Search Results (174)

Gaseous microemboli (GME) represent a common complication of cardiac structural interventions across both surgical and transcatheter approaches. Intraoperative transesophageal echocardiography (TEE) represents a convenient methodology to monitor and visualize the presence of circulating GME. However, their detection and quantification are far from trivial due to operator-dependent view, high velocity, and objects with similar structure in the background. Here, we propose a feasibility study based on a 2.5D U-Net architecture to detect GME in space-time connected data. We applied and tested such an architecture on a pilot dataset of eight TEE recordings (60 fps, $600\times 800$ pixels) from eight different patients undergoing cardiac surgery, resulting in improved detection of moving GMEs against the background with respect to classical spot detection algorithms and 2D U-Net, yet retaining real-time execution speed with respect to more complex deep-learning architectures. Under leave-one-patient-out cross-validation, the selected model achieved strong detection performance under a three-pixel radius-tolerant grace-zone evaluation, with a precision of 92.55% and recall of 80.54%, corresponding to radius-tolerant Intersection over Union (IoU) and Dice coefficients of 73.95% and 84.13%, respectively. Complementarily, strict pixel-based segmentation metrics were also computed, yielding an IoU of 41.74% and a Dice coefficient of 57.98%. The selected model achieved an average inference time of $0.12 \mathrm{s}$ per batch on the tested hardware. To assess specificity on unseen data, we additionally evaluated the model on an external GME-negative TEE dataset, where it produced predominantly empty or near-empty masks, indicating a low rate of spurious detections. These results support the technical feasibility of real-time GME segmentation, although broader clinical validation on larger multi-patient, multicenter datasets is still required. Full article

Artificial intelligence (AI)-based digital twins (DTs) are emerging as a groundbreaking paradigm in rehabilitation and physiotherapy, enabling the creation of dynamic virtual representations of patients for continuous monitoring, prognostic assessment and personalised therapeutic interventions. This article presents a global, interdisciplinary review of AI-based DT technologies in rehabilitation settings utilising the Internet of Medical Things (IoMT), with particular emphasis on the integration of wearable and implantable sensor systems in next-generation wireless healthcare applications. The article analyses how multimodal wearable sensors, implantable devices and smart wireless communication networks can support the acquisition of real-time biomechanical and physiological data for adaptive rehabilitation. By combining perspectives from biomedical engineering, physiotherapy, computational intelligence and wireless healthcare systems, this article highlights the emerging opportunities and challenges associated with the creation of scalable digital twin ecosystems for precision rehabilitation. The proposed vision contributes to the development of smart, connected and personalized rehabilitation infrastructures, in line with future paradigms of healthcare and wireless communication. Full article

Large language models (LLMs) are expanding the capabilities of socially assistive robots (SARs) through natural dialogue, personalisation, multimodal reasoning, retained interaction context, and adaptive behaviour in healthcare. Integrating generative language models into robots, however, complicates evaluation because fluent output may exaggerate perceived competence and increase the risks of hallucination, overtrust, privacy exposure, relationship dependency, and unsafe reliance on advice or actions. This PRISMA-informed review synthesises healthcare robotics, human–robot interaction, LLM-enabled systems, ethics, implementation, and care delivery. Database searches returned 128 records, of which 110 were unique after deduplication. Supplementary retrieval and assessment yielded 85 substantive sources spanning background mapping, primary analysis, and governance. Studies focused mainly on feasibility, usability, acceptability, dialogue quality, and short-term engagement, whereas longitudinal safety, governance of retained interaction context, comparative effectiveness, workflow integration, and sustained healthcare value received limited attention. These gaps indicate that evaluation of LLM-enabled SARs must account for physical presence, social role, interaction memory, and potential actions rather than focus on conversational performance alone. The review therefore proposes HEART, a healthcare-specific evaluative architecture comprising Human-Centred Communication, Ethical and Trustworthy Deployment, Adaptive and Embodied Intelligence, Relationship Continuity, and Translational Healthcare Value. HEART uses boundary rules, operational indicators, qualitative labels, and non-additive deployment gates to separate evaluative domains, define assessable outcomes, summarise reported support, and prevent strengths in one area from masking critical safety or governance failures. Future research should validate HEART through longitudinal and comparative assessment of hallucination severity, language-to-action safety, long-term effects, equity, and post-deployment monitoring. Full article

