Siemens (Romania)

Fractional flow reserve (FFR) is a functional index quantifying the severity of coronary artery lesions and is clinically obtained using an invasive, catheter-based measurement. Recently, physics-based models have shown great promise in being able to noninvasively estimate FFR from patient-specific anatomical information, e.g., obtained from computed tomography scans of the heart and the coronary arteries. However, these models have high computational demand, limiting their clinical adoption. In this paper, we present a machine-learning-based model for predicting FFR as an alternative to physics-based approaches. The model is trained on a large database of synthetically generated coronary anatomies, where the target values are computed using the physics-based model. The trained model predicts FFR at each point along the centerline of the coronary tree, and its performance was assessed by comparing the predictions against physics-based computations and against invasively measured FFR for 87 patients and 125 lesions in total. Correlation between machine-learning and physics-based predictions was excellent (0.9994, P < 0.001), and no systematic bias was found in Bland-Altman analysis: mean difference was -0.00081 ± 0.0039. Invasive FFR ≤ 0.80 was found in 38 lesions out of 125 and was predicted by the machine-learning algorithm with a sensitivity of 81.6%, a specificity of 83.9%, and an accuracy of 83.2%. The correlation was 0.729 (P < 0.001). Compared with the physics-based computation, average execution time was reduced by more than 80 times, leading to near real-time assessment of FFR. Average execution time went down from 196.3 ± 78.5 s for the CFD model to ∼2.4 ± 0.44 s for the machine-learning model on a workstation with 3.4-GHz Intel i7 8-core processor.

Background: Coronary computed tomographic angiography (CTA) is a reliable modality to detect coronary artery disease. However, CTA generally overestimates stenosis severity compared with invasive angiography, and angiographic stenosis does not necessarily imply hemodynamic relevance when fractional flow reserve (FFR) is used as reference. CTA-based FFR (CT-FFR), using computational fluid dynamics (CFD), improves the correlation with invasive FFR results but is computationally demanding. More recently, a new machine-learning (ML) CT-FFR algorithm has been developed based on a deep learning model, which can be performed on a regular workstation. In this large multicenter cohort, the diagnostic performance ML-based CT-FFR was compared with CTA and CFD-based CT-FFR for detection of functionally obstructive coronary artery disease. Methods and Results: At 5 centers in Europe, Asia, and the United States, 351 patients, including 525 vessels with invasive FFR comparison, were included. ML-based and CFD-based CT-FFR were performed on the CTA data, and diagnostic performance was evaluated using invasive FFR as reference. Correlation between ML-based and CFD-based CT-FFR was excellent ( R =0.997). ML-based (area under curve, 0.84) and CFD-based CT-FFR (0.84) outperformed visual CTA (0.69; P &lt;0.0001). On a per-vessel basis, diagnostic accuracy improved from 58% (95% confidence interval, 54%–63%) by CTA to 78% (75%–82%) by ML-based CT-FFR. The per-patient accuracy improved from 71% (66%–76%) by CTA to 85% (81%–89%) by adding ML-based CT-FFR as 62 of 85 (73%) false-positive CTA results could be correctly reclassified by adding ML-based CT-FFR. Conclusions: On-site CT-FFR based on ML improves the performance of CTA by correctly reclassifying hemodynamically nonsignificant stenosis and performs equally well as CFD-based CT-FFR.

Purpose To compare two technical approaches for determination of coronary computed tomography (CT) angiography–derived fractional flow reserve (FFR)—FFR derived from coronary CT angiography based on computational fluid dynamics (hereafter, FFRCFD) and FFR derived from coronary CT angiography based on machine learning algorithm (hereafter, FFRML)—against coronary CT angiography and quantitative coronary angiography (QCA). Materials and Methods A total of 85 patients (mean age, 62 years ± 11 [standard deviation]; 62% men) who had undergone coronary CT angiography followed by invasive FFR were included in this single-center retrospective study. FFR values were derived on-site from coronary CT angiography data sets by using both FFRCFD and FFRML. The performance of both techniques for detecting lesion-specific ischemia was compared against visual stenosis grading at coronary CT angiography, QCA, and invasive FFR as the reference standard. Results On a per-lesion and per-patient level, FFRML showed a sensitivity of 79% and 90% and a specificity of 94% and 95%, respectively, for detecting lesion-specific ischemia. Meanwhile, FFRCFD resulted in a sensitivity of 79% and 89% and a specificity of 93% and 93%, respectively, on a per-lesion and per-patient basis (P = .86 and P = .92). On a per-lesion level, the area under the receiver operating characteristics curve (AUC) of 0.89 for FFRML and 0.89 for FFRCFD showed significantly higher discriminatory power for detecting lesion-specific ischemia compared with that of coronary CT angiography (AUC, 0.61) and QCA (AUC, 0.69) (all P < .0001). Also, on a per-patient level, FFRML (AUC, 0.91) and FFRCFD (AUC, 0.91) performed significantly better than did coronary CT angiography (AUC, 0.65) and QCA (AUC, 0.68) (all P < .0001). Processing time for FFRML was significantly shorter compared with that of FFRCFD (40.5 minutes ± 6.3 vs 43.4 minutes ± 7.1; P = .042). Conclusion The FFRML algorithm performs equally in detecting lesion-specific ischemia when compared with the FFRCFD approach. Both methods outperform accuracy of coronary CT angiography and QCA in the detection of flow-limiting stenosis. © RSNA, 2018

