Advanced Home-Based Shoulder Rehabilitation: A Systematic Review of Remote Monitoring Devices and Their Therapeutic Efficacy.

Shoulder pain represents the most frequently reported musculoskeletal disorder, often leading to significant functional impairment and pain, impacting quality of life. Home-based rehabilitation programs offer a more accessible and convenient solution for an effective shoulder disorder treatment, addressing logistical and financial constraints associated with traditional physiotherapy. The aim of this systematic review is to report the monitoring devices currently proposed and tested for shoulder rehabilitation in home settings. The research question was formulated using the PICO approach, and the PRISMA guidelines were applied to ensure a transparent methodology for the systematic review process. A comprehensive search of PubMed and Scopus was conducted, and the results were included from 2014 up to 2023. Three different tools (i.e., the Rob 2 version of the Cochrane risk-of-bias tool, the Joanna Briggs Institute (JBI) Critical Appraisal tool, and the ROBINS-I tool) were used to assess the risk of bias. Fifteen studies were included as they fulfilled the inclusion criteria. The results showed that wearable systems represent a promising solution as remote monitoring technologies, offering quantitative and clinically meaningful insights into the progress of individuals within a rehabilitation pathway. Recent trends indicate a growing use of low-cost, non-intrusive visual tracking devices, such as camera-based monitoring systems, within the domain of tele-rehabilitation. The integration of home-based monitoring devices alongside traditional rehabilitation methods is acquiring significant attention, offering broader access to high-quality care, and potentially reducing healthcare costs associated with in-person therapy.

GATEKEEPER's Strategy for the Multinational Large-Scale Piloting of an eHealth Platform: Tutorial on How to Identify Relevant Settings and Use Cases.

BACKGROUND: The World Health Organization's strategy toward healthy aging fosters person-centered integrated care sustained by eHealth systems. However, there is a need for standardized frameworks or platforms accommodating and interconnecting multiple of these systems while ensuring secure, relevant, fair, trust-based data sharing and use. The H2020 project GATEKEEPER aims to implement and test an open-source, European, standard-based, interoperable, and secure framework serving broad populations of aging citizens with heterogeneous health needs. OBJECTIVE: We aim to describe the rationale for the selection of an optimal group of settings for the multinational large-scale piloting of the GATEKEEPER platform. METHODS: The selection of implementation sites and reference use cases (RUCs) was based on the adoption of a double stratification pyramid reflecting the overall health of target populations and the intensity of proposed interventions; the identification of a principles guiding implementation site selection; and the elaboration of guidelines for RUC selection, ensuring clinical relevance and scientific excellence while covering the whole spectrum of citizen complexities and intervention intensities. RESULTS: Seven European countries were selected, covering Europe's geographical and socioeconomic heterogeneity: Cyprus, Germany, Greece, Italy, Poland, Spain, and the United Kingdom. These were complemented by the following 3 Asian pilots: Hong Kong, Singapore, and Taiwan. Implementation sites consisted of local ecosystems, including health care organizations and partners from industry, civil society, academia, and government, prioritizing the highly rated European Innovation Partnership on Active and Healthy Aging reference sites. RUCs covered the whole spectrum of chronic diseases, citizen complexities, and intervention intensities while privileging clinical relevance and scientific rigor. These included lifestyle-related early detection and interventions, using artificial intelligence-based digital coaches to promote healthy lifestyle and delay the onset or worsening of chronic diseases in healthy citizens; chronic obstructive pulmonary disease and heart failure decompensations management, proposing integrated care management based on advanced wearable monitoring and machine learning (ML) to predict decompensations; management of glycemic status in diabetes mellitus, based on beat to beat monitoring and short-term ML-based prediction of glycemic dynamics; treatment decision support systems for Parkinson disease, continuously monitoring motor and nonmotor complications to trigger enhanced treatment strategies; primary and secondary stroke prevention, using a coaching app and educational simulations with virtual and augmented reality; management of multimorbid older patients or patients with cancer, exploring novel chronic care models based on digital coaching, and advanced monitoring and ML; high blood pressure management, with ML-based predictions based on different intensities of monitoring through self-managed apps; and COVID-19 management, with integrated management tools limiting physical contact among actors. CONCLUSIONS: This paper provides a methodology for selecting adequate settings for the large-scale piloting of eHealth frameworks and exemplifies with the decisions taken in GATEKEEPER the current views of the WHO and European Commission while moving forward toward a European Data Space.

