Assessment of a smartphone-based software application as a potential digital tool in tooth shade selection: a prospective clinical study

OBJECTIVE

To determine the reliability of a newly developed software application (Shadent) in natural tooth shade selection.

METHOD AND MATERIALS

A smartphone device with the self-developed Shadent software (patent no. 201841046815, Intellectual Property of India) recorded tooth shades of the maxillary right central incisor and canine for 250 volunteers. A prospective, clinical, double-blind trial was conducted. The shade match obtained under the standardized condition from Shadent software was compared with the visual method and spectrophotometer. The digital recordings were captured by two master's candidates, while the visual shade match was evaluated by four master's candidates and one faculty member from the Department of Prosthodontics. Crosstab analysis assessed the relationship between the visual method of shade selection, a spectrophotometer, and the Shadent software. Cohen kappa was used to measure the agreement between the methods.

RESULTS

A kappa coefficient of 0.59 was observed between the visual assessment and the Shadent software, with an agreement percentage of 64.6%. A kappa coefficient of 0.65 was observed between the spectrophotometer and the Shadent software with an agreement of 69.3%. The software also had a better Cohen kappa agreement with the maxillary central incisor than the maxillary canine.

CONCLUSIONS

Shadent's reliability was comparable with the visual and spectrophotometric methods and offered repeatability with standardized light intensity.

Artificial Intelligence-Enabled Software Prototype to Inform Opioid Pharmacovigilance From Electronic Health Records: Development and Usability Study

Background

The use of patient health and treatment information captured in structured and unstructured formats in computerized electronic health record (EHR) repositories could potentially augment the detection of safety signals for drug products regulated by the US Food and Drug Administration (FDA). Natural language processing and other artificial intelligence (AI) techniques provide novel methodologies that could be leveraged to extract clinically useful information from EHR resources.

Objective

Our aim is to develop a novel AI-enabled software prototype to identify adverse drug event (ADE) safety signals from free-text discharge summaries in EHRs to enhance opioid drug safety and research activities at the FDA.

Methods

We developed a prototype for web-based software that leverages keyword and trigger-phrase searching with rule-based algorithms and deep learning to extract candidate ADEs for specific opioid drugs from discharge summaries in the Medical Information Mart for Intensive Care III (MIMIC III) database. The prototype uses MedSpacy components to identify relevant sections of discharge summaries and a pretrained natural language processing (NLP) model, Spark NLP for Healthcare, for named entity recognition. Fifteen FDA staff members provided feedback on the prototype's features and functionalities.

Results

Using the prototype, we were able to identify known, labeled, opioid-related adverse drug reactions from text in EHRs. The AI-enabled model achieved accuracy, recall, precision, and F1-scores of 0.66, 0.69, 0.64, and 0.67, respectively. FDA participants assessed the prototype as highly desirable in user satisfaction, visualizations, and in the potential to support drug safety signal detection for opioid drugs from EHR data while saving time and manual effort. Actionable design recommendations included (1) enlarging the tabs and visualizations; (2) enabling more flexibility and customizations to fit end users' individual needs; (3) providing additional instructional resources; (4) adding multiple graph export functionality; and (5) adding project summaries.

Conclusions

The novel prototype uses innovative AI-based techniques to automate searching for, extracting, and analyzing clinically useful information captured in unstructured text in EHRs. It increases efficiency in harnessing real-world data for opioid drug safety and increases the usability of the data to support regulatory review while decreasing the manual research burden.

Magnetoencephalography Atlas Viewer for Dipole Localization and Viewing

Magnetoencephalography (MEG) is a noninvasive neuroimaging technique widely recognized for epilepsy and tumor mapping. MEG clinical reporting requires a multidisciplinary team, including expert input regarding each dipole's anatomic localization. Here, we introduce a novel tool, the "Magnetoencephalography Atlas Viewer" (MAV), which streamlines this anatomical analysis. The MAV normalizes the patient's Magnetic Resonance Imaging (MRI) to the Montreal Neurological Institute (MNI) space, reverse-normalizes MNI atlases to the native MRI, identifies MEG dipole files, and matches dipoles' coordinates to their spatial location in atlas files. It offers a user-friendly and interactive graphical user interface (GUI) for displaying individual dipoles, groups, coordinates, anatomical labels, and a tri-planar MRI view of the patient with dipole overlays. It evaluated over 273 dipoles obtained in clinical epilepsy subjects. Consensus-based ground truth was established by three neuroradiologists, with a minimum agreement threshold of two. The concordance between the ground truth and MAV labeling ranged from 79% to 84%, depending on the normalization method. Higher concordance rates were observed in subjects with minimal or no structural abnormalities on the MRI, ranging from 80% to 90%. The MAV provides a straightforward MEG dipole anatomic localization method, allowing a nonspecialist to prepopulate a report, thereby facilitating and reducing the time of clinical reporting.

Simulation training for medical emergencies: Evaluation of dentists' long-term learning skills and confidence

INTRODUCTION

As the population ages and more patients experience medical emergencies during dental treatments, dentists must competently and confidently manage these situations. We developed a simulation training course for medical emergencies in the dental setting using an inexpensive vital sign simulation app for smartphones/tablets without the need for an expensive simulator. However, the duration for which this effect is maintained is unclear. This study was performed to evaluate the long-term educational effect at 3, 6, and 12 months after taking the course.

MATERIALS AND METHODS

Thirty-nine dental residents participated in this course. Scenarios included vasovagal syncope, anaphylaxis, hyperventilation syndrome, and acute coronary syndrome, each of which the participants had to diagnose and treat. The participants were evaluated using a checklist for anaphylaxis diagnosis and treatment skills immediately after and 3, 6, and 12 months after the course. The participants were also surveyed about their confidence in diagnosing and treating these conditions by questionnaire before, immediately after, and 3, 6, and 12 months after the course.

RESULTS

The checklist scores for anaphylaxis were significantly lower at 3, 6, and 12 months after the course than immediately after the course. The percentage of participants who provided a correct diagnosis and appropriate treatment for vasovagal syncope, hyperventilation syndrome, and acute coronary syndrome was lower at all reassessments than immediately after the course.

CONCLUSION

Because medical emergency management skills and confidence declined within 3 months, it would be useful to introduce a refresher course approximately 3 months after the initial course to maintain skills and confidence.