Our world and our lives are changing in many ways. Communication, networking, and computing technologies are among the most influential enablers that shape our lives today. Digital data and connected worlds of physical objects, people, and devices are rapidly changing the way we work, travel, socialize, and interact with our surroundings, and they have a profound impact on different domains, such as healthcare, environmental monitoring, urban systems, and control and management applications, among several other areas. Cities currently face an increasing demand for providing services that can have an impact on people's everyday lives. The CityPulse framework supports smart city service creation by means of a distributed system for semantic discovery, data analytics, and interpretation of large-scale (near-)real-time Internet of Things data and social media data streams. To goal is to break away from silo applications and enable cross-domain data integration. The CityPulse framework integrates multimodal, mixed quality, uncertain and incomplete data to create reliable, dependable information and continuously adapts data processing techniques to meet the quality of information requirements from end users. Different than existing solutions that mainly offer unified views of the data, the CityPulse framework is also equipped with powerful data analytics modules that perform intelligent data aggregation, event detection, quality assessment, contextual filtering, and decision support. This paper presents the framework, describes its components, and demonstrates how they interact to support easy development of custom-made applications for citizens. The benefits and the effectiveness of the framework are demonstrated in a use-case scenario implementation presented in this paper.

Cloud computing is emerging as a major trend in the ICT industry. While most of the attention of the research community is focused on considering the perspective of the Cloud providers, offering mechanisms to support scaling of resources and interoperability and federation between Clouds, the perspective of developers and operators willing to choose the Cloud without being strictly bound to a specific solution is mostly neglected. We argue that Model-Driven Development can be helpful in this context as it would allow developers to design software systems in a cloud-agnostic way and to be supported by model transformation techniques into the process of instantiating the system into specific, possibly, multiple Clouds. The MODAClouds (MOdel-Driven Approach for the design and execution of applications on multiple Clouds) approach we present here is based on these principles and aims at supporting system developers and operators in exploiting multiple Clouds for the same system and in migrating (part of) their systems from Cloud to Cloud as needed. MODAClouds offers a quality-driven design, development and operation method and features a Decision Support System to enable risk analysis for the selection of Cloud providers and for the evaluation of the Cloud adoption impact on internal business processes. Furthermore, MODAClouds offers a run-time environment for observing the system under execution and for enabling a feedback loop with the design environment. This allows system developers to react to performance fluctuations and to re-deploy applications on different Clouds on the long term.

A novel architecture for the field of industrial automation is described, the goals of which are: 1) computation of optimal production plans; 2) automated usage of the optimized plans; 3) flexibility and reusability at development and maintenance; and 4) seamless transition from current practice to the approach introduced herein. The architecture consists of three main components: 1) a set of OPC unified architecture (UA) servers, which are used to model the information from the device level; 2) a set of services organized into two layers (basic and complex services), which act as a link between the first and the third layer; and 3) a constraint satisfaction problem (CSP) layer for the computation of production plans. Extensive performance tests motivate the choice of the service development framework, and prove the effectiveness of the special adapter software solution for the integration of current devices and the ability of the UA server to manage a high number of UA connections. As a proof-of-concept, the architecture has been tested for a real manufacturing problem composed of four flexible manufacturing systems. The results show that the architecture is able to efficiently control and monitor a real manufacturing process according to an optimized schedule with over 99% of the time spent on the manufacturing.