Predictors of adverse events after endoscopic ultrasound-guided through-the-needle biopsy of pancreatic cysts: a recursive partitioning analysis.

BACKGROUND AND STUDY AIMS : Endoscopic ultrasound-guided through-the-needle biopsy (TTNB) of pancreatic cystic lesions (PCLs) is associated with a non-negligible risk for adverse events (AEs). We aimed to identify the hierarchic interaction among independent predictors for TTNB-related AEs and to generate a prognostic model using recursive partitioning analysis (RPA). PATIENTS AND METHODS : Multicenter retrospective analysis of 506 patients with PCLs who underwent TTNB. RPA of predictors for AEs was performed and the model was validated by means of bootstrap resampling. RESULTS : Mean cysts size was 36.7 mm. Most common diagnoses were intraductal papillary mucinous neoplasm (IPMN, 45 %), serous cystadenoma (18.8 %), and mucinous cystadenoma (12.8 %). Fifty-eight (11.5 %) AEs were observed. At multivariate analysis, age (odds ratio [OR] 1.32, 1.09-2.14; p = 0.05), number of TTNB passes (OR from 2.17, 1.32-4.34 to OR 3.16, 2.03-6.34 with the increase of the number of passes), complete aspiration of the cyst (OR 0.56, 0.31-0.95; p = 0.02), and diagnosis of IPMN (OR 4.16, 2.27-7.69; p < 0.001) were found to be independent predictors of AEs, as confirmed by logistic regression and random forest analyses. RPA identified three risk classes: high-risk (IPMN sampled with multiple microforceps passes, 28 % AEs rate), low-risk (1.4 % AE rate, including patients < 64 years with other-than-IPMN diagnosis sampled with ≤ 2 microforceps passes and with complete aspiration of the cyst) and middle-risk class (6.1 % AEs rate, including the remaining patients). CONCLUSION : TTNB should be selectively used in the evaluation of patients with IPMN. The present model could be applied during patient selection as to optimize the benefit/risk of TTNB.

Exploring the misalignment on the value of further research between payers and manufacturers. A case study on a novel total artificial heart.

Payers and manufacturers can disagree on the appropriate level of evidence that is required for new medical devices, resulting in high societal costs due to decisions taken with sub-optimal information. A cost-effectiveness model of a hypothetical total artificial heart was built using data from the literature and the (simulated) results of a pivotal study. The expected value of perfect information (EVPI) was calculated from both the payer and manufacturer perspectives, using net monetary benefit and the company's return on investment respectively. A function was also defined, linking effectiveness to market shares. Additional constraints such as a minimum clinical difference or maximum budget impact were introduced into the company's decisions to simulate additional barriers to adoption. The difference in the EVPI between manufacturers and payers varied greatly depending on the underlying decision rules and constraints. The manufacturer's EVPI depends on the probability of being reimbursed, the uncertainty on the (cost-)effectiveness of the technology, as well as other parameters relating to initial investments, operating costs and market dynamics. The use of Value of information for both perspectives can outline potential misalignments and can be particularly useful to inform early dialogs between manufacturers and payers, or negotiations on conditional reimbursement schemes.

COVID-19 preparedness and social dynamics in a Sub-Saharan Africa country, Benin.

This project aims to assess and analyse the perception and impact of the COVID-19 pandemic in Benin. The applied research methodology was interdisciplinary and combined field studies that used ethnographic and social research methods with coding and data analysis, leading to theoretical dilemmas, which were analysed from the viewpoint of bioethical reflection. Furthermore, biomedical engineering approaches were used to assess the preparedness to COVID-19. Despite the preparedness to COVID-19 due to the promoted governmental measures, a peculiar management of the pandemic emerged. The latter, although noteworthy, did not overcome the typical challenges of medical locations in low-resource settings. This, together with the controversial spread of information and local beliefs, caused significant economic and social consequences, exceeding the benefits related to the containment of the virus. This research highlights how the emotion of fear, in this specific situation, was herald of dramatic consequences, rather than having a heuristic and empowering effect.

