Tento článek zpracovává téma nových trendů a technologií v urologii, a to konkrétně v oblasti telemedicíny a umělé inteligence. Nejprve stručně pojednává o přínosech telemedicíny a jak mění pohled na vztah mezi lékařem a pacientem. Podrobněji se pak text věnuje především umělé inteligenci, jež se v současnosti dostává do popředí zájmu laické i odborné veřejnosti. Její potenciál v urologii je testován v mnoha studiích, především se zaměřením na uroonkologii, v menší míře pak také v oblasti benigních urologických onemocnění. Článek se snaží identifikovat nejvýznamnější pokroky v této rychle se rozvíjející oblasti, a zároveň předkládá současné limity jejího zapojení do klinické praxe.
This article explores the emerging trends and technologies in urology, focusing on telemedicine and artificial intelligence. It provides a brief overview of the benefits of telemedicine and its impact on the patient-physician interactions. The article subsequently explores in detail the use of artificial intelligence, which is currently gaining considerable interest from both general public and medical professionals. Its potential in urology has been tested in a number of clinical studies, particularly in the field of uro-oncology and, to a lesser extent, in benign urological diseases. The aim of this article is to identify the key advances in this rapidly evolving field, while also highlighting the current limitations of its implementation into clinical practice.
- MeSH
- Deep Learning MeSH
- Humans MeSH
- Robotic Surgical Procedures MeSH
- Machine Learning MeSH
- Telemedicine MeSH
- Artificial Intelligence MeSH
- Urologic Neoplasms diagnosis therapy MeSH
- Urology * trends MeSH
- Check Tag
- Humans MeSH
- Publication type
- Review MeSH
Introduction: Socioeconomic disparities continue to hinder full access to e-learning and digital health resources, making both educational and health equity elusive. This study addresses the research question: How do socioeconomic factors influence the accessibility and effectiveness of e-learning and consequently health literacy in underserved communities? Methods: A systematic meta-synthesis of qualitative studies was conducted following the process by Noblit and Hare. Studies focusing on health inequalities without a connection to educational access or e-learning were excluded to maintain coherence with the research aim. Results: The interpretation of the findings resulted in five themes: (1) digital divide, (2) technological and language barriers, (3) inadequate infrastructure, (4) economic constraints, (5) cultural and contextual adaptations. Conclusion: The digital divide, along with technological proficiency and language barriers, exacerbates educational disparities by limiting access to technology and supportive resources, particularly for non-English speaking populations. The findings underscore the need for a more holistic examination of accessibility beyond educational outcomes, incorporating health literacy dimensions as well.
- MeSH
- Digital Divide MeSH
- Communication Barriers MeSH
- Qualitative Research MeSH
- Humans MeSH
- Computer-Assisted Instruction MeSH
- Review Literature as Topic MeSH
- Social Justice MeSH
- Socioeconomic Factors * MeSH
- Socioeconomic Disparities in Health MeSH
- Educational Technology methods MeSH
- Health Literacy * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Research Support, Non-U.S. Gov't MeSH
INTRODUCTION: The provision of optimal care for older adults with complex chronic conditions (CCCs) poses significant challenges due to the interplay of multiple medical, pharmacological, functional and psychosocial factors. To address these challenges, the I-CARE4OLD project, funded by the EU-Horizon 2020 programme, developed an advanced clinical decision support tool-the iCARE tool-leveraging large longitudinal data from millions of home care and nursing home recipients across eight countries. The tool uses machine learning techniques applied to data from interRAI assessments, enriched with registry data, to predict health trajectories and evaluate pharmacological and non-pharmacological interventions. This study aims to pilot the iCARE tool and assess its feasibility, usability and impact on clinical decision-making among healthcare professionals. METHODS AND ANALYSIS: A minimum of 20 participants from each of the seven countries (Italy, Belgium, the Netherlands, Poland, Finland, Czechia and the USA) participated in the study. Participants were general practitioners, geriatricians and other medical specialists, nurses, physiotherapists and other healthcare providers involved in the care of older adults with CCC. The study design involved pre-surveys and post-surveys, tool testing with hypothetical patient cases and evaluations of predictions and treatment recommendations. Two pilot modalities-decision loop and non-decision loop-were implemented to assess the effect of the iCARE tool on clinical decisions. Descriptive statistics and bivariate and multivariate analysis will be conducted. All notes and text field data will be translated into English, and a thematic analysis will be performed. The pilot testing started in September 2024, and data collection ended in January 2025. At the time this protocol was submitted for publication, data collection was complete but data analysis had not yet begun. ETHICS AND DISSEMINATION: Ethical approvals were granted in each participating country before the start of the pilot. All participants gave informed consent to participate in the study. The results of the study will be published in peer-reviewed journals and disseminated during national and international scientific and professional conferences and meetings. Stakeholders will also be informed via the project website and social media, and through targeted methods such as webinars, factsheets and (feedback) workshops. The I-CARE4OLD consortium will strive to publish as much as possible open access, including analytical scripts. Databases will not become publicly available, but the data sets used and/or analysed as part of the project can be made available on reasonable request and with the permission of the I-CARE4OLD consortium.