Cardiovascular disease remains the leading global health challenge, claiming approximately 19.8 million lives annually. The convergence of wearable technology and artificial intelligence represents a transformative shift in cardiovascular healthcare, enabling continuous real-time monitoring beyond conventional clinical settings. This narrative review synthesises current evidence on integrating consumer-grade and medical-grade wearable devices with AI algorithms for continuous cardiovascular monitoring applications, with particular attention to real-world translational applicability and global health equity. This review examined the technological landscape of wearable cardiovascular monitoring devices, including smartwatches with photoplethysmography and electrocardiogram capabilities, continuous cardiac monitoring patches, and emerging biosensor technologies. Also, the review explored AI methodologies, particularly machine learning and deep learning architectures, employed in processing complex physiological data streams from these devices. Clinical applications demonstrate impressive capabilities: arrhythmia detection with sensitivity rates exceeding 98%, continuous blood pressure monitoring through cuffless technologies, heart failure decompensation prediction, and cardiovascular risk stratification. However, substantial challenges persist, including data quality assurance, algorithm interpretability, regulatory compliance, and seamless clinical workflow integration. Privacy concerns, health disparities in algorithm performance, and the need for robust validation across diverse populations remain critical considerations. AI-enhanced wearable systems hold considerable potential for shifting cardiovascular care from reactive treatment paradigms towards predictive, preventive, and precision medicine approaches. Future directions include edge computing architectures, federated learning approaches, personalised AI models, enhanced interoperability with electronic health records, and expansion to resource-limited settings, ultimately improving patient outcomes whilst reducing healthcare costs. Full article

Hybrid healthcare integrates digital and in-person services across care delivery and governance, becoming central to modern health systems. Driven by technological advances and the COVID-19 pandemic, it reshapes care pathways, workforce organisation, and system management. Hybrid models can improve outcomes while reducing staff and administrative burdens. This paper presents a consortium-based exploratory review to conceptualise hybrid healthcare and define its core domains. Using qualitative analysis and two AI-focused case studies, it identifies five interrelated domains: operations, care pathways, education, governance, and data-driven personalised care. Findings, including evidence from Czechia, offer a practical framework for analysing hybrid healthcare transformation. Full article

Cardiac arrhythmias complicate 20–50% of surgical procedures and contribute substantially to perioperative morbidity, mortality, and healthcare costs, with postoperative atrial fibrillation (POAF) being the most frequent form. Their genesis reflects the convergence of surgical stress, anaesthetic agents, autonomic imbalance, systemic inflammation, and electrolyte disturbances, explaining the limited efficacy of single-mechanism interventions. This narrative review synthesises contemporary evidence on pathophysiology, risk stratification, prevention, acute management, and emerging technologies, emphasising individualised, patient-tailored approaches. MEDLINE, Embase, and Cochrane CENTRAL were searched (January 2010–January 2026), prioritising randomised trials, meta-analyses, and guidelines. Contemporary risk stratification integrates clinical scores, biomarkers, and electrocardiographic parameters; machine-learning models show moderate discrimination (pooled AUC 0.84) and may enable more personalised prediction pending external validation. Evidence-based prophylaxis—beta-blockade, magnesium, selective amiodarone, and emerging anti-inflammatory strategies such as colchicine—reduces POAF in high-risk populations, while acute management is guided by haemodynamic status and individual risk. Anticoagulation follows CHA₂DS₂-VASc stratification, although optimal timing and duration remain undefined. Wearable monitoring, AI-based detection, and atrial-selective agents show clinical promise. Systematic, personalised integration of risk assessment, prophylaxis, monitoring, and management offers the clearest path to reducing arrhythmia-associated morbidity. Full article

