Development of Learning Media Based on Mobil Learning Application

Abstract: Development of mobile learning application-based media. Objectives: The purpose of this study is to develop a more dynamic learning media by utilizing technology in the form of applications on smartphone devices to improve student learning outcomes. Methods: This study uses research and development methods with data collection techniques in the form of questionnaires, feasibility tests and effectiveness of mobile learning applications developed as learning media through stages designed according to the ADDHIE model. Findings: Product development in the form of mobile learning applications is feasible as a learning medium and effective for improving student learning outcomes with an increase in the average value of 28 students from 57.32 to 81.43. Conclusion: Mobile learning application is a good choice as a learning media.Keywords: Learning media, mobile learning application, research and developmentAbstrak: Pengembangan media pembelajaran berbasis aplikasi mobile learning. Tujuan: Penelitian ini bertujuan untuk mengembangkan media pembelajaran yang lebih dinamis dengan memanfaatkan teknologi dalam bentuk aplikasi pada perangkat smartphone untuk meningkatkan hasil belajar siswa. Metode: penelitian ini menggunakan metode penelitian dan pengembangan dengan teknik pengumpulan data dalam bentuk kuesioner, tes kelayakan dan efektivitasi aplikasi pembelajaran mobile yang dikembangkan sebagai media pembelajaran melalui tahapan yang dirancang sesuai dengan model ADDHIE. Temuan: Pengembangan produk dalam bentuk aplikasi pembelajaran mobile layak sebagai media pembelajaran dan efektif untuk meningkatkan hasil belajar siswa dengan peningkatan nilai rata-rata 28 siswa dari 57,32 menjadi 81,43. Kesimpulan: Aplikasi pembelajar mobile merupakan pilihan yang baik sebagai media pembelajaran.Kata kunci: Media pembelajaran, aplikasi pembelajaran mobile, penelitian pengembangan DOI: http://dx.doi.org/10.23960/jpp.v9.i1.20190

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Recent Advances in Machine Learning Applied to Ultrasound Imaging

Machine learning (ML) methods are pervading an increasing number of fields of application because of their capacity to effectively solve a wide variety of challenging problems. The employment of ML techniques in ultrasound imaging applications started several years ago but the scientific interest in this issue has increased exponentially in the last few years. The present work reviews the most recent (2019 onwards) implementations of machine learning techniques for two of the most popular ultrasound imaging fields, medical diagnostics and non-destructive evaluation. The former, which covers the major part of the review, was analyzed by classifying studies according to the human organ investigated and the methodology (e.g., detection, segmentation, and/or classification) adopted, while for the latter, some solutions to the detection/classification of material defects or particular patterns are reported. Finally, the main merits of machine learning that emerged from the study analysis are summarized and discussed. © 2022 by the authors. Licensee MDPI, Basel, Switzerland

Integration of cardiovascular risk assessment with COVID-19 using artificial intelligence

Artificial Intelligence (AI), in general, refers to the machines (or computers) that mimic "cognitive" functions that we associate with our mind, such as "learning" and "solving problem". New biomarkers derived from medical imaging are being discovered and are then fused with non-imaging biomarkers (such as office, laboratory, physiological, genetic, epidemiological, and clinical-based biomarkers) in a big data framework, to develop AI systems. These systems can support risk prediction and monitoring. This perspective narrative shows the powerful methods of AI for tracking cardiovascular risks. We conclude that AI could potentially become an integral part of the COVID-19 disease management system. Countries, large and small, should join hands with the WHO in building biobanks for scientists around the world to build AI-based platforms for tracking the cardiovascular risk assessment during COVID-19 times and long-term follow-up of the survivors

Decision support system for cardiovascular problems

The two main lines of medical research in this project are vascular anatomy (large vessels around the heart, coronaries and peripheral arteries) and heart chambers. Geometric models will be constructed to aid clinical diagnosis or multiphysical modelling and simulation. Two levels of complexity will be considered. For heart modelling, the first level will concentrate on models of the left and right ventricular cavities for robust and efficient extraction of simple clinical indexes of geometry, volume, mass, and wall kinetics. The second level will aim at more complex, fourchambered models, which will be important in developing comprehensive solid and fluid models to assist the design of medical devices

