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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. [ABSTRACT FROM AUTHOR]

In 2018, the position paper 'Imaging the adult with congenital heart disease: a multimodality imaging approach' was published. The paper highlights, in the first part, the different imaging modalities applied in adult congenital heart disease patients. In the second part, these modalities are discussed more detailed for moderate to complex anatomical defects. Because of the length of the paper, simple lesions were not touched on. However, imaging modalities to use for simple shunt lesions are still poorly known. One is looking for structured recommendations on which they can rely when dealing with an (undiscovered) shunt lesion. This information is lacking for the initial diagnostic process, during repair and at follow-up. Therefore, this paper will focus on atrial septal defect, ventricular septal defect, and persistent arterial duct. Pre-, intra-, and post-procedural imaging techniques will be systematically discussed. This position paper will offer algorithms that might help at a glance. The document is prepared for general cardiologists, trainees, medical students, imagers/technicians to select the most appropriate imaging modality and to detect the requested information for each specific lesion. It might serve as reference to which researchers could refer when setting up a (imaging) study. [ABSTRACT FROM AUTHOR]

We propose a novel decomposition approach that breaks down the levels and trends of lifespan inequality as the sum of cause-of-death contributions. The suggested method shows whether the levels and changes in lifespan inequality are attributable to the levels and changes in (1) the extent of inequality in the cause-specific age-at-death distribution (the "Inequality" component), (2) the total share of deaths attributable to each cause (the "Proportion" component), or (3) the cause-specific mean age at death (the "Mean" component). This so-called Inequality–Proportion–Mean (or IPM) method is applied to 10 low-mortality countries in Europe. Our findings suggest that the most prevalent causes of death (in our setting, "circulatory system" and "neoplasms") do not necessarily contribute the most to overall levels of lifespan inequality. In fact, "perinatal and congenital" causes are the strongest drivers of lifespan inequality declines. The contribution of the IPM components to changes in lifespan inequality varies considerably across causes, genders, and countries. Among the three components, the Mean one explains the least lifespan inequality dynamics, suggesting that shifts in cause-specific mean ages at death alone contributed little to changes in lifespan inequality. [ABSTRACT FROM AUTHOR]

Trustworthy medical AI requires transparency about the development and testing of underlying algorithms to identify biases and communicate potential risks of harm. Abundant guidance exists on how to achieve transparency for medical AI products, but it is unclear whether publicly available information adequately informs about their risks. To assess this, we retrieved public documentation on the 14 available CE-certified AI-based radiology products of the II b risk category in the EU from vendor websites, scientific publications, and the European EUDAMED database. Using a self-designed survey, we reported on their development, validation, ethical considerations, and deployment caveats, according to trustworthy AI guidelines. We scored each question with either 0, 0.5, or 1, to rate if the required information was "unavailable", "partially available," or "fully available." The transparency of each product was calculated relative to all 55 questions. Transparency scores ranged from 6.4% to 60.9%, with a median of 29.1%. Major transparency gaps included missing documentation on training data, ethical considerations, and limitations for deployment. Ethical aspects like consent, safety monitoring, and GDPR-compliance were rarely documented. Furthermore, deployment caveats for different demographics and medical settings were scarce. In conclusion, public documentation of authorized medical AI products in Europe lacks sufficient public transparency to inform about safety and risks. We call on lawmakers and regulators to establish legally mandated requirements for public and substantive transparency to fulfill the promise of trustworthy AI for health. [ABSTRACT FROM AUTHOR]

New propulsion systems in aircrafts must meet strict regulations and emission limitations. The Flightpath 2050 goals set by the Advisory Council for Aviation Research and Innovation in Europe (ACARE) include reductions of 75%, 90%, and 65% in CO2, NOx, and noise, respectively. These goals are not fully satisfied by marginal improvements in gas turbine technology or aircraft design. A novel control design procedure for the next generation of turbofan engines is proposed in this paper to improve Full Authority Digital Engine Control (FADEC) systems and reduce the emission levels to meet the Flightpath 2050 regulations. Hence, an Adaptive Network–based Fuzzy Inference System (ANFIS), nonlinear autoregressive network with exogenous inputs (NARX) techniques, and the block-structure Hammerstein–Wiener approach are used to develop a model for a turbofan engine. The Min–Max control structure is chosen as the most widely used practical control algorithm for gas turbine aero engines. The objective function is considered to minimize the emission level for the engine in a pre-defined maneuver while keeping the engine performance in different aspects. The Genetic Algorithm (GA) is applied to find the optimized control structure. The results confirm the effectiveness of the proposed approach in emission reduction for the next generation of turbofan engines. [ABSTRACT FROM AUTHOR]

