Accuracy Of App Research Articles

APLIKASI PENERJEMAH BAHASA ISYARAT BISINDO MENGGUNAKAN METODE YOLOv5 BERBASIS MOBILE

This research aims to develop a mobile application that is able to recognize and translate BISINDO in real-time using the YOLOv5 method. YOLOv5 (You Only Look Once version 5) is an efficient and fast object detection algorithm, making it suitable for implementation on mobile devices. In developing this application, we collected and labeled a typical BISINDO dataset, then trained the YOLOv5 model to detect and recognize these characteristics. The test results show that the implemented model has high accuracy in recognizing various BISINDO characteristics. Apart from that, this application is also designed with an intuitive and easy-to-use user interface, so that users can quickly and easily communicate with people using sign language. With this application, it is hoped that it can expand communication access for people with hearing impaired and increase their social inclusion in society. Further research is needed to continue improving the app's accuracy and efficiency and adding more features that support different user needs.

PsySuite: An android application designed to perform multimodal psychophysical testing.

In behavioral sciences, there is growing concern about the inflation of false-positive rates due to the amount of under-powered studies that have been shared in the past years. While problematic, having the possibility to recruit (lots of) participants (for a lot of time) is realistically not achievable for many research facilities. Factors that hinder the reaching of optimal sample sizes are, to name but a few, research costs, participants' availability and commitment, and logistics. We challenge these issues by introducing PsySuite, an Android app designed to foster a remote approach to multimodal behavioral testing. To validate PsySuite, we first evaluated its ability to generate stimuli appropriate to rigorous psychophysical testing, measuring both the app's accuracy (i.e., stimuli's onset, offset, and multimodal simultaneity) and precision (i.e., the stability of a given pattern across trials), using two different smartphone models. We then evaluated PsySuite's ability to replicate perceptual performances obtained using a classic psychophysical paradigm, comparing sample data collected with the app against those measured via a PC-based setup. Our results showed that PsySuite could accurately reproduce stimuli with a minimum duration of 7ms, 17ms, and 30ms for the auditory, visual, and tactile modalities, respectively, and that perceptual performances obtained with PsySuite were consistent with the perceptual behavior observed using the classical setup. Combined with the high accessibility inherently supported by PsySuite, here we ought to share the app to further boost psychophysical research, aiming at setting it to a cheap, user-friendly, and portable level.

Contactless and Calibration-Free Blood Pressure and Pulse Rate Monitor for Screening and Monitoring of Hypertension: Cross-Sectional Validation Study.

The key to reducing the immense morbidity and mortality burdens of cardiovascular diseases is to help people keep their blood pressure (BP) at safe levels. This requires that more people with hypertension be identified, diagnosed, and given tools to lower their BP. BP monitors are critical to hypertension diagnosis and management. However, there are characteristics of conventional BP monitors (oscillometric cuff sphygmomanometers) that hinder rapid and effective hypertension diagnosis and management. Calibration-free, software-only BP monitors that operate on ubiquitous mobile devices can enable on-demand BP monitoring, overcoming the hardware barriers of conventional BP monitors. This study aims to investigate the accuracy of a contactless BP monitor software app for classifying the full range of clinically relevant BPs as hypertensive or nonhypertensive and to evaluate its accuracy for measuring the pulse rate (PR) and BP of people with BPs relevant to stage-1 hypertension. The software app, known commercially as Lifelight, was investigated following the data collection and data analysis methodology outlined in International Organization for Standardization (ISO) 81060-2:2018/AMD 1:2020 "Non-invasive Sphygmomanometers-Part 2: Clinical investigation of automated measurement type." This validation study was conducted by the independent laboratory Element Materials Technology Boulder (formerly Clinimark). The study generated data from 85 people aged 18-85 years with a wide-ranging distribution of BPs specified in ISO 81060-2:2018/AMD 1:2020. At least 20% were required to have Fitzpatrick scale skin tones of 5 or 6 (ie, dark skin tones). The accuracy of the app's BP measurements was assessed by comparing its BP measurements with measurements made by dual-observer manual auscultation using the same-arm sequential method specified in ISO 81060-2:2018/AMD 1:2020. The accuracy of the app's PR measurements was assessed by comparing its measurements with concurrent electroencephalography-derived heart rate values. The app measured PR with an accuracy root-mean-square of 1.3 beats per minute and mean absolute error of 1.1 (SD 0.8) beats per minute. The sensitivity and specificity with which it determined that BPs exceeded the in-clinic systolic threshold for hypertension diagnosis were 70.1% and 71.7%, respectively. These rates are consistent with those reported for conventional BP monitors in a literature review by The National Institute for Health and Care Excellence. The app's mean error for measuring BP in the range of normotension and stage-1 hypertension (ie, 65/85, 76% of participants) was 6.5 (SD 12.9) mm Hg for systolic BP and 0.4 (SD 10.6) mm Hg for diastolic BP. Mean absolute error was 11.3 (SD 10.0) mm Hg and 8.6 (SD 6.8) mm Hg, respectively. A calibration-free, software-only medical device was independently tested against ISO 81060-2:2018/AMD 1:2020. The safety and performance demonstrated in this study suggest that this technique could be a potential solution for rapid and scalable screening and management of hypertension.