Decision support system for cardiovascular problems

The DISHEART project aims at developing a new computer based decision support system (DSS) integrating medical image data, modelling, simulation, computational Grid technologies and artificial intelligence methods for assisting clinical diagnosis and intervention in cardiovascular problems. The RTD goal is to improve and link existing state of the art technologies in order to build a computerised cardiovascular model for the analysis of the heart and blood vessels. The resulting DISHEART DSS interfaces computational biomechanical analysis tools with the information coming from multimodal medical images. The computational model is coupled to an artificial neural network (ANN) based decision model that can be educated for each particular patient with data coming from his/her images and/or analyses. The DISHEART DSS system is validated in trials of clinical diagnosis, surgical intervention and subject-specific design of medical devices in the cardiovascular domain. The DISHEART DSS also contributes to a better understanding of cardiovascular morphology and function as inferred from routine imaging examinations. Four reputable medical centers in Europe took an active role in the validation and dissemination of the DISHEART DSS as well as the elaboration of computational material and medical images. The integrated DISHEART DSS supports health professionals in taking promptly the best possible decision for prevention, diagnosis and treatment. Emphasis was put in the development of userfriendly, fast and reliable tools and interfaces providing access to heterogeneous health information sources, as well as on new methods for decision support and risk analysis. The use of Grid computing technology is essential in order to optimise and distribute the heavy computational work required for physical modelling and numerical simulations and especially for the parametric analysis required for educating the DSS for every particular application. The four end user SMEs participating in the project benefits from the new DISHEART DSS. The companies COMPASS, QUANTECH and Heartcore will market the DSS among public and private organizations related to the cardiovascular field. EndoArt will exploit the DISHEART DSS as a support for enhanced design and production of clinical devices. The partnership was sought in order to gather the maximum complementary of skills for the successful development of the project Disheart DSS, requiring experts in Mechanical sciences, Medical sciences, Informatic, and FEM technique to grow up the testes.Postprint (published version

The Effect of a Community-Based Multi-Lifestyle Intervention on Cardiovascular Health in Rural Populations: A Community-Based Participatory Research Approach

Background: The overall rate of cardiovascular diseases (CVD) mortality has decreased in the US over the last few years. However, in rural areas, this reduction in CVD mortality is less substantial compared to urban areas despite the effort to translate lifestyle intervention which was successful in urban areas. An effective approach to translating a lifestyle intervention into a rural setting would be to consider their rural characteristics, resources, and to engage the local community directly. This may be accomplished by using community-based participatory research (CBPR), which is an approach that involves equitable partnerships between researchers and community stakeholders.;Aims: Aim 1: Use CBPR principles to conduct a pilot study to test the feasibility and acceptability of a rural multicomponent lifestyle intervention in CVD patients and their partners. We will determine feasibility by examining study participant recruitment and retention, implementation fidelity, and acceptability by assessing provider and patient satisfaction. Aim 2: Examine the effect of CBPR-developed lifestyle intervention on CV health and other traditional CVD risk factors in rural populations. We hypothesize the pulse wave velocity and endothelial function will be enhanced with improved Framingham risk score and 6-min walk distance after 10 weeks of intervention.;Methods: We assessed the feasibility and acceptability using satisfaction questionnaires, retention and recruitment rate along with cardiovascular parameters i.e., Framingham risk score, 6-min walk test, reactive hyperemia index, carotid thickness, and pulse wave velocity, pre and post 10 week lifestyle intervention.;Results: 10 patients with CVD were screened with their family member, yielding 20 individuals screened (recruitment rate 59%). 17 patients were enrolled and retained (retention rate 100%). The 6-minute walk distance significantly (p=0.01) increased (466+/-17m to 503+/-17m) with a tendency of improved heart rates and diastolic function represented by sub-endocardial viability ratio after 10 weeks intervention, although these parameters did not quite reach statistical significance. In contrast, there were no significant changes in the other CVD parameters. These findings provide valuable evidence to conduct lifestyle intervention for CVD patients in rural areas.;Conclusion: This study suggests that the multi-lifestyle intervention using the principle of CBPR is feasible and acceptable to improve CV health in rural CVD populations. Because this study was conducted as a pilot study which consists of smaller populations and shorter periods than the full intervention, we expect that phase 2 will show more significant outcomes which determine if an educational lifestyle intervention is beneficial to reduce the incidence of CVD in rural areas. These findings will also provide an important evidence to implement a larger trial targeted at CVD patients and their family member

