Considerations for the Adoption of Digital Algorithms and Cardiovascular Decision-Support Tools in Clinical Practice.

Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide, necessitating early detection and effective management to reduce morbidity and mortality. Telemedicine has emerged as an innovative approach to address healthcare delivery challenges, particularly in underserved areas. This systematic review evaluates the role of telemedicine in the Early detection of cardiovascular diseases. A systematic search of PubMed, Cochrane Library, Embase, and Web of Science databases was conducted for studies published between 2014 and 2024. Eligible studies included randomized controlled trials, observational studies, and meta-analyses that evaluated telemedicine interventions in CVD detection or management. Study quality was assessed using the Cochrane Risk of Bias tool and the Newcastle-Ottawa Scale. Fifteen studies covered diverse telemedicine approaches such as remote monitoring, wearable devices, and mobile applications. Key findings included: Early Detection: Telemedicine improved diagnostic efficiency, with wearable devices identifying arrhythmias (sensitivity 95%) and telemonitoring accelerating hypertension diagnoses by 25%. Telemedicine is a valuable tool for the early detection and management of CVDs, significantly improving clinical outcomes. Despite its benefits, challenges such as the digital divide, privacy concerns, and provider training must be addressed. Future studies should explore the cost-effectiveness, scalability, and long-term outcomes of telemedicine in cardiovascular care.

The need for effective early detection and optimal therapy monitoring of cardiovascular diseases as the leading cause of death has led to an adaptation of the guidelines with a focus on cardiac computed tomography (CCTA) in patients with a low to intermediate risk of coronary heart disease (CHD). In particular, the introduction of photon-counting computed tomography (PCCT) in CT diagnostics promises significant advances through higher temporal and spatial resolution, and also enables advanced texture analysis, known as radiomics analysis. Originally developed in oncological imaging, radiomics analysis is increasingly being used in cardiac imaging and research. The aim is to generate imaging biomarkers that improve the early detection of cardiovascular diseases and therapy monitoring.The present study summarizes the current developments in cardiac CT texture analysis with a particular focus on evaluations of PCCT data sets in different regions, including the myocardium, coronary plaques, and pericoronary/epicardial fat tissue.These developments could revolutionize the diagnosis and treatment of cardiovascular diseases and significantly improve patient prognoses worldwide. The aim of this review article is to shed light on the current state of radiomics research in cardiovascular imaging and to identify opportunities for establishing it in clinical routine in the future. · Radiomics: Enables deeper, objective analysis of cardiovascular structures via feature quantification.. · PCCT: Provides a higher quality image, improving stability and reproducibility in cardiac CT.. · Early detection: PCCT and radiomics enhance cardiovascular disease detection and management.. · Challenges: Technical and standardization issues hinder widespread clinical application.. · Future: Advancing PCCT technologies could soon integrate radiomics in routine practice.. · Ayx I, Bauer R, Schönberg SO et al. Cardiac Radiomics Analyses in Times of Photon-counting Computed Tomography for Personalized Risk Stratification in the Present and in the Future. Rofo 2025; DOI 10.1055/a-2499-3122.

Wearable technology has rapidly evolved, becoming an integral tool in healthcare, particularly for cardiovascular health monitoring and disease prevention. This systematic review evaluates wearable device advancements, applications, and limitations, emphasizing their role in real-time health tracking, early detection of cardiovascular diseases, and lifestyle interventions. The study reviewed 112 peer-reviewed articles, applying the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure a rigorous and transparent approach. The findings highlight the integration of advanced sensors, such as photoplethysmography (PPG) and electrocardiography (ECG), and artificial intelligence-powered algorithms that enable enhanced diagnostic accuracy and predictive capabilities. Wearable devices have proven effective in remote patient monitoring, reducing hospital readmissions, and promoting proactive health management by empowering users to adopt and sustain healthier behaviors. However, challenges persist in ensuring device accuracy, particularly across diverse populations, with issues such as motion artifacts, signal noise, and variability in sensor performance noted. Furthermore, the review identifies a critical gap in the inclusion of underrepresented populations, such as older adults, individuals with darker skin tones, and those from low-resource settings, which limits the generalizability of findings. Despite these challenges, the study concludes that wearable technology has immense potential to revolutionize healthcare by enhancing accessibility, patient engagement, and preventive care. Addressing the identified gaps will be crucial for maximizing wearable health technologies' equitable adoption and impact.