The prescription and recommendation of prebiotics and probiotics by healthcare professionals remain an evolving area of research. Despite increasing recognition of their benefits in modulating the gut microbiome, healthcare professionals including dietitians, often lack standardised guidelines and sufficient training to integrate them into practice. Barriers to implementation include insufficient clinical evidence, cost, uncertainty regarding strain-specific efficacy, and a lack of consensus on appropriate therapeutic applications. Studies indicate that while many healthcare professionals acknowledge the potential benefits of these products, their hesitancy arises from insufficient supporting research evidence and associated education. Future efforts should concentrate on enhancing clinical guidelines, improving educational programs for healthcare professionals, and conducting large-scale trails to establish evidence-based recommendations. Additionally, integrating gut microbiome sequencing into clinical decision-making could improve personalised nutrition and optimise the prescription of prebiotics and probiotics. Further research is necessary to address knowledge gaps and promote effective, evidence-based use of these interventions by healthcare professionals. This review explores current prescribing practices, knowledge, and perceptions of healthcare professionals regarding prebiotics and probiotics with a particular focus on the evidence, education, and implementation gaps that may influence their clinical application. Full article

Background: Digital exclusion remains a key barrier to equitable access to digital health services, particularly among individuals with visual impairment. Limited access to devices and digital literacy restricts participation in increasingly digital-first healthcare systems. This study aimed to evaluate the feasibility and exploratory service impact of a device recycling and digital inclusion pilot at a tertiary ophthalmic hospital. Materials and Methods: The six-month pilot at Moorfields Eye Hospital involved the refurbishment and distribution of donated electronic devices (laptops and mobile phones) alongside personalised digital literacy training delivered by trained volunteers. Twenty-two patients with visual impairment were enrolled; 18 completed the programme. Pre- and post-intervention questionnaires assessed digital engagement and confidence across key domains. Paired data were analysed using the Wilcoxon signed-rank test. Results: Across 216 item-level engagement responses, the number of responses indicating daily engagement increased from 31 to 49. Mean self-reported confidence scores improved from 3.1 to 5.1 out of 10 (Wilcoxon signed-rank test, V = 148, p = 0.0008; r = 0.81). Patients reported increased use of email, messaging, online forms, and General Practice (GP) appointment systems. Using secondary lifecycle data and modelled estimates, the reuse of refurbished laptops was associated with an indicative saving of approximately 5.3 tonnes of CO₂-equivalent emissions. Conclusions: This service evaluation suggests that a multi-component intervention combining device provision with tailored support may improve digital engagement and confidence among patients with visual impairment. These findings support the feasibility of integrating digital inclusion initiatives within ophthalmology services, with potential co-benefits for environmental sustainability. Full article

Endometriosis is a chronic, debilitating condition affecting approximately 10–15% of reproductive-aged women and it is often associated with significant diagnostic delays due to its heterogeneity and unreliable non-invasive tests. Artificial intelligence (AI) offers innovative methods for improving endometriosis diagnosis, prognosis and research via advanced pattern recognition and data analysis capabilities. The integration of AI in diagnostic workflow has the potential to improve efficiency, accuracy, and patient outcomes. This review summarises current developments of AI—including machine learning, deep learning, and natural language processing—in the diagnostic workflow of endometriosis. It analyses different fields of diagnostics ranging from AI-assisted imaging in detection of pouch of Douglas to multi-omics biomarkers assisting the clinical decision process. AI can enhance accuracy, reducing diagnostic delays and supporting personalised treatment planning. However, there are multiple limitations, such as small datasets, overfitting, and lack of external validation and variability. Further research and evaluation are required before it can be implemented into healthcare systems. AI holds promise as a non-invasive, scalable adjunct to current diagnostics, potentially reducing the economic and personal burden endometriosis carries. Full article