Evaluation of an artificial intelligence-based decision support for the detection of cutaneous melanoma in primary care: a prospective real-life clinical trial.

Use of artificial intelligence (AI), or machine learning, to assess dermoscopic images of skin lesions to detect melanoma has, in several retrospective studies, shown high levels of diagnostic accuracy on par with - or even outperforming - experienced dermatologists. However, the enthusiasm around these algorithms has not yet been matched by prospective clinical trials performed in authentic clinical settings. In several European countries, including Sweden, the initial clinical assessment of suspected skin cancer is principally conducted in the primary healthcare setting by primary care physicians, with or without access to teledermoscopic support from dermatology clinics. To determine the diagnostic performance of an AI-based clinical decision support tool for cutaneous melanoma detection, operated by a smartphone application (app), when used prospectively by primary care physicians to assess skin lesions of concern due to some degree of melanoma suspicion. This prospective multicentre clinical trial was conducted at 36 primary care centres in Sweden. Physicians used the smartphone app on skin lesions of concern by photographing them dermoscopically, which resulted in a dichotomous decision support text regarding evidence for melanoma. Regardless of the app outcome, all lesions underwent standard diagnostic procedures (surgical excision or referral to a dermatologist). After investigations were complete, lesion diagnoses were collected from the patients' medical records and compared with the app's outcome and other lesion data. In total, 253 lesions of concern in 228 patients were included, of which 21 proved to be melanomas, with 11 thin invasive melanomas and 10 melanomas in situ. The app's accuracy in identifying melanomas was reflected in an area under the receiver operating characteristic (AUROC) curve of 0.960 [95% confidence interval (CI) 0.928-0.980], corresponding to a maximum sensitivity and specificity of 95.2% and 84.5%, respectively. For invasive melanomas alone, the AUROC was 0.988 (95% CI 0.965-0.997), corresponding to a maximum sensitivity and specificity of 100% and 92.6%, respectively. The clinical decision support tool evaluated in this investigation showed high diagnostic accuracy when used prospectively in primary care patients, which could add significant clinical value for primary care physicians assessing skin lesions for melanoma.

Risks and Requirements in Sustainable App Development—A Review

Apps are a part of the everyday lives of many people around the world. Digital applications support many areas, including the private sphere and the working world. To strengthen social sustainability in app development, it is necessary to consider users’ needs. The aim of this paper is to identify users’ requirements and to explore possible concerns. Furthermore, we aim to investigate how sustainability can be integrated into app development. For this purpose, 25 studies were analyzed, in which users were interviewed. The papers were qualitatively evaluated. Our results reveal three main findings. First, specific requirements, such as value for monetary aspects, security, and privacy, play a crucial role for users. Second, users expressed concerns about the attractiveness, motivation, and accuracy of apps. Third, an absolute research gap was revealed in the integration of sustainability in app development. Based on the findings, the Sustainable App Development Model (SADM) was then developed. This model incorporates the most fundamental aspects of social, economic, and environmental sustainability and is characterized by a circular approach to app development.

Strategies to increase downloads of COVID-19 exposure notification apps: A discrete choice experiment.

Exposure notification apps have been developed to assist in notifying individuals of recent exposures to SARS-CoV-2. However, in several countries, such apps have had limited uptake. We assessed whether strategies to increase downloads of exposure notification apps should emphasize improving the accuracy of the apps in recording contacts and exposures, strengthening privacy protections and/or offering financial incentives to potential users. In a discrete choice experiment with potential app users in the US, financial incentives were more than twice as important in decision-making about app downloads, than privacy protections, and app accuracy. The probability that a potential user would download an exposure notification app increased by 40% when offered a $100 reward to download (relative to a reference scenario in which the app is free). Financial incentives might help exposure notification apps reach uptake levels that improve the effectiveness of contact tracing programs and ultimately enhance efforts to control SARS-CoV-2. Rapid, pragmatic trials of financial incentives for app downloads in real-life settings are warranted.