Introduction: Artificial intelligence (AI) has emerged as a transformative tool in medicine, with significant applications in the cardiovascular field, especially in the early detection of cardiovascular diseases (CVDs). Its ability to process large volumes of clinical data through technologies such as machine learning (ML) and deep neural networks (DL) has provided faster, more accurate, and personalized diagnoses. Objectives: To analyze how AI contributes to the early detection of CVDs and, as specific objectives, to identify the main algorithms used, describe the benefits for patients and health professionals, and discuss the ethical and technical challenges associated with their implementation. Justification: The research highlights the social importance of reducing mortality associated with CVDs, the academic relevance of consolidating knowledge about AI in cardiology, and the scientific impact of exploring new diagnostic approaches to improve health care. Methodology: An integrative literature review was carried out, covering articles published between 2018 and 2023, in databases such as PubMed, Scopus, and Embase. Inclusion criteria prioritized peer-reviewed studies that discussed the application of AI in the diagnosis and management of CVDs. Results and discussion: The results showed that AI improves diagnostic accuracy, reduces response time, and enables personalized prognoses. However, challenges such as data privacy, algorithmic bias, and acceptance by professionals were identified as barriers to its implementation. Conclusion: AI is a promising tool in cardiovascular clinical practice, but its consolidation depends on strategies that address ethical and technical limitations and promote adequate training of health professionals.

The Role, Potential Benefits and Cost-effectiveness of Digital Tools for Diabetes Management.

IntroductionDigital medical devices (e.g., continuous glucose monitors), healthcare apps, and electronic health record tools like patient portals have significantly altered patient self-management and treatment, particularly for patients with chronic conditions.1–3...

Effective healthcare currently incorporates a patient-centric system and accessible technology for patient self-management. This study aimed to develop and validate a novel questionnaire titled the Digital Tool Use Questionnaire for Diabetes (DTUQ-D) - a screening tool identifying the type, number, and frequency of digital tools used by Type 2 Diabetes Mellitus (T2DM) patients with within HMOs, online, and via applications. The questionnaire was administered to two ethnic groups and both genders. A mixed-methods approach was used. In the qualitative phase, the questionnaire was developed through phone surveys of 29 T2DM patients, two endocrinologists and two technology experts. In the quantitative phase, involving 367 participants, convergent validity, construct validity, and reliability were examined. Findings indicated that the DTUQ-D is valid and reliable, successfully identifying digital tools utilized by T2DM patients, notwithstanding variations in factor structures between ethnic groups. This questionnaire provides a foundation for future research, offering a standardized approach to evaluating digital tool usage. The study enhances understanding of the role of digital tools in healthcare, especially for T2DM self-management. It also can be easily adapted to assess digital tool use for other illnesses by adjusting instructions and the wording of certain items.

ObjectiveThis study synthesises the effectiveness and applicability of trigger tools for detecting adverse events (AEs) across various healthcare settings.DesignThis study used an umbrella review to consolidate findings from existing systematic reviews, assess the quality of evidence and identify gaps in current knowledge.MethodsA comprehensive search was conducted across SCOPUS, Web of Science and PubMed, and included systematic reviews and meta-analyses from 2009 to 2024 focusing on trigger tools used in healthcare settings. Data extraction and quality appraisal followed Joanna Briggs Institute guidelines. Narrative synthesis was employed owing to the heterogeneity among studies. Nine systematic reviews were included.ResultsNine systematic reviews were analysed. Results showed substantial variation in trigger tool performance. Detection rates ranged from 0.8% to 66% across healthcare settings and populations. Preventability rates varied widely from 7% to 94.4%. Automated detection methods demonstrated inconsistent results with AE prevalence, ranging from 0.1% to 29.2%. The studies within the reviews exhibited significant variability in the types of trigger tools analysed and the methodologies used, affecting detection outcomes. The quality of the included studies varied, with inconsistent definitions of AEs and differences in study design limiting the generalisability of the findings.ConclusionsTrigger tools vary significantly in effectiveness, influenced by healthcare context and tool design. Automated methods need refinement. Standardised methodologies and context-specific tools are essential to improve patient safety. Future research should focus on these areas to enhance the accuracy and applicability of trigger tools in different healthcare settings.PROSPERO registration numberCRD42024581456.