- MeSH
- Chronic Disease therapy MeSH
- Clinical Decision-Making * methods MeSH
- Humans MeSH
- Pilot Projects MeSH
- Prognosis MeSH
- Aged MeSH
- Machine Learning * MeSH
- Decision Support Systems, Clinical * MeSH
- Check Tag
- Humans MeSH
- Aged MeSH
- Publication type
- Journal Article MeSH
Cystathionine β-synthase (CBS) deficiency (classical homocystinuria) has a wide range of severity. Mildly affected patients typically present as adults with thromboembolism and respond to treatment with pyridoxine. Severely affected patients usually present during childhood with learning difficulties, ectopia lentis and skeletal abnormalities; they are pyridoxine non-responders (NR) or partial responders (PR) and require treatment with a low-methionine diet and/or betaine. The European network and registry for Homocystinurias and methylation Defects (E-HOD) has published management guidelines for CBS deficiency and recommended keeping plasma total homocysteine (tHcy) concentrations below 100 μmol/L. We have now analysed data from 311 patients in the registry to see how closely treatment follows the guidelines. Pyridoxine-responsive patients generally achieved tHcy concentrations below 50 μmol/L, but many NRs and PRs had a mean tHcy considerably above 100 μmol/L. Most NRs were managed with betaine and a special diet. This usually involved severe protein restriction and a methionine-free amino acid mixture, but some patients had a natural protein intake substantially above the WHO safe minimum. Work is needed on the methionine content of dietary protein as estimates vary widely. Contrary to the guidelines, most NRs were on pyridoxine, sometimes at dangerously high doses. tHcy concentrations were similar in groups prescribed high or low betaine doses and natural protein intakes. High tHcy levels were probably often due to poor compliance. Comparing time-to-event graphs for NR patients detected by newborn screening and those ascertained clinically showed that treatment could prevent thromboembolism (risk ratio 0.073) and lens dislocation (risk ratio 0.069).
- MeSH
- Betaine * therapeutic use MeSH
- Cystathionine beta-Synthase deficiency MeSH
- Child MeSH
- Adult MeSH
- Homocysteine * blood metabolism MeSH
- Homocystinuria * drug therapy MeSH
- Infant MeSH
- Humans MeSH
- Methionine * deficiency MeSH
- Adolescent MeSH
- Young Adult MeSH
- Infant, Newborn MeSH
- Child, Preschool MeSH
- Pyridoxine * therapeutic use MeSH
- Registries * MeSH
- Treatment Outcome MeSH
- Check Tag
- Child MeSH
- Adult MeSH
- Infant MeSH
- Humans MeSH
- Adolescent MeSH
- Young Adult MeSH
- Male MeSH
- Infant, Newborn MeSH
- Child, Preschool MeSH
- Female MeSH
- Publication type
- Journal Article MeSH
Využití digitální patologie a umělé inteligence v anatomické patologii představuje revoluční krok směrem k modernizaci diagnostických procesů. Digitalizace, postavená zejména na využívání tzv. whole slide imaging, umožňuje vytvářet celoplošné digitální obrazy histologických preparátů, což přináší potenciální benefity v oblasti přesnosti a dostupnosti diagnostiky. Na rozdíl od tradiční mikroskopie poskytuje digitální patologie též možnost telemedicíny a vzdálené konzultace, čímž otevírá nové možnosti spolupráce a sdílení odborných znalostí na národní i mezinárodní úrovni. Implementace digitálního pracovního postupu nicméně vyžaduje rozsáhlé investice do skenerů, softwarových platforem, vysokokapacitních úložišť a IT infrastruktury. Navzdory nemalým nákladům na implementaci však přináší řadu výhod, včetně časových úspor, možnosti centralizace diagnostiky a snížení nákladů na transport vzorků. Tento příspěvek se zaměřuje na praktické aspekty implementace digitální patologie v patologických laboratořích s důrazem na přínosy, rizika a technologické požadavky spojené s digitalizací a diskutuje i zásadní role validace a verifikace celého nového pracovního procesu. Článek představuje digitální patologii jako dynamicky se rozvíjející obor s vysokým potenciálem pro personalizovanou medicínu, zlepšení diagnostické přesnosti a podporu vzdálené spolupráce, čímž reaguje na rostoucí nároky moderní medicíny.