Best Practice Guidance for Digital Contact Tracing Apps: A Cross-disciplinary Review of the Literature.

BackgroundDigital contact tracing apps have the potential to augment contact tracing systems and disrupt COVID-19 transmission by rapidly identifying secondary cases prior to the onset of infectiousness and linking them into a system of quarantine, testing, and health care worker case management. The international experience of digital contact tracing apps during the COVID-19 pandemic demonstrates how challenging their design and deployment are.ObjectiveThis study aims to derive and summarize best practice guidance for the design of the ideal digital contact tracing app.MethodsA collaborative cross-disciplinary approach was used to derive best practice guidance for designing the ideal digital contact tracing app. A search of the indexed and gray literature was conducted to identify articles describing or evaluating digital contact tracing apps. MEDLINE was searched using a combination of free-text terms and Medical Subject Headings search terms. Gray literature sources searched were the World Health Organization Institutional Repository for Information Sharing, the European Centre for Disease Prevention and Control publications library, and Google, including the websites of many health protection authorities. Articles that were acceptable for inclusion in this evidence synthesis were peer-reviewed publications, cohort studies, randomized trials, modeling studies, technical reports, white papers, and media reports related to digital contact tracing.ResultsEthical, user experience, privacy and data protection, technical, clinical and societal, and evaluation considerations were identified from the literature. The ideal digital contact tracing app should be voluntary and should be equitably available and accessible. User engagement could be enhanced by small financial incentives, enabling users to tailor aspects of the app to their particular needs and integrating digital contact tracing apps into the wider public health information campaign. Adherence to the principles of good data protection and privacy by design is important to convince target populations to download and use digital contact tracing apps. Bluetooth Low Energy is recommended for a digital contact tracing app's contact event detection, but combining it with ultrasound technology may improve a digital contact tracing app's accuracy. A decentralized privacy-preserving protocol should be followed to enable digital contact tracing app users to exchange and record temporary contact numbers during contact events. The ideal digital contact tracing app should define and risk-stratify contact events according to proximity, duration of contact, and the infectiousness of the case at the time of contact. Evaluating digital contact tracing apps requires data to quantify app downloads, use among COVID-19 cases, successful contact alert generation, contact alert receivers, contact alert receivers that adhere to quarantine and testing recommendations, and the number of contact alert receivers who subsequently are tested positive for COVID-19. The outcomes of digital contact tracing apps' evaluations should be openly reported to allow for the wider public to review the evaluation of the app.ConclusionsIn conclusion, key considerations and best practice guidance for the design of the ideal digital contact tracing app were derived from the literature.

Do prostate cancer-related mobile phone apps have a role in contemporary prostate cancer management? A systematic review by EAU young academic urologists (YAU) urotechnology group

Aims and objectivesTo review the available literature regarding the use of prostate cancer-related mobile phone applications (PCA).Materials and methodsThe search was for English language articles between inceptions of databases to June 2019. Medline, EMBASE, Cochrane Library, CINAHL and Web of Science were searched. Full-text articles were reviewed, and the following data were extracted to aid with app analysis: name of application, developer, platform (Apple App Store or Google Play Store) and factors assessed by the article.ResultsThe search yielded 1825 results of which 13 studies were included in the final review. 44 PCAs were identified from the data collected of which 59% of the PCAs had an educational focus. 11 apps were inactive and 5 weren’t updated within the last year. Five studies focused on the development and testing of apps (MyHealthAvatar, CPC, Rotterdam, Interaktor, NED). Two studies evaluated the readability of PCAs. Most PCAs had a reading level greater than that of the average patient. Two studies evaluated the quality and accuracy of apps. Majority of PCAs were accurate with a wide range of information. The study reported most PCAs to have deficient or insufficient scores for data protection. Two studies evaluated the accuracy of Rotterdam, CORAL and CPC risk calculators. Rotterdam was the best performer.ConclusionsPCAs are currently in its infancy and do require further development before widespread integration into existing clinical practise. There are concerns with data protection, high readability standards and lack of information update in current PCAs. If developed appropriately with responsible governance, they do have the potential to play important roles in modern-day prostate cancer management