Oral cancer is the most common type of cancer among men in India and other countries in South Asia. Late diagnosis contributes significantly to this mortality, highlighting the need for effective and specific point-of-care diagnostic tools. The same regions with high prevalence of oral cancer have seen extensive growth in mobile phone infrastructure, which enables widespread access to telemedicine services. In this work, we describe the evaluation of an automated tablet-based mobile microscope as an adjunct for telemedicine-based oral cancer screening in India. Brush biopsy, a minimally invasive sampling technique was combined with a simplified staining protocol and a tablet-based mobile microscope to facilitate local collection of digital images and remote evaluation of the images by clinicians. The tablet-based mobile microscope (CellScope device) combines an iPad Mini with collection optics, LED illumination and Bluetooth-controlled motors to scan a slide specimen and capture high-resolution images of stained brush biopsy samples. Researchers at the Mazumdar Shaw Medical Foundation (MSMF) in Bangalore, India used the instrument to collect and send randomly selected images of each slide for telepathology review. Evaluation of the concordance between gold standard histology, conventional microscopy cytology, and remote pathologist review of the images was performed as part of a pilot study of mobile microscopy as a screening tool for oral cancer. Results indicated that the instrument successfully collected images of sufficient quality to enable remote diagnoses that show concordance with existing techniques. Further studies will evaluate the effectiveness of oral cancer screening with mobile microscopy by minimally trained technicians in low-resource settings.

In recent years, powered by state-of-the-art achievements in a broad range of areas, machine learning has received considerable attention from the healthcare sector. Despite their ability to provide solutions within personalized medicine, strict regulations on the confidentiality of patient health information have in many cases hindered the adoption of deep learning-based solutions in clinical workflows. To allow for the processing of sensitive health information without disclosing the underlying data, we propose a solution based on fully homomorphic encryption (FHE). The considered encryption scheme, MORE (Matrix Operation for Randomization or Encryption), enables the computations within a neural network model to be directly performed on floating point data with a relatively small computational overhead. We consider the well-known MNIST digit recognition problem to evaluate the feasibility of the proposed method and show that performance does not decrease when deep learning is applied on MORE homomorphic data. To further evaluate the suitability of the method for healthcare applications, we first train a model on encrypted data to estimate the outputs of a whole-body circulation (WBC) hemodynamic model and then provide a solution for classifying encrypted X-ray coronary angiography medical images. The findings highlight the potential of the proposed privacy-preserving deep learning methods to outperform existing approaches by providing, within a reasonable amount of time, results equivalent to those achieved by unencrypted models. Lastly, we discuss the security implications of the encryption scheme and show that while the considered cryptosystem promotes efficiency and utility at a lower security level, it is still applicable in certain practical use cases.

This paper presents a new optimization approach, based on a hybrid algorithm, used for the design of a new high-speed permanent magnet synchronous machine (HS-PMSM). The HS-PMSM is used for the electromagnetic propulsion system (i.e., the machine will be connected to a magnetic gear) of an electric vehicle. The hybrid optimization algorithm combines two differential evolution methods and a memory mechanism, in order to increase the quality of the algorithm. The evolution of the rotor structure of the HS-PMSM (running at 22 000 r/min) is presented, as well as some aspects of the mechanical risks due to centrifugal forces. The best suited variant will be optimized based on the new optimization approach and the solution will be numerically and experimentally validated.

We introduce a patient-specific model for coronary circulation, by combining anatomical, hemodynamic and functional information from medical images and other clinical observations. The main components of the coupled model are: a lumped heart model, a reduced-order model for hemodynamics in the arterial vessel tree (both healthy and stenosed), and a physiological model for the microvascular bed. The anatomy of the vessel tree is extracted from Coronary Computed Tomography Angiography (CTA) images, followed by an estimation of the impedance of the distal microvascular network. For the blood flow simulations, three states are modeled: rest, drug-induced hyperemia and intense exercise. The results show an excellent agreement with the literature and provide a model for virtual assessment of the flow and underlying functional measures in healthy and stenosed coronary arteries.

Smart Cities are considered recently as a promising solution for providing efficient services to citizens with the use of Information and Communication Technologies. With the latest advances on the Internet of Things, a new era has emerged in the Smart City domain, opening new opportunities for the development of efficient and low-cost applications that aim to improve the Quality of Life in cities. Although there is much research in this area, which has resulted in the development of many commercial products, significant parameters like reliability, security and privacy have not been considered as very important up until now. The newly launched FP7-SmartCities-2013 project RERUM aims to build upon the advances in the area of Internet of Things in Smart Cities and develop a framework to enhance reliability and security of smart city applications, with the citizen at the center of attention. This work presents four applications that will be developed within RERUM, gives a general description of the open reliability and security issues that have to be taken into account and gives an overall view of the solutions that RERUM will develop to address these issues.