Algorithmic decision-making and other types of artificial intelligence (AI) can be used to predict who will commit crime, who will be a good employee, who will default on a loan, etc. However, algorithmic decision-making can also threaten human rights, such as the right to non-discrimination. The paper evaluates current legal protection in Europe against discriminatory algorithmic decisions. The paper shows that non-discrimination law, in particular through the concept of indirect discrimination, prohibits many types of algorithmic discrimination. Data protection law could also help to defend people against discrimination. Proper enforcement of non-discrimination law and data protection law could help to protect people. However, the paper shows that both legal instruments have severe weaknesses when applied to artificial intelligence. The paper suggests how enforcement of current rules can be improved. The paper also explores whether additional rules are needed. The paper argues for sector-specific – rather than general – rules, and outlines an approach to regulate algorithmic decision-making. [ABSTRACT FROM AUTHOR]

BACKGROUND: Upper extremity function (UEF) is often compromised in multiple sclerosis (MS), although its importance is regularly underrecognized relative to ambulation. We explored the concurrent presence of impairment in UEF and ambulation by examining various aspects of UEF across different levels of ambulation. METHODS: The cohort consisted of 247 patients with clinically definite MS or clinically isolated syndrome according to the revised 2010 McDonald criteria. The Nine-Hole Peg Test and the Expanded Disability Status Scale were used to stratify patients into clinically different subgroups. For UEF, cerebellar function (finger-to-nose test), pyramidal function (pronator drift test), and the ability to perform a task of activities of daily living (drinking- from-cup test) were examined. Patient-reported limitations of UEF in daily life were assessed using the Arm Function in Multiple Sclerosis Questionnaire. RESULTS: Patients in more severely impaired ambulation groups displayed poorer performance on all UEF measures. Although most patients had normal to mild (n = 147) or moderate (n = 46) ambulatory impairment, 87.7% exhibited some level of UEF impairment as defined using the Nine-Hole Peg Test. Most patients had mild UEF impairment (n = 174), accounting for the largest proportion in all ambulation groups (51.9%-77.8%). CONCLUSIONS: A distinct pattern of impairment was found for ambulation and multiple aspects of UEF. Independent assessment of multiple aspects of disability may be helpful in treatment decision-making and could support the development of rehabilitation strategies that specifically target UEF impairment [ABSTRACT FROM AUTHOR]

Individual tree detection algorithms (ITD) are used to obtain accurate information about trees. Following the process of individual tree detection, it is possible to use additional processing tools to determine tree parameters such as tree height, crown base height, crown volume, or stem volume. However, many of the methods developed so far have focused on parameterising the algorithms based on the study area, height structure or tree species analysed. Applying the parameters of the method can be challenging in areas with dense and heterogeneous forests with a diverse stand structure. Therefore, this work aimed to develop a method to correct the results of ITD algorithms to identify individual trees more reliably, taking into account different ITD methods based on the Canopy Height Model. In the present study, we proposed a three-step approach to correct segmentation errors. In the first step, erroneous (under- and over-segmentation errors) and correct segments were classified. After classification, the second step was to refine the under-segmentation errors. The final step was to merge segments from the over-segmentation class with correct segments based on the specified conditions. The study was conducted in one of the most complex and diverse forest communities in Europe, making tree identification a major challenge. The accuracy of the segmentation improvements varied depending on the method applied and tree species group examined. Thus, based on the results, the paper advocates for the correction method due to its efficiency in mixed forest stands. Therefore, the present study offers a possible solution to reduce segmentation errors by considering different forest types and different CHM-based ITD methods for identifying individual trees. [ABSTRACT FROM AUTHOR]

Indirect methods for the indirect estimation of reference intervals are increasingly being used, especially for validation of reference intervals, as they can be applied to routine patient data. In this study, we compare three statistically different indirect methods for the verification and validation of reference intervals in eight laboratories distributed throughout Europe. The RefLim method is a fast and simple approach which calculates the reference intervals by extrapolating the theoretical 95 % of non-pathological values from the central linear part of a quantile-quantile plot. The Truncated Maximum Likelihood (TML) method estimates a smoothed kernel density function for the distribution of the mixed data, for which it is assumed that the "central" part of the distribution represents the healthy population. The refineR utilizes an inverse modelling approach. This algorithm identifies a model that best explains the observed data before transforming the data with the Box-Cox transformation. We show that the different indirect methods each have their advantages but can also lead to inaccurate or ambiguous results depending on the approximation of the mathematical model to real-world data. A combination of different methodologies can improve the informative value and thus the reliability of results. Based on routine measurements of four enzymes alkaline phosphatase (ALP), total amylase (AMY), cholinesterase (CHE) and gamma-glutamyl transferase (GGT) in adult women and men, we demonstrate that some reference limits taken from the literature need to be adapted to the laboratory's particular local and population characteristics. [ABSTRACT FROM AUTHOR]