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.

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.

Geospatial analysis of short-term exposure to air pollution and risk of cardiovascular diseases and mortality-A systematic review.

The cardiovascular risk associated with short-term ambient air pollution exposure is well-documented. However, recent advancements in geospatial techniques have provided new insights into this risk. This systematic review focuses on short-term exposure studies that applied advanced geospatial pollution modelling to estimate cardiovascular disease (CVD) risk and accounted for additional unconventional neighbourhood-level confounders to analyse their modifier effect on the risk.

Implementation of a GEOAI model to assess the impact of agricultural land on the spatial distribution of PM2.5 concentration.

Air pollution is considered one of the major environmental risks to health worldwide. Researchers are making significant efforts to study it, thanks to state-of-art technologies in data collection and processing, and to mitigate its effect. In this context, while a lot is known about the role of urbanization, industries, and transport, the impact of agricultural activities on the spatial distribution of pollution is less studied, despite knowledge about emissions suggest it is not a secondary factor.

Heatwave Definition and Impact on Cardiovascular Health: A Systematic Review.

We aimed to analyze recent literature on heat effects on cardiovascular morbidity and mortality, focusing on the adopted heat definitions and their eventual impact on the results of the analysis. The search was performed on PubMed, ScienceDirect, and Scopus databases: 54 articles, published between January 2018 and September 2022, were selected as relevant. In total, 21 different combinations of criteria were found for defining heat, 12 of which were based on air temperature, while the others combined it with other meteorological factors.

Detection of patients with COVID-19 by the emergency medical services in Lombardy through an operator-based interview and machine learning models.

Background: The regional emergency medical service (EMS) in Lombardy (Italy) developed clinical algorithms based on operator-based interviews to detect patients with COVID-19 and refer them to the most appropriate hospitals. Machine learning (ML)-based models using additional clinical and geospatial epidemiological data may improve the identification of infected patients and guide EMS in detecting COVID-19 cases before confirmation with SARS-CoV-2 reverse transcriptase PCR (rtPCR).

Methods: This was an observational, retrospective cohort study using data from October 2020 to July 2021 (training set) and October 2021 to December 2021 (validation set) from patients who underwent a SARS-CoV-2 rtPCR test within 7 days of an EMS call.

The digital journey: 25 years of digital development in electrophysiology from an Europace perspective.

Aims: Over the past 25 years there has been a substantial development in the field of digital electrophysiology (EP) and in parallel a substantial increase in publications on digital cardiology.In this celebratory paper, we provide an overview of the digital field by highlighting publications from the field focusing on the EP Europace journal.

Results: In this journey across the past quarter of a century we follow the development of digital tools commonly used in the clinic spanning from the initiation of digital clinics through the early days of telemonitoring, to wearables, mobile applications, and the use of fully virtual clinics.

A deep-learning approach for myocardial fibrosis detection in early contrast-enhanced cardiac CT images.

Aims: Diagnosis of myocardial fibrosis is commonly performed with late gadolinium contrast-enhanced (CE) cardiac magnetic resonance (CMR), which might be contraindicated or unavailable. Coronary computed tomography (CCT) is emerging as an alternative to CMR. We sought to evaluate whether a deep learning (DL) model could allow identification of myocardial fibrosis from routine early CE-CCT images.

Artificial intelligence in medical device software and high-risk medical devices - a review of definitions, expert recommendations and regulatory initiatives.

Introduction: Artificial intelligence (AI) encompasses a wide range of algorithms with risks when used to support decisions about diagnosis or treatment, so professional and regulatory bodies are recommending how they should be managed.

Areas Covered: AI systems may qualify as standalone medical device software (MDSW) or be embedded within a medical device. Within the European Union (EU) AI software must undergo a conformity assessment procedure to be approved as a medical device.

ESC Working Group on e-Cardiology Position Paper: accuracy and reliability of electrocardiogram monitoring in the detection of atrial fibrillation in cryptogenic stroke patients: In collaboration with the Council on Stroke, the European Heart Rhythm Association, and the Digital Health Committee.

The role of subclinical atrial fibrillation as a cause of cryptogenic stroke is unambiguously established. Long-term electrocardiogram (ECG) monitoring remains the sole method for determining its presence following a negative initial workup. This position paper of the European Society of Cardiology Working Group on e-Cardiology first presents the definition, epidemiology, and clinical impact of cryptogenic ischaemic stroke, as well as its aetiopathogenic association with occult atrial fibrillation.

Validation of CORE-MD PMS Support Tool: A Novel Strategy for Aggregating Information from Notices of Failures to Support Medical Devices' Post-Market Surveillance.