Diabetes Digital App Technology: Benefits, Challenges, and Recommendations. A Consensus Report by the European Association for the Study of Diabetes (EASD) and the American Diabetes Association (ADA) Diabetes Technology Working Group

Digital health technology, especially digital and health applications ("apps"), have been developing rapidly to help people manage their diabetes. Numerous health-related apps provided on smartphones and other wireless devices are available to support people with diabetes who need to adopt either lifestyle interventions or medication adjustments in response to glucose-monitoring data. However, regulations and guidelines have not caught up with the burgeoning field to standardize how mobile health apps are reviewed and monitored for patient safety and clinical validity. The available evidence on the safety and effectiveness of mobile health apps, especially for diabetes, remains limited. The European Association for the Study of Diabetes (EASD) and the American Diabetes Association (ADA) have therefore conducted a joint review of the current landscape of available diabetes digital health technology (only stand-alone diabetes apps, as opposed to those that are integral to a regulated medical device, such as insulin pumps, continuous glucose monitoring systems, and automated insulin delivery systems) and practices of regulatory authorities and organizations. We found that, across the U.S. and Europe, mobile apps intended to manage health and wellness are largely unregulated unless they meet the definition of medical devices for therapeutic and/or diagnostic purposes. International organizations, including the International Medical Device Regulators Forum and the World Health Organization, have made strides in classifying different types of digital health technology and integrating digital health technology into the field of medical devices. As the diabetes digital health field continues to develop and become more fully integrated into everyday life, we wish to ensure that it is based on the best evidence for safety and efficacy. As a result, we bring to light several issues that the diabetes community, including regulatory authorities, policy makers, professional organizations, researchers, people with diabetes, and health care professionals, needs to address to ensure that diabetes health technology can meet its full potential. These issues range from inadequate evidence on app accuracy and clinical validity to lack of training provision, poor interoperability and standardization, and insufficient data security. We conclude with a series of recommended actions to resolve some of these shortcomings.

Diabetes digital app technology: benefits, challenges, and recommendations. A consensus report by the European Association for the Study of Diabetes (EASD) and the American Diabetes Association (ADA) Diabetes Technology Working Group.

Digital health technology, especially digital and health applications ('apps'), have been developing rapidly to help people manage their diabetes. Numerous health-related apps provided on smartphones and other wireless devices are available to support people with diabetes who need to adopt either lifestyle interventions or medication adjustments in response to glucose-monitoring data. However, regulations and guidelines have not caught up with the burgeoning field to standardise how mobile health apps are reviewed and monitored for patient safety and clinical validity. The available evidence on the safety and effectiveness of mobile health apps, especially for diabetes, remains limited. The European Association for the Study of Diabetes (EASD) and the American Diabetes Association (ADA) have therefore conducted a joint review of the current landscape of available diabetes digital health technology (only stand-alone diabetes apps, as opposed to those that are integral to a regulated medical device, such as insulin pumps, continuous glucose monitoring systems, and automated insulin delivery systems) and practices of regulatory authorities and organisations. We found that, across the USA and Europe, mobile apps intended to manage health and wellness are largely unregulated unless they meet the definition of medical devices for therapeutic and/or diagnostic purposes. International organisations, including the International Medical Device Regulators Forum and WHO, have made strides in classifying different types of digital health technology and integrating digital health technology into the field of medical devices. As the diabetes digital health field continues to develop and become more fully integrated into everyday life, we wish to ensure that it is based on the best evidence for safety and efficacy. As a result, we bring to light several issues that the diabetes community, including regulatory authorities, policymakers, professional organisations, researchers, people with diabetes and healthcare professionals, needs to address to ensure that diabetes health technology can meet its full potential. These issues range from inadequate evidence on app accuracy and clinical validity to lack of training provision, poor interoperability and standardisation, and insufficient data security. We conclude with a series of recommended actions to resolve some of these shortcomings.

Visualization of Superficial Cerebral Lesions Using a Smartphone Application.

Accurate localizations of cerebral lesions ensure good surgical outcomes and minimize intraoperative complications. Currently, microscope-integrated neuronavigation systems compensate these requirements. Since these units are expensive and large, the need to create more practical and portable systems in this "smart" century has become apparent. The See-Through Camera smartphone application (app) is a photography app that penetrates the existing background picture and merges this with a camera image on the smartphone display. This app was applied to neurosurgery for visualization of the projection of the tumors over head in 20 patients. App accuracy was tested according to neuronavigation system. Precision of the app was evaluated according to the superposition ratio of the tumor image drawn by the navigation system with the tumor picture on the smartphone display. This ratio was 40%-100% in 16 (80%) patients and below 40% as an unfavorable result in the rest of the patients. Basic rationality of this app can provide the basis of future pocket navigation systems in terms of its design and principles.