A framework for designing medical devices resilient to low-resource settings.

BACKGROUND: To date (April 2021), medical device (MD) design approaches have failed to consider the contexts where MDs can be operationalised. Although most of the global population lives and is treated in Low- and Middle-Income Countries (LMCIs), over 80% of the MD market share is in high-resource settings, which set de facto standards that cannot be taken for granted in lower resource settings. Using a MD designed for high-resource settings in LMICs may hinder its safe and efficient operationalisation. In the literature, many criteria for frameworks to support resilient MD design were presented. However, since the available criteria (as of 2021) are far from being consensual and comprehensive, the aim of this study is to raise awareness about such challenges and to scope experts' consensus regarding the essentiality of MD design criteria. RESULTS: This paper presents a novel application of Delphi study and Multiple Criteria Decision Analysis (MCDA) to develop a framework comprising 26 essential criteria, which were evaluated and chosen by international experts coming from different parts of the world. This framework was validated by analysing some MDs presented in the WHO Compendium of innovative health technologies for low-resource settings. CONCLUSIONS: This novel holistic framework takes into account some domains that are usually underestimated by MDs designers. For this reason, it can be used by experts designing MDs resilient to low-resource settings and it can also assist policymakers and non-governmental organisations in shaping the future of global healthcare.

Biomedical engineering and ethics: reflections on medical devices and PPE during the first wave of COVID-19.

In March 2019, the World Health Organization (WHO) declared that humanity was entering a global pandemic phase. This unforeseen situation caught everyone unprepared and had a major impact on several professional categories that found themselves facing important ethical dilemmas. The article revolves around the category of biomedical and clinical engineers, which were among those most involved in dealing with and finding solutions to the pandemic. In hindsight, the major issues brought to the attention of biomedical engineers have raised important ethical implications, such as the allocation of resources, the responsibilities of science and the inadequacy and non-universality of the norms and regulations on biomedical devices and personal protective equipment. These issues, analyzed one year after the first wave of the pandemic, come together in the appeal for responsibility for thought, action and, sometimes, even silence. This highlights the importance of interdisciplinarity and the definitive collapse of the Cartesian fragmentation of knowledge, calling for the creation of more fora, where this kind of discussions can be promoted.

Radiomic and Genomic Machine Learning Method Performance for Prostate Cancer Diagnosis: Systematic Literature Review.

BACKGROUND: Machine learning algorithms have been drawing attention at the joining of pathology and radiology in prostate cancer research. However, due to their algorithmic learning complexity and the variability of their architecture, there is an ongoing need to analyze their performance. OBJECTIVE: This study assesses the source of heterogeneity and the performance of machine learning applied to radiomic, genomic, and clinical biomarkers for the diagnosis of prostate cancer. One research focus of this study was on clearly identifying problems and issues related to the implementation of machine learning in clinical studies. METHODS: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol, 816 titles were identified from the PubMed, Scopus, and OvidSP databases. Studies that used machine learning to detect prostate cancer and provided performance measures were included in our analysis. The quality of the eligible studies was assessed using the QUADAS-2 (quality assessment of diagnostic accuracy studies-version 2) tool. The hierarchical multivariate model was applied to the pooled data in a meta-analysis. To investigate the heterogeneity among studies, I2 statistics were performed along with visual evaluation of coupled forest plots. Due to the internal heterogeneity among machine learning algorithms, subgroup analysis was carried out to investigate the diagnostic capability of machine learning systems in clinical practice. RESULTS: In the final analysis, 37 studies were included, of which 29 entered the meta-analysis pooling. The analysis of machine learning methods to detect prostate cancer reveals the limited usage of the methods and the lack of standards that hinder the implementation of machine learning in clinical applications. CONCLUSIONS: The performance of machine learning for diagnosis of prostate cancer was considered satisfactory for several studies investigating the multiparametric magnetic resonance imaging and urine biomarkers; however, given the limitations indicated in our study, further studies are warranted to extend the potential use of machine learning to clinical settings. Recommendations on the use of machine learning techniques were also provided to help researchers to design robust studies to facilitate evidence generation from the use of radiomic and genomic biomarkers.