An analysis of the immune and vascular systems in untreated hypertension

Background: Blood pressure regulation leads to hypertension through complex environmental and genetic interactions, mediated by cardiac, vascular, endocrine, and renal systems. The immune system interacts with all of these, and may have a role in hypertension and associated organ damage. Methods and Results: The Inflammatension study comprehensively assessed vascular function (endothelial function, arterial stiffness, intima-media thickness, and cardiovascular variability), the immune cell ‘signature’ (including B and T cell subsets, monocyte and dendritic cells, and intracellular stimulation studies), and circulating protein biomarkers, in an untreated hypertensive group compared to normotensive controls, and in consideration of phenotypic groups, as follows. Does cardiovascular function differ between incident hypertension versus healthy controls? Hypertensive disease progression involves early arterial stiffness. Carotid atherosclerosis and impairment in endothelial function were not detected. Measures of arterial stiffness strongly correlate with each other, with ambulatory and central BP, and with cardiovascular variability. Are phenotypic subgroups apparent in hypertension? White coat hypertension patients demonstrated arterial stiffening in excess of sustained hypertension; masked hypertension patients vascular characteristics were akin to normotension. Machine learning techniques generated three phenotypic groups of hypertension, ‘arterially stiffened’, ‘vaso-protected’, and ‘non-dipper’. Identifying immune cell ‘signature’ in patients: Flow cytometry demonstrated lower CD4+ naïve cells (CD45RA+CCR7+CD45RO+CD62L+) in hypertension. CD4+ T central memory cells were expanded in hypertension, along with CD62- T effector memory cells in an adjusted model. Hypertensive group had proportionally fewer CD28+ lymphocytes and CD8+ TEMRA cells, and T cells polarised towards Th1/Tc1 and Th17.1/Tc17.1. Intermediate monocytes demonstrated a differing pattern of CCR2 and CCR5 chemokine receptor expression, and alterations in STAT1 and STAT6 phosphorylation cascades. Increased NK cell CD56+Dim expression and reduced NKT and T lymphocytes CD122 expression was linked to hypertension. Nocturnal non-dipping was associated with similar immune cell signature changes as hypertension, and dendritic cell mannose receptor downregulation in addition. The circulating protein biomarker ‘signature’ of untreated hypertension and hypertensive phenotypes: Cytokines and chemokines dominated the 34 biomarkers differing between normotension and hypertension, though failed to meet Bonferroni-adjusted thresholds. Inflammatory biomarkers correlated with BP and arterial stiffness, but not endothelial function. Associations were concordant across systolic and diastolic BP; TPP1, CCL7, CCL11, and CCL21 positively correlating; IL18R1, and KYNU negatively. These relationships were more pronounced in the hypertensive subgroup, especially CD molecules and cytokines. HGF, AGE, and CCL21 showed greatest between-group differences and correlations across arterial parameters. Systolic nocturnal dipping demonstrated negative correlation with immune cell interaction and cellular adhesion biomarkers (CTRC, EPHA1, LGALS4, SIT1, SMOC, IL-18 and TNFSF11). Sixteen of the 85 correlating biomarkers also differed between the ‘arterially stiffened’, ‘vaso-protected’, and ‘non-dipper’ phenotypic groups. Conclusions: In untreated hypertension arterial stiffness is already detectable, and along with nocturnal dipping and estimates of central BP, categorise hypertensive phenotypes. The exploratory data support alterations of circulating immune biomarkers, and innate (monocytes) and adaptive (T cells) immune compartments. Nocturnal dipping and hypertension phenotypes especially demonstrate immune system variances