BackgroundThere is a notable a gap between promising research findings and implementation of digital health tools. Understanding and addressing barriers to use is key to widespread implementation.MethodsA survey was administered to a self-selecting sample in-person (n = 157) or online (n = 58), with questions examining: i) ownership and usage rates of digital devices among people with psychosis; ii) interest in using technology to engage with mental health services; and iii) facilitators of and barriers to using digital tools in a mental healthcare context.ResultsDevice ownership: Virtually all participants owned a mobile phone (95%) or smartphone (90%), with Android phones slightly more prevalent than iPhones. Only a minority owned a fitness tracker (15%) or smartwatch (13%). Device ownership was significantly lower in unemployed people and those without secondary education. Device cost and paranoid ideation were barriers to ownership.Technology and mental health services: Most participants (88%) said they would willingly try a mental health app. Symptom monitoring apps were most popular, then appointment reminders and medication reminders. Half the sample would prefer an app alongside face-to-face support; the other half preferred remote support or no other mental health support.Facilitators: Participants thought using a mental health app could increase their understanding of psychosis generally, and of their own symptoms. They valued the flexibility of digital tools in enabling access to support anywhere, anytime.Barriers: Prominent barriers to using mental health apps were forgetting, lack of motivation, security concerns, and concerns it would replace face-to-face care. Overall participants reported no substantial effects of technology on their mental health, although a quarter said using a phone worsened paranoid ideation. A third used technology more when psychotic symptoms were higher, whereas a third used it less. Around half used technology more when experiencing low mood.ConclusionsOur findings suggest rapidly increasing device ownership among people with psychosis, mirroring patterns in the general population. Smartphones appear appropriate for delivering internet-enabled support for psychosis. However, for a sub-group of people with psychosis, the sometimes complex interaction between technology and mental health may act as a barrier to engagement, alongside more prosaic factors such as forgetting.

Introduction: This article, developed between 2022 and 2023 within the framework of Applied Stochastic Processes by the SciBas group at the Universidad Distrital Francisco José de Caldas, focuses on the role of Hidden Markov Models (HMM) in predicting cardiovascular diseases. Problem: The addressed issue is the need to enhance the early detection of heart diseases, emphasizing how HMM can address uncertainty in clinical data and detect complex patterns. Objective: To evaluate the use of Hidden Markov Models (HMM) in the analysis of electrocardiograms (ECG) for the early detection of cardiovascular diseases. Methodology: The methodology comprises a literature review concerning the relationship between HMM and cardiovascular diseases, followed by the application of HMM to prevent heart attacks and address uncertainty in clinical data. Results: The findings indicate that HMM is effective in preventing heart diseases, yet its effectiveness is contingent upon data quality. These results are promising but not universally applicable. Conclusions: In summary, this study underscores the utility of HMM in early infarction detection and its statistical approach in medicine. It is emphasized that HMM is not infallible and should be complemented with other clinical options and assessment methods in real-world situations. Originality: This work stands out for its statistical and probabilistic approach in the application of Hidden Markov Models (HMM) in medical analysis, offering an innovative perspective and enhancing the understanding of their utility in the field of medicine. Limitations: It is recognized that there are limitations, such as dependence on data quality and variable applicability in clinical cases. These limitations should be considered in the context of their implementation in medical practice.

Home healthcare in the Italian health system has proven to be an essential factor in adequately responding to the health needs of an increasingly aging population. The opportunities offered by digitization and new technologies, such as artificial intelligence (AI) and robotics, are a lever for making home care services more effective and efficient on the one hand, and on the other for improving remote patient monitoring. Telemedicine devices have enormous potential for telemonitoring and telerehabilitation of patients suffering from chronic disabling diseases; in particular, AI systems can now provide very useful managerial and decision-making support in numerous clinical areas. AI combined with digitalization, could also allow for the remote monitoring of patients' health conditions. In this paper authors describe some digital and healthcare tools or system of AI, such as the Connected Care model, the Home Care Premium (HCP) project, The Resilia App and some professional service robotics. In this context, to optimize potential and concrete healthcare improvements, some limits need to be overcome: gaps in health information systems and digital tools at all levels of the Italian National Health Service, the slow dissemination of the computerized medical record, issues of digital literacy, the high cost of devices, the poor protection of data privacy. The danger of over-reliance on such systems should also be examined. Therefore the legal systems of the various countries, including Italy, should indicate clear decision-making paths for the patient.