Is robotic right colectomy economically sustainable? a multicentre retrospective comparative study and cost analysis.

BACKGROUND: Following the Food and Drug Administration approval, robot-assisted colorectal surgery has gained more acceptance among surgeons. One of the open issues about robotic surgery is the economic sustainability. The aim of our study is to evaluate the economic sustainability of robotic as compared to laparoscopic right colectomy for the Italian National Health System. METHODS: We performed a retrospective multicentre case-matched study including 94 patients for each group from four different Italian surgical departments. An economic evaluation gathered from a real-world data was performed to assess the sustainability of the robotic approach for right colectomy in the Italian National Health System. In particular, a differential cost analysis between the two procedures was performed. RESULTS: No statistical differences were found between the two groups for postoperative outcomes. After a careful review of the literature on the cost assessment for the operative room, medical devices and hospital stay according with our data, we estimated the followings: (a) the mean operative room cost for robotic group was 2179 ± 476 € vs. 1376 ± 322 € for laparoscopic group; (b) the mean hospital stay cost for robotic group was 3143 ± 1435 € vs. 3292 ± 1123 € for laparoscopic group; and (c) the mean cost for instruments was 6280 € for robotic group vs. 1504 € for laparoscopic group. The total mean cost of robotic right colectomy was 11,576 ± 1915 € vs. 6196 ± 1444 € for laparoscopic right colectomy. CONCLUSION: In conclusion, to date, robotic right colectomy with intracorporeal anastomosis does not provide any significant clinical advantages, which may justify the additional costs, as compared to its laparoscopic counterpart. Further evolution of robotic technology and experience may lead to a reduction of costs, especially if the robotic platform is used in an appropriate healthcare setting.

Endovascular Treatment versus Medical Therapy for Hypertensive Patients with Renal Artery Stenosis: An Updated Systematic Review.

The benefit of balloon angioplasty, with or without stenting, for the treatment of patients with renal artery stenosis remains controversial. A number of randomized controlled trials have attempted to determine its efficacy but the matter remains unclear. A 2014 Cochrane review, which combined data from 8 trials, showed homogeneity among the trials with no significant benefit shown. This systematic review replicates the same research methods and meta-analysis while expanding it to include papers between 2014 and 2018. One of the trials included in the previous review published results in the interim. Additionally, 2 ongoing trials identified in the 2014 review are yet to publish any result. Meta-analysis of the reports showed no heterogeneity between trials and no significant improvement shown by balloon angioplasty, with or without stenting, versus medical therapy. Further studies are recommended in order to assess the benefits of balloon angioplasty for patients with more severe renal artery stenosis.

HEALTH TECHNOLOGY ASSESSMENT METHODS GUIDELINES FOR MEDICAL DEVICES: HOW CAN WE ADDRESS THE GAPS? THE INTERNATIONAL FEDERATION OF MEDICAL AND BIOLOGICAL ENGINEERING PERSPECTIVE.

OBJECTIVES: Current health technology assessment (HTA) methods guidelines for medical devices may benefit from contributions by biomedical and clinical engineers. Our study aims to: (i) review and identify gaps in the current HTA guidelines on medical devices, (ii) propose recommendations to optimize the impact of HTA for medical devices, and (iii) reach a consensus among biomedical engineers on these recommendations. METHODS: A gray literature search of HTA agency Web sites for assessment methods guidelines on devices was conducted. The International Federation of Medical and Biological Engineers (IFMBE) then convened a structured focus group, with experts from different fields, to identify potential gaps in the current HTA guidelines, and to develop recommendations to fill these perceived gaps. The thirty recommendations generated from the focus group were circulated in a Delphi survey to eighty-five biomedical and clinical engineers. RESULTS: Thirty-two panelists, from seventeen countries, participated in the Delphi survey. The responses showed a strong agreement on twenty-seven of thirty recommendations. Some uncertainties remain about the methods to accurately assess the effectiveness and safety, and interoperability of a medical device with other devices or within the clinical setting. CONCLUSIONS: As medical devices differ from drug therapies, current HTA methods may not accurately reflect the conclusions of their assessment. Recommendations informed by the focus group discussions and Delphi survey responses aimed to address the perceived gaps, and to provide a more integrated approach in medical device assessments in combining engineering with other perspectives, such as clinical, economic, patient, human factors, ethical, and environmental.