Introduction: The EU Medical Device Regulation 2017/745 defines new rules for the certification and post-market surveillance of medical devices (MD), including an additional review by Expert Panels of clinical evaluation data for high-risk MD if reports and alerts suggest possibly associated increased risks. Within the EU-funded CORE-MD project, our aim was to develop a tool to support such process in which web-accessible safety notices (SN) are automatically retrieved and aggregated based on their specific MD categories and the European Medical Device Nomenclature (EMDN) classification by applying an Entity Resolution (ER) approach to enrich data integrating different sources. The performance of such approach was tested through a pilot study on the Italian data.

A token-mixer architecture for CAD-RADS classification of coronary stenosis on multiplanar reconstruction CT images.

Background And Objective: In patients with suspected Coronary Artery Disease (CAD), the severity of stenosis needs to be assessed for precise clinical management. An automatic deep learning-based algorithm to classify coronary stenosis lesions according to the Coronary Artery Disease Reporting and Data System (CAD-RADS) in multiplanar reconstruction images acquired with Coronary Computed Tomography Angiography (CCTA) is proposed.

Methods: In this retrospective study, 288 patients with suspected CAD who underwent CCTA scans were included.

Cardiovascular magnetic resonance images with susceptibility artifacts: artificial intelligence with spatial-attention for ventricular volumes and mass assessment.

Background: Segmentation of cardiovascular magnetic resonance (CMR) images is an essential step for evaluating dimensional and functional ventricular parameters as ejection fraction (EF) but may be limited by artifacts, which represent the major challenge to automatically derive clinical information. The aim of this study is to investigate the accuracy of a deep learning (DL) approach for automatic segmentation of cardiac structures from CMR images characterized by magnetic susceptibility artifact in patient with cardiac implanted electronic devices (CIED).

Methods: In this retrospective study, 230 patients (100 with CIED) who underwent clinically indicated CMR were used to developed and test a DL model.

Cardiovascular deconditioning and impact of artificial gravity during 60-day head-down bed rest-Insights from 4D flow cardiac MRI.

Microgravity has deleterious effects on the cardiovascular system. We evaluated some parameters of blood flow and vascular stiffness during 60 days of simulated microgravity in head-down tilt (HDT) bed rest. We also tested the hypothesis that daily exposure to 30 min of artificial gravity (1 g) would mitigate these adaptations.

Smartwatch-Based Blood Pressure Measurement Demonstrates Insufficient Accuracy.

Background: Novel smartwatch-based cuffless blood pressure (BP) measuring devices are coming to market and receive FDA and CE labels. These devices are often insufficiently validated for clinical use. This study aims to investigate a recently CE-cleared smartwatch using cuffless BP measurement in a population with normotensive and hypertensive individuals scheduled for 24-h BP measurement.

Generating High-Granularity COVID-19 Territorial Early Alerts Using Emergency Medical Services and Machine Learning.

The pandemic of COVID-19 has posed unprecedented threats to healthcare systems worldwide. Great efforts were spent to fight the emergency, with the widespread use of cutting-edge technologies, especially big data analytics and AI. In this context, the present study proposes a novel combination of geographical filtering and machine learning (ML) for the development and optimization of a COVID-19 early alert system based on Emergency Medical Services (EMS) data, for the anticipated identification of outbreaks with very high granularity, up to single municipalities.

Six Drivers to Face the XXI Century Challenges and Build the New Healthcare System: "La Salute in Movimento" Manifesto.

The aging of the population, the burden of chronic diseases, possible new pandemics are among the challenges for healthcare in the XXI century. To face them, technological innovations and the national recovery and resilience plan within the European Union can represent opportunities to implement changes and renovate the current healthcare system in Italy, in an effort to guarantee equal access to health services. Considering such scenario, a panel of Italian experts gathered in a multidisciplinary Think Tank to discuss possible design of concepts at the basis of a new healthcare system.

Assessment of the relationship between regional wall motion abnormality score revealed by parametric imaging and the extent of LGE with CMR.

The aim of this study was to assess the relationship between left ventricular (LV) regional myocardial wall motion abnormality (WMA), revealed by visual interpretation of cardiac magnetic resonance (CMR) cine images together with the computed wall motion parametric image, and the transmural scar extent, as assessed by Late gadolinium Enhancement (LGE), in 40 patients. Each cine CMR short-axis loop was processed to compute a parametric image where each pixel represents the amplitude of the Hilbert transform of videointensity over time. Two expert radiologists blindly interpreted the cine CMR images in combination with the corresponding parametric image to assign a WMA score for each of the 16 myocardial sectors in which the LV myocardium was subdivided.

Automated classification of hand gestures using a wristband and machine learning for possible application in pill intake monitoring.

Background: Thanks to the increased interest towards health and lifestyle, a larger adoption in wearable devices for activity tracking is present among the general population. Wearable devices such as smart wristbands integrate inertial units, including accelerometers and gyroscopes, which can be utilised to perform automatic classification of hand gestures. This technology could also find an important application in automatic medication adherence monitoring.