Background: Telemedicine may advance endometriosis care, but few initiatives are integrated in outpatient follow-up. A novel telemedicine approach—tele-patient-reported outcome measures (telePROM)—includes an endometriosis-specific questionnaire and phone and video consultations combined with text messaging (chat) with a multidisciplinary endometriosis team. This study explores how healthcare professionals experience telePROM and its integration in clinical practice. Methods: A qualitative study guided by interpretive description methodology. Data were generated through observations and focus group interviews conducted between January 2023 and March 2024 at a referral centre for endometriosis within a university hospital. A purposive sample of ten healthcare professionals comprising physicians, nurses and a medical secretary participated in the focus group interviews. Inductive analysis was inspired by interpretive description and carried out through an iterative process involving four steps, leading to the development of final themes and interpretation. Results: Three themes were identified from analysis: (1) Balancing Personalised Care With Increased Clinical Complexity; (2) Changing Professional Boundaries in a Digitally Supported Care Model; and (3) System Friction and Flexibility when Integrating TelePROM. Conclusions: Telemedicine improved endometriosis care by supporting patient-initiated and personalised consultations. However, sustainable, effective, and safe integration of telemedicine appears to require clinical experience, interdisciplinary collaboration, and supervision. Text communication (chat) proved to be an important element to ensure collection of additional information to complement patient-reported outcomes and it is essential for patient triage; yet it is rarely described in the literature. Ensuring organisational resilience during the digital transformation of healthcare requires ongoing training of healthcare professionals’ communicative and digital competences and may necessitate restructured technical support, including designated telemedicine experts in clinical practice to eliminate technical disruptions. These initiatives may contribute to and support the future implementation of telemedicine in healthcare. Full article

Postpartum depression (PPD) affects a substantial proportion of women globally and is often underdiagnosed due to barriers in screening, stigma, and limited access to care. This study presents the design and pilot evaluation of an mHealth application (“HeartHabit”) intended to support user awareness, self-monitoring, and potential identification of symptoms of PPD among Greek-speaking mothers. An alpha version of the application was evaluated through an online survey with 30 women within the first postpartum year, using a walkthrough video. The evaluation focused on perceived usability and acceptability rather than clinical outcomes or real-world use. Usability and app quality were assessed via the System Usability Scale (SUS) and a qualitative version of the user Mobile Application Rating Scale (uMARS), respectively, adopting a mixed-methods approach. Demographics, and mood and stress screening data were also captured. Quantitative data were analysed via descriptive statistics and qualitative responses via Framework Analysis. The results indicated high perceived usability (mean SUS = 83.7/100). Qualitative findings highlighted the importance of practical usability, self-regulation tools, personalisation, and connectivity with healthcare professionals. Privacy, data transparency, and user control over personal data were perceived as critical for trust. The application was perceived as a potentially useful adjunct to formal care or as at-home support when access to services is limited. Larger, controlled trials, clinical implementation protocols and clinician training are needed to promote the app’s safe integration into formal care. This mixed-methods evaluation, incorporating usability assessment and patient involvement, may offer a useful paradigm for early-stage digital mental health intervention development. Full article

Background: Childhood vaccination remains the cornerstone of public health strategies, substantially reducing global morbidity and mortality, yet suboptimal uptake persists in many settings. In South Africa, the challenge is evident, with persistent outbreaks of vaccine-preventable diseases. Addressing localised immunisation shortfalls requires elucidating the complex interplay of factors beyond conventional access barriers. This qualitative study provides context-specific insights into the behavioural and social drivers influencing childhood vaccination uptake among caregivers in Cape Town, South Africa. Methods: Utilising an exploratory qualitative research design, thematic analysis was applied to interview data (n = 25 caregivers) collected via a purposive sampling strategy designed to capture maximum variation in experiences within targeted low-uptake subdistricts. Interpretation of the data was systematically guided by the World Health Organization’s Behavioural and Social Drivers (BeSD) framework. The latter consists of four domains, namely, “Thinking and Feeling”, “Social Processes”, “Motivation”, and “Practical Factors”. Findings: Analysis across BeSD domains reflected a pattern of the intention–behaviour gap, where caregivers are motivated for vaccination but face structural and practical barriers affecting timely uptake. In the Thinking and Feeling domain, widespread conviction regarding the vital benefits of vaccination co-existed with significant anxiety concerning minor side effects (e.g., pain and fever), which sometimes precipitated missed subsequent appointments. Caregivers frequently accept immunisation as a social routine despite having limited knowledge of the diseases it prevents. Social Processes demonstrated that while decision-making authority rested primarily with mothers, compliance relied on the delegation of logistical responsibilities to extended family members. Critically, reports of poor communication, judgment, or negative attitudes among healthcare workers undermined trust and acted as barriers to sustained engagement. Within the Practical Factors domain, structural constraints frequently overshadowed high intent, with pervasive issues such as long waiting times and financial costs cited as the main reasons for missed appointments. Conclusions: Participants generally expressed strong acceptance of vaccination, but attainment of optimal coverage is constrained by systemic failures in patient–provider communication and persistent logistical barriers within the public healthcare delivery system. Strategic public health interventions must therefore move beyond addressing only attitudinal opposition to prioritise targeted efforts that mitigate structural constraints and reinforce personalised, empathetic communication to sustain caregiver confidence and adherence. Full article