On the use of approximate entropy and sample entropy with centre of pressure time-series.

BACKGROUND: Approximate entropy (ApEn) and sample entropy (SampEn) have been previously used to quantify the regularity in centre of pressure (COP) time-series in different experimental groups and/or conditions. ApEn and SampEn are very sensitive to their input parameters: m (subseries length), r (tolerance) and N (data length). Yet, the effects of changing those parameters have been scarcely investigated in the analysis of COP time-series. This study aimed to investigate the effects of changing parameters m, r and N on ApEn and SampEn values in COP time-series, as well as the ability of these entropy measures to discriminate between groups. METHODS: A public dataset of COP time-series was used. ApEn and SampEn were calculated for m = {2, 3, 4, 5}, r = {0.1, 0.15, 0.2, 0.25, 0.3, 0.35, 0.4, 0.45, 0.5} and N = {600, 1200} (30 and 60 s, respectively). Subjects were stratified in young adults (age < 60, n = 85), and older adults (age ≥ 60) with (n = 18) and without (n = 56) falls in the last year. The effects of changing parameters m, r and N on ApEn and SampEn were investigated with a three-way ANOVA. The ability of ApEn and SampEn to discriminate between groups was investigated with a mixed ANOVA (within-subject factors: m, r and N; between-subject factor: group). Specific combinations of m, r and N producing significant differences between groups were identified using the Tukey's honest significant difference procedure. RESULTS: A significant three-way interaction between m, r and N confirmed the sensitivity of ApEn and SampEn to the input parameters. SampEn showed a higher consistency and ability to discriminate between groups than ApEn. Significant differences between groups were mostly observed in longer (N = 1200) COP time-series in the anterior-posterior direction. Those differences were observed for specific combinations of m and r, highlighting the importance of an adequate selection of input parameters. CONCLUSIONS: Future studies should favour SampEn over ApEn and longer time-series (≥ 60 s) over shorter ones (e.g. 30 s). The use of parameter combinations such as SampEn (m = {4, 5}, r = {0.25, 0.3, 0.35}) is recommended.

Blood Pressure Drop Prediction by using HRV Measurements in Orthostatic Hypotension.

Orthostatic Hypotension is defined as a reduction of systolic and diastolic blood pressure within 3 minutes of standing, and may cause dizziness and loss of balance. Orthostatic Hypotension has been considered an important risk factor for falls since 1960. This paper presents a model to predict the systolic blood pressure drop due to orthostatic hypotension, relying on heart rate variability measurements extracted from 5 minute ECGs recorded before standing. This model was developed and validated with the leave-one-out cross-validation technique involving 10 healthy subjects, and finally tested with an additional 5 healthy subjects, whose data were not used during the training and cross-validation process. The results show that the model predicts correctly the systolic blood pressure drop in 80 % of all experiments, with an error rate below the measurement error of a sphygmomanometer digital device.

Analysis of Pharmaceutical Companies' Social Media Activity during the COVID-19 Pandemic and Its Impact on the Public.

The COVID-19 pandemic, a period of great turmoil, was coupled with the emergence of an "infodemic", a state when the public was bombarded with vast amounts of unverified information from dubious sources that led to a chaotic information landscape. The excessive flow of messages to citizens, combined with the justified fear and uncertainty imposed by the unknown virus, cast a shadow on the credibility of even well-intentioned sources and affected the emotional state of the public. Several studies highlighted the mental toll this environment took on citizens by analyzing their discourse on online social networks (OSNs). In this study, we focus on the activity of prominent pharmaceutical companies on Twitter, currently known as X, as well as the public's response during the COVID-19 pandemic. Communication between companies and users is examined and compared in two discrete channels, the COVID-19 and the non-COVID-19 channel, based on the content of the posts circulated in them in the period between March 2020 and September 2022, while the emotional profile of the content is outlined through a state-of-the-art emotion analysis model. Our findings indicate significantly increased activity in the COVID-19 channel compared to the non-COVID-19 channel while the predominant emotion in both channels is joy. However, the COVID-19 channel exhibited an upward trend in the circulation of fear by the public. The quotes and replies produced by the users, with a stark presence of negative charge and diffusion indicators, reveal the public's preference for promoting tweets conveying an emotional charge, such as fear, surprise, and joy. The findings of this research study can inform the development of communication strategies based on emotion-aware messages in future crises.