Over the last decade, learning and working in medicine has been increasingly influenced by digital tools. Today’s medical students are growing up in a digital age where digital tools and devices are a regular part of professional life. The development of digital competence is crucial for healthcare education, as healthcare professionals will be the ones helping orientate patients in digital healthcare systems and tools. This study aims to explore the opinions and experiences of medical college students regarding the factors influencing digital competence. Four focus group discussions were organised, with 26 medical college students in total in various study programmes in December 2021. The interviews were conducted in Latvian and transcribed by the lead researcher. Thematic analysis of the focus group discussions identified four main themes to be associated with the factors influencing digital competence: personal factors (self-confidence in using Information and Communication Technologies (ICTs), attitudes towards ICTs, prior training on ICTs, motivation), learning design (online, blended learning), lecturers’ digital competence and external aspects (family support, Covid-19 pandemic). This study presented results that could also be helpful for other universities to promote targeted development and improvement of digital competence of their students.

Atherosclerosis is a condition indicated by loss of arterial elasticity because of thickening of the arterial wall due to plaques that cause cardiovascular disease. Increased arterial stiffness is one sign of atherosclerosis, so measurement of arterial stiffness can be used as an early detection of cardiovascular disease. One method of measuring arterial stiffness is by analyzing the second derivative of a photoplethysmography (PPG) signal taken from a non-invasive fingertip. PPG signals are obtained from infra red signal transmission to detect changes in blood volume in microvascular vessels. By calculating the delay between direct waves and reflection waves from PPG signals and also from taking the subject's height can be obtained arterial stiffness index. PPG data collection was carried out on 30 subjects, with 10 subjects normal and 10 subjects were cardiac catheterization patients and 10 subjects were athlete. The stiffness index value for cardiac catheterization patients increased compared to athlete and normal subjects. Increased arterial stiffness is one sign of atherosclerosis, so measurement of arterial stiffness can be used as an early detection of cardiovascular disease.

Early detection of cardiovascular disease would allow timely institution of preventive measures. Arterial endothelium play a primary role in processes leading to the development of atherosclerotic plaque and cardiovascular disease in general. Determination of flow-mediated dilatation (FMD) of brachial arteries from B-mode ultrasound image sequences offers a noninvasive surrogate index of endothelial function. A highly automated method for analysis of brachial ultrasound image sequences is reported and its performance assessed. The method overcomes the variability of brachial ultrasound images across subjects by incorporating machine learning and quality control steps. The automated method outperformed conventional manual analysis by providing a decreased analysis bias, increased reproducibility, and improved measurement accuracy. Consequently, it decreases inter- and intraobserver as well interinstitution variability. The method has been employed in a number of population studies with thousands of subjects analyzed.

Cardiovascular disease (CVD) is the leading cause of death in Asia-Pacific, although highly preventable, CVD was the cause of an estimated 9.4 million deaths. It accounted for one-third of all deaths in 2016. Hypertension is one of cardiovascular disease risk factors. Early detection of cardiovascular disease is the main issues to reduce the prevalence of the cardiovascular disease. Adolescence was the population in which the modifiable cardiovascular risk factor firstly developed. In the year 2018, Indonesian Health Ministry discovered that the prevalence of the cardiovascular disease in East Java was 0.2 % higher than national prevalence and Malang as one of the biggest cities in East Java with high population growth, hence early detection of cardiovascular disease become highlight issue. This cross-sectional survey was held in September 2019 at SMPN 5 Malang, which participated by 123 students (75 females and 48 males). Bivariate analysis was done to examine the correlation between each variable, and logistic regression was done to the most influential variable. Seventy-eight per cent of students were sedentary lifestyle with physical activities < 300 minutes/week, 16.2 % of students were an active smoker, and all were male. Among 123 students, 0.8 % with no cardiovascular risk factor, 29.2 % had 1 risk factor, 30.8 % had 2 risk factors, 27.6 % had 3 risk factors, and 11.3 % had 4 risk factors. The most prevalent risk factors were sedentary life (80 %), increase waist circumference (50 %), passive smoker (48.5 %), overweight & obesity (15 %), and active smoker (14,6 %). Waist circumference above normal was more prevalent in female (65 %) than male (31%). Hypertension had been found in 14.6 % of students and had a significant correlation with heart rate (p 0.011) References Arnett, D. K., Blumenthal, R. S., Albert, M. A., Michos, E. D., Buroker, A. B., Miedema, M. D., ... & Himmelfarb, C. D. (2019). 2019 ACC/AHA