Development and Preliminary Usability and Accuracy Testing of the EBMT Gvhd App to Support Graft Versus Host Disease Diagnosis and Scoring According to NIH Criteria, By the EBMT Complications and Quality of Life Working Party

Introduction: Accurate diagnosis and severity scoring of acute and, in particular, chronic GVHD remains a challenge for clinical practice and for correct self-reporting of GVHD data to evaluate transplant outcomes. Perceived complexity and time investment issues limit the implementation of international standards and the recently updated NIH criteria for chronic GVHD. Here, we describe the development of the EBMT GVHD App, a computer/web-based algorithm-driven application to help physicians correctly diagnose and score severity of acute and chronic GVHD and improve the implementation of current standards in clinical practice and research.Methods: We applied a User Centered Design (UCD) process, through an iterative process between end-users and the development team to ensure that the App is user-friendly and efficient. A first EBMT GVHD App version (v0.0) tested an initial GVHD algorithm. An second improved prototype v1.0 was developed as a true web application (App) compatible with desktop computers and smartphones/tablets. V1.0 relies on two modules: a diagnostic module for NIH diagnostic, distinctive, common signs or proven evidence of GVHD (skin, nails, scalp/body hair, mouth, eyes, genitals, gastrointestinal tract, liver, lungs and muscles/joints), and a scoring moduleto assess severity of acute (Glucksberg and IBMTR criteria), chronic and overlap (NIH criteria) GVHD. The App v1.0 was tested by 28 hematology professionals (University Hospitals of Leuven, Belgium): 8 senior physicians, 8 junior physicians, 2 medical students and 10 data managers/research nurses; median experience in hematology 2.25 years, range 0-30, IQR 6.6, evenly distributed, by profession and seniority, to one of two groups (A and B).Usability of the App was determined for user experience and satisfaction. User experience was tested at baseline and end of study with the technology acceptance model (TAM), evaluating six Perceived Usefulness Statements rated on a 7-point Likert-like scale (1=extremely unlikely to 7=extremely likely). User satisfaction was evaluated by PSSUQ (Post-Study System Usability Questionnaire), which scores system usefulness, information quality and interface quality, on a 7-point Likert-like scale (1=strongly agree to 7=strongly disagree). App's accuracy relied on the proportion of correctly assessed clinical scenarios from 4 representative GVHD cases developed by a panel of GVHD experts as gold standard. In a quasi-experimental crossover design, professionals were invited to solve two cases either with standard paper tools or with the App, and later crossed over to use the other tool, both for the two other cases, as well as to solve again with the new tool their previous two cases. Comparisons were performed 'within groups' and 'between groups' (A vs B).Results: User experience and satisfaction were very good, with a median of 6 TAM points for user experience, and a median overall PSSUQ score of 2.2 for user satisfaction, 2.1 for System Use, 2.4 for Information Quality and 1.7 for Interface Quality. Users (70%) reported that they would be more likely to use the App on a desktop than on a mobile device.Accuracy results were only moderate with standard paper tools: 65% for diagnosis and 45% for scoring. The use of the App significantly increased diagnostic and scoring accuracy to 94% (+29%) and 88% (+43%), respectively (both p<0.001). The App also improved accuracy of individuals repeating the same clinical case (within groups) for diagnosis (+27%) and scoring (+42%), beyond a potential learning effect.From v1.0 results, an App v2.0 has been developed refining details in the algorithm, improving term description, adding a user's manual and the option of generating patient reports, which is now ready for further testing.Conclusions: The "EBMT GVHD App" is a first electronic tool to diagnose and score GVHD. Initial testing of v1.0 uniformly showed high scores for user experience and satisfaction, accurately reflected the subtle nuances of the NIH criteria, and improved significantly the accuracy of a diverse group of hematology professionals to diagnose and score severity of GVHD, compared to their practice with standard tools. Testing of v2.0 is underway to adapt layout and screen content and to address ambiguities of current guidelines. A larger study with a subsequent v3.0 is warranted in real life setting to evaluate macroscopic scalability. DisclosuresLee:Kadmon: Consultancy; Bristol-Myers Squibb: Consultancy.