A 3D-printed condom intrauterine balloon tamponade: Design, prototyping, and technical validation.

Post-partum haemorrhage is among the main causes of (preventable) mortality for women in low-resource settings (LRSs), where, in 2017, the mortality ratio was 462 out of every 100 000 live births, over 10 times higher than for high-resource settings. There are different treatments available for post-partum haemorrhage. The intrauterine balloon tamponade is a medical device that proved to be a simple and cost-effective approach. Currently, there are several balloon tamponades available, with different design and working principles. However, all these devices were designed for high-resource settings, presenting several aspects that could be inappropriate for many lower-income countries. This paper presents the results of a preclinical study aiming at informing the design, prototyping and validation of a 3D-printed intrauterine balloon tamponade concept, contributing towards the United Nation's Sustainable Development Goal 3: Good health and Well-being. Frugal engineering concepts and contextualised design techniques were applied throughout, to define the design requirements and specifications. The performance of the final prototype was validated against the requirements of the UK National Health System (NHS) technical guidelines and relevant literature, measuring the water leak and pressure drop over time, both open air and in a approximate uterus model. The resulting prototype is made up of six components, some of which are easy to retrieve, namely a water bottle, a silicone tube and an ordinary condom, while others can be manufactured locally using 3D printers, namely a modified bottle cap, a flow stopper and a valve for holding the condom in place. Validation testing bore promising results with no water or pressure leak open air, and minimal leaks in the approximate uterus model. This demonstrates that the 3D printed condom-based intrauterine balloon tamponade is performing well against the requirements and, when compared to the state of the art, it could be a more appropriate and more resilient solution to low-resource settings, as it bypasses the challenges in the supply of consumables and presents a greener option based on circular economy.

Clearing the Fog: A Scoping Literature Review on the Ethical Issues Surrounding Artificial Intelligence-Based Medical Devices.

The use of AI in healthcare has sparked much debate among philosophers, ethicists, regulators and policymakers who raised concerns about the implications of such technologies. The presented scoping review captures the progression of the ethical and legal debate and the proposed ethical frameworks available concerning the use of AI-based medical technologies, capturing key themes across a wide range of medical contexts. The ethical dimensions are synthesised in order to produce a coherent ethical framework for AI-based medical technologies, highlighting how transparency, accountability, confidentiality, autonomy, trust and fairness are the top six recurrent ethical issues. The literature also highlighted how it is essential to increase ethical awareness through interdisciplinary research, such that researchers, AI developers and regulators have the necessary education/competence or networks and tools to ensure proper consideration of ethical matters in the conception and design of new AI technologies and their norms. Interdisciplinarity throughout research, regulation and implementation will help ensure AI-based medical devices are ethical, clinically effective and safe. Achieving these goals will facilitate successful translation of AI into healthcare systems, which currently is lagging behind other sectors, to ensure timely achievement of health benefits to patients and the public.

Are the variations in ECG morphology associated to different blood glucose levels? implications for non-invasive glucose monitoring for T1D paediatric patients.

AIMS: Recent clinical trials and real-world studies highlighted those variations in ECG waveforms and HRV recurrently occurred during hypoglycemic and hyperglycemic events in patients with diabetes. However, while several studies have been carried out for adult age, there is lack of evidence for paediatric patients. The main aim of the study is to identify the correlations of variations in ECG Morphology waveforms with blood glucose levels in a paediatric population. METHODS: T1D paediatric patients who use CGM were enrolled. They wear an additional non-invasive wearable device for recording physiological data and respiratory rate. Glucose metrics, ECG parameters and HRV features were collected, and Wilcoxon rank-sum test and Spearman's correlation analysis were used to explore if different levels of blood glucose were associated to ECG morphological changes. RESULTS: Results showed that hypoglycaemic events in paediatric patients with T1D are strongly associated with variations in ECG morphology and HRV. CONCLUSIONS: Results showed the opportunity of using the ECG as a non-invasive adding instrument to monitor the hypoglycaemic events through the integration of the ECG continuous information with CGM data. This innovative approach represents a promising step forward in diabetes management, offering a more comprehensive and effective means of detecting and responding to critical changes in glucose levels.