Additive Manufacturing (AM) and Wearable Technologies (WT) have rapidly evolved over the past decade. AM offers highly customisable fabrication, while WT enables minimally invasive health monitoring. The intersection of these fields presents emerging opportunities in biomedical and engineering domains. This study aims to map the scientific landscape of AM–WT research between 2015 and 2025 through a comprehensive bibliometric analysis. A total of 718 peer-reviewed publications were extracted from Web of Science (WoS), Scopus, and PubMed, following PRISMA-ScR guidelines. Using RStudio and the Bibliometrix package, analyses included co-authorship, citation trends, keyword co-occurrence, and thematic mapping. Custom author disambiguation scripts enhanced data quality and reliability. An annual publication growth of 24.89% was observed, with notable increases after 2020. Core themes included 3D printing, biosensors, microfluidics, and organ-on-a-chip devices. A shift from manufacturing-oriented research to biomedical integration is evident. Research output is dominated by the US, China, and South Korea, with moderate but not yet highly internationalised collaboration. The field of AM–WT research is undergoing a decisive transition from fabrication-focused studies to interdisciplinary, application-driven innovations. This shift is marked by increasing integration in healthcare and bioelectronics, yet hindered by regional imbalances and thematic gaps. Addressing these will be critical to advancing global impact. This study offers a cross-database bibliometric overview of AM–WT research. By combining three major data sources, it provides enhanced coverage and introduces novel analytical dimensions to guide future interdisciplinary efforts in personalised healthcare and wearable device innovation. Full article

Artificial intelligence (AI) has shown substantial potential to improve patient outcomes in parenteral nutrition by enabling individualised nutritional strategies, early prediction of metabolic and infectious complications, and optimised real-time clinical decision-making. Evidence from global clinical practice demonstrates that AI integration can enhance patient safety, reduce complication rates, and improve resource utilisation. In Bulgaria, recent developments in parenteral nutrition reflect progress toward standardisation, wider availability of modern formulations, and alignment with international clinical guidelines. However, the adoption of AI-driven systems for personalised nutrition planning and continuous risk assessment remains limited. Key barriers include the availability and quality of clinical data, regulatory and ethical considerations, and the need for targeted training of healthcare professionals. This review highlights both the opportunities and challenges associated with implementing AI in parenteral nutrition in the Bulgarian context. Potential benefits include improved patient outcomes, shorter hospital stays, more efficient healthcare delivery, and alignment with international best practices. At the same time, overcoming infrastructural, regulatory, and educational barriers is essential for successful implementation. Conclusions: The integration of AI into parenteral nutrition requires a multidisciplinary approach that combines clinical expertise, technological innovation, and supportive health policy. Such an approach offers the potential to sustainably enhance patient care in Bulgaria and position national practice in line with leading global standards. Full article

Walking (or gait) speed is recognised as a robust indicator of health status, functional capacity, and physiological reserve across the lifespan; however, its objective measurement remains underused in routine musculoskeletal physiotherapy practice. This commentary argues that gait speed is underutilised in musculoskeletal physiotherapy despite its strong prognostic and functional relevance, and proposes its cautious adoption as a functional vital sign to support more objective, standardised, and interpretable rehabilitation decision making. Evidence from an orthopaedic population undergoing total hip and knee arthroplasty illustrates the persistent gap between surgical success and functional recovery, as reflected in sustained deficits in walking speed relative to healthy benchmarks. Methodological issues in gait speed assessment are considered, and the potential future role of wearable sensors and digital health technologies in capturing real-world locomotor performance is highlighted. Overall, the evidence suggests that gait speed can provide an objective, low-cost, and scalable measure that integrates multiple domains of musculoskeletal function. Therefore, the routine integration of gait speed into physiotherapy assessment may help to quantify functional impairment, support personalised rehabilitation, reduce practice variation, and align musculoskeletal care with contemporary adaptive and digitally enabled healthcare models. Full article