Leveraging natural language processing to aggregate field safety notices of medical devices across the EU.

The European Union (EU) Medical Device Regulation and In Vitro Medical Device Regulation have introduced more rigorous regulatory requirements for medical devices, including new rules for post-market surveillance. However, EU market vigilance is limited by the absence of harmonized reporting systems, languages and nomenclatures among Member States. Our aim was to develop a framework based on Natural Language Processing capable of automatically collecting publicly available Field Safety Notices (FSNs) reporting medical device problems by applying web scraping to EU authority websites, to attribute the most suitable device category based on the European Medical Device Nomenclature (EMDN), and to display processed FSNs in an aggregated way to allow multiple queries.

Safety notices and registry outlier data measure different aspects of safety and performance of total knee implants: a comparative study of safety notices and register outliers.

Background And Purpose:  Safety notices for medical devices such as total knee arthroplasty (TKA) implants may indicate problems in their design or performance that require corrective action to prevent patient harm. Safety notices are often published on national Ministries of Health or regulatory agencies websites. It is unknown whether problems triggering safety notices identify the same implants as those identified by registries as "outlier.

Standardized assessment of evidence supporting the adoption of mobile health solutions: A Clinical Consensus Statement of the ESC Regulatory Affairs Committee: Developed in collaboration with the European Heart Rhythm Association (EHRA), the Association of Cardiovascular Nursing & Allied Professions (ACNAP) of the ESC, the Heart Failure Association (HFA) of the ESC, the ESC Young Community, the ESC Working Group on e-Cardiology, the ESC Council for Cardiology Practice, the ESC Council of Cardio-Oncology, the ESC Council on Hypertension, the ESC Patient Forum, the ESC Digital Health Committee, and the European Association of Preventive Cardiology (EAPC).

Mobile health (mHealth) solutions have the potential to improve self-management and clinical care. For successful integration into routine clinical practice, healthcare professionals (HCPs) need accepted criteria helping the mHealth solutions' selection, while patients require transparency to trust their use. Information about their evidence, safety and security may be hard to obtain and consensus is lacking on the level of required evidence.

Analysis of the Sustainable Development Goal 3 index for Italian municipalities.

Objectives: Improving health at global and local scales is one of the 17 Sustainable Development Goals (SDGs) set by the United Nations (UN) for the period 2015-2030, specifically defined by SDG3, which includes 13 targets described by 28 indicators. In this context, the aim of the current study was to propose a protocol to infer SDG3 values at municipality level with the current openly available data.

Study Design: The study incorporated a quantitative research.

Deep learning approaches for the detection of scar presence from cine cardiac magnetic resonance adding derived parametric images.

This work proposes a convolutional neural network (CNN) that utilizes different combinations of parametric images computed from cine cardiac magnetic resonance (CMR) images, to classify each slice for possible myocardial scar tissue presence. The CNN performance comparison in respect to expert interpretation of CMR with late gadolinium enhancement (LGE) images, used as ground truth (GT), was conducted on 206 patients (158 scar, 48 control) from Centro Cardiologico Monzino (Milan, Italy) at both slice- and patient-levels. Left ventricle dynamic features were extracted in non-enhanced cine images using parametric images based on both Fourier and monogenic signal analyses.