Towards a common European ethical and legal framework for conducting clinical research: the GATEKEEPER experience.

This paper examines the ethical and legal challenges encountered during the GATEKEEPER Project and how these challenges informed the development of a comprehensive framework for future Large-Scale Pilot (LSP) projects. GATEKEEPER is a LSP Project with 48 partners conducting 30 implementation studies across Europe with 50,000 target participants grouped into 9 Reference Use Cases. The project underscored the complexity of obtaining ethical approval across various jurisdictions with divergent regulations and procedures. Through a detailed analysis of the issues faced and the strategies employed to navigate these challenges, this study proposes an ethical and legal framework. This framework, derived from a comparative analysis of ethical application forms and regulations, aims to streamline the ethical approval process for future LSP research projects. By addressing the hurdles encountered in GATEKEEPER, the proposed framework offers a roadmap for more efficient and effective project management, ensuring smoother implementation of similar projects in the future.

The Use of Contact Tracing Technologies for Infection Prevention and Control Purposes in Nosocomial Settings: A Systematic Literature Review.

BACKGROUND: Pandemic management and preparedness are more needed than ever before and there is widespread governmental interest in learning from the COVID-19 pandemic in order to ensure the availability of evidence-based Infection Prevention and Control measures. Contact tracing is integral to Infection Prevention and Control, facilitating breaks in the chain of transmission in a targeted way, identifying individuals who have come into contact with an infected person, and providing them with instruction/advice relating to testing, medical advice and/or self-isolation. AIM: This study aims to improve our understanding of the use of contact tracing technologies in healthcare settings. This research seeks to contribute to the field of Infection Prevention and Control by investigating how these technologies can mitigate the spread of nosocomial infections. Ultimately, this study aims to improve the quality and safety of healthcare delivery. METHODS: A systematic literature review was conducted, and journal articles investigating the use of contact tracing technologies in healthcare settings were retrieved from databases held on the OvidSP platform between March and September 2022, with no date for a lower limit. RESULTS: In total, 277 studies were retrieved and screened, and 14 studies were finally included in the systematic literature review. Most studies investigated proximity sensing technologies, reporting promising results. However, studies were limited by small sample sizes and confounding factors, revealing contact tracing technologies remain at a nascent stage. Investment in research and development of new testing technologies is necessary to strengthen national and international contact tracing capabilities. CONCLUSION: This review aims to contribute to those who intend to create robust surveillance systems and implement infectious disease reporting protocols.

Risk score model to automatically detect prostate cancer patients by integrating diagnostic parameters.

Introduction: Prostate cancer (PCa) is one of the prevailing forms of cancer among men. At present, multiparametric MRI is the imaging method for localizing tumors and staging cancer. Radiomics plays a key role and hold potential for PCa detection, reducing the need for unnecessary biopsies, characterizing tumor aggression, and overseeing PCa recurrence post-treatment. Methods: Furthermore, the integration of radiomics data with clinical and histopathological data can further enhance the understanding and management of PCa and decrease unnecessary transfers to specialized care for expensive and intrusive biopsies. Therefore, the aim of this study is to develop a risk model score to automatically detect PCa patients by integrating non-invasive diagnostic parameters (radiomics and Prostate-Specific Antigen levels) along with patient's age. Results: The proposed approach was evaluated using a dataset of 189 PCa patients who underwent bi-parametric MRI from two centers. Elastic-Net Regularized Generalized Linear Model achieved 91% AUC to automatically detect PCa patients. The model risk score was also used to assess doubt cases of PCa at biopsy and then compared to bi-parametric PI-RADS v2. Discussion: This study explored the relative utility of a well-developed risk model by combining radiomics, Prostate-Specific Antigen levels and age for objective and accurate PCa risk stratification and supporting the process of making clinical decisions during follow up.