The application of digital pathology and artificial intelligence in anatomical pathology represents a revolutionary step towards the modernization of diagnostic processes. Digitalization, primarily based on creation and subsequent use of whole slide imaging, enables generating of full digital images of histological slides, offering potential benefits in diagnostic accuracy and accessibility. Unlike traditional microscopy, digital pathology also facilitates telemedicine and remote consultation, opening new possibilities for collaboration and sharing of expertise at both national and international levels. However, implementing a digital workflow requires substantial investments in scanners, software platforms, high-capacity storage, and IT infrastructure. Despite considerable costs of implementation, it brings numerous advantages, including time savings, opportunities for centralized diagnostics, and a reduction in sample transport costs. This paper focuses on the practical aspects of implementing digital pathology in pathology laboratories, emphasizing the benefits, risks, and technological requirements associated with digitalized workflows. It also discusses crucial roles of validation and verification, which are essential for ensuring a diagnostic accuracy of digital images compared to conventional microscopy. The article presents digital pathology as a dynamically evolving field with high potential for personalized medicine, improved diagnostic accuracy, and support for remote collaboration, addressing the growing demands of modern medicine.
- MeSH
- Humans MeSH
- Pathology * trends MeSH
- Machine Learning * trends MeSH
- Telemedicine trends MeSH
- Check Tag
- Humans MeSH
BACKGROUND: Cardiac rehabilitation is a beneficial multidisciplinary treatment of exercise promotion, patient education, risk factor management, and psychosocial counseling for people with coronary heart disease (CHD) that is underutilized due to substantial disparities in access, referral, and participation. Empirical studies suggest that cardiac telerehabilitation (CTR) have safety and efficacy comparable to traditional in-person cardiac rehabilitation, however, older adults are under-reported with effectiveness, feasibility, and usability remains unclear. METHODS: The study randomized 43 older adults (84 % males) to the 12-week CTR intervention or standard of care. Guided by Social Cognitive Theory, participants received individualized in-person assessment and e-coaching sessions, followed by CTR usage at home. Data were collected at baseline (T0), six-week (T1), and 12-week (T2). RESULTS: Participants in the CTR intervention group showed significant improvement in daily steps (T1: β = 4126.58, p = 0.001; T2: β = 5285, p = 0.01) and health-promoting lifestyle profile (T1: β = 23.26, p < 0.001; T2: β = 12.18, p = 0.008) across study endpoints. Twenty participants completed the intervention, with 40 % used the website for data-uploading or experiential learning, 90 % used the pedometer for tele-monitoring. Improving awareness of rehabilitation and an action focus were considered key facilitators while physical discomforts and difficulties in using the technology were described as the main barriers. CONCLUSIONS: The CTR is feasible, safe and effective in improving physical activity and healthy behaviors in older adults with CHD. Considering the variation in individual cardiovascular risk factors, full-scale RCT with a larger sample is needed to determine the effect of CTR on psychological symptoms, body weight and blood pressure, and quality of life.
- Publication type
- Journal Article MeSH
Telemedicine is an emerging development in the healthcare domain, where the Internet of Things (IoT) fiber optics technology assists telemedicine applications to improve overall digital healthcare performances for society. Telemedicine applications are bowel disease monitoring based on fiber optics laser endoscopy, gastrointestinal disease fiber optics lights, remote doctor-patient communication, and remote surgeries. However, many existing systems are not effective and their approaches based on deep reinforcement learning have not obtained optimal results. This paper presents the fiber optics IoT healthcare system based on deep reinforcement learning combinatorial constraint scheduling for hybrid telemedicine applications. In the proposed system, we propose the adaptive security deep q-learning network (ASDQN) algorithm methodology to execute all telemedicine applications under their given quality of services (deadline, latency, security, and resources) constraints. For the problem solution, we have exploited different fiber optics endoscopy datasets with images, video, and numeric data for telemedicine applications. The objective is to minimize the overall latency of telemedicine applications (e.g., local, communication, and edge nodes) and maximize the overall rewards during offloading and scheduling on different nodes. The simulation results show that ASDQN outperforms all telemedicine applications with their QoS and objectives compared to existing state action reward state (SARSA) and deep q-learning network (DQN) policy during execution and scheduling on different nodes.
- MeSH
- Algorithms MeSH
- Deep Learning * MeSH
- Internet of Things * MeSH
- Humans MeSH
- Fiber Optic Technology MeSH
- Telemedicine * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
BACKGROUND: Online learning has the potential to increase accessibility to high quality and cost-effective resources in prevention of risk behaviors. The aim of this pilot study was to assess the experience of university students with the comprehensive online course on prevention. METHODS: In this pilot study, an online questionnaire was administered to 51 Czech and 31 Ukrainian university students who completed the online Introduction to Evidence-based Prevention (INEP) full semester course between February 2022 and February 2023. Students were asked about their experience with INEP represented by 17 distinct features. Data were analyzed by descriptive statistics and mean comparisons tests. RESULTS: Students reported high overall satisfaction with INEP and with its respected features. The Structure and the Relevance features of INEP have been especially appreciated, while the Quizzes feature was perceived as only average by most students. INEP seemed to encourage most students (82%) to take other e-learning courses. CONCLUSION: The online INEP course received favorable feedback from university students across two distinct settings. INEP holds potential for broader integration into university study programs. These findings add to the ongoing discourse regarding enhancements in the education of future prevention professionals, making them relevant to practitioners, policymakers, and university-level decision-makers.