INTRODUCTION: With the expansion of available Information and Communication Technology (ICT) services, a plethora of data sources provide structured and unstructured data used to detect certain health conditions or indicators of disease. Data is spread across various settings, stored and managed in different systems. Due to the lack of technology interoperability and the large amounts of health-related data, data exploitation has not reached its full potential yet. AIM: The aim of the CrowdHEALTH approach, is to introduce a new paradigm of Holistic Health Records (HHRs) that include all health determinants defining health status by using big data management mechanisms. METHODS: HHRs are transformed into HHRs clusters capturing the clinical, social and human context with the aim to benefit from the collective knowledge. The presented approach integrates big data technologies, providing Data as a Service (DaaS) to healthcare professionals and policy makers towards a "health in all policies" approach. A toolkit, on top of the DaaS, providing mechanisms for causal and risk analysis, and for the compilation of predictions is developed. RESULTS: CrowdHEALTH platform is based on three main pillars: Data & structures, Health analytics, and Policies. CONCLUSIONS: A holistic approach for capturing all health determinants in the proposed HHRs, while creating clusters of them to exploit collective knowledge with the aim of the provision of insight for different population segments according to different factors (e.g. location, occupation, medication status, emerging risks, etc) was presented. The aforementioned approach is under evaluation through different scenarios with heterogeneous data from multiple sources.

Data privacy is a major concern when accessing and processing sensitive medical data. A promising approach among privacy-preserving techniques is homomorphic encryption (HE), which allows for computations to be performed on encrypted data. Currently, HE still faces practical limitations related to high computational complexity, noise accumulation, and sole applicability the at bit or small integer values level. We propose herein an encoding method that enables typical HE schemes to operate on real-valued numbers of arbitrary precision and size. The approach is evaluated on two real-world scenarios relying on EEG signals: seizure detection and prediction of predisposition to alcoholism. A supervised machine learning-based approach is formulated, and training is performed using a direct (non-iterative) fitting method that requires a fixed and deterministic number of steps. Experiments on synthetic data of varying size and complexity are performed to determine the impact on runtime and error accumulation. The computational time for training the models increases but remains manageable, while the inference time remains in the order of milliseconds. The prediction performance of the models operating on encoded and encrypted data is comparable to that of standard models operating on plaintext data.

Surface decontamination is of general concern in many technical fields including optics, electronics, medical environments, as well as art conservation. In this respect, we developed thin copolymer networks covalently bonded to flexible polyethylene (PE) sheets for hydrogel-based cleaning of varnished paintings. The syntheses of acrylates and methacrylates of the surfactants Triton X-100, Brij 35, and Ecosurf EH-3 or EH-9 and their incorporation into copolymers with acrylamide (PAM) and N-(4-benzoylphenyl)acrylamide are reported. Photocrosslinked polymer networks were prepared from these copolymers on corona-treated PE sheets, which can be swollen with aqueous solution to form hydrogel layers. The cleaning efficacy of these PE-PAM hydrogel systems, when swollen with appropriate cleaning solutions, was evaluated on painting surfaces in dependence of the PAM copolymer composition and degree of crosslinking. Specifically, soil and varnish removal and varnish surface solubilization were assessed on mock-ups as well as on paintings, indicating that even surfactant-free cleaning solutions were effective.

As Artificial Intelligence (AI) becomes more integrated into public governance, concerns about its transparency and accountability have become increasingly important. The use of AI in decision-making processes raises questions about bias, fairness, and the protection of individual fundamental rights. To ensure that AI is used in a way that upholds democratic values, it is essential to develop systems that are trustworthy, transparent, and accountable. Trusted AI allows citizens to have greater trust in public organizations and their decision-making processes, while it also enables public authorities and policy makers to be more transparent and accountable, providing citizens with greater visibility into how policies are developed. In addition, it encourages the use of AI in a way that promotes fairness and equity, ensuring that decision-making processes are unbiased and discrimination free against certain groups of individuals. This paper investigates how these desirable attributes can be developed in ways that are feasible and effective through the design of a holistic environment that incorporates AI and Big Data management mechanisms while preserving that the AI technology should be shaped around human rights, values, and societal needs. Societal change and evidence-based policies will be achieved through the extension of business and policy making processes with advanced approaches, such as eXplainable AI (XAI) and Situation-Aware Explainability (SAX). To this end, a novel approach is proposed, which will converge techniques and research on multiple domains, including social sciences, Trustworthy AI, Ethical AI, Big Data analytics, IoT, and blockchain into a unified ecosystem.

Current neural networks architectures are many times harder to train because of the increasing size and complexity of the used datasets. Our objective is to design more efficient training algorithms utilizing causal relationships inferred from neural networks. The transfer entropy (TE) was initially introduced as an information transfer measure used to quantify the statistical coherence between events (time series). Later, it was related to causality, even if they are not the same. There are only few papers reporting applications of causality or TE in neural networks. Our contribution is an information-theoretical method for analyzing information transfer between the nodes of feedforward neural networks. The information transfer is measured by the TE of feedback neural connections. Intuitively, TE measures the relevance of a connection in the network and the feedback amplifies this connection. We introduce a backpropagation type training algorithm that uses TE feedback connections to improve its performance.

Using the CityPulse framework we developed a proof of concept application for Braşov public transportation company. The application provides route recommendations and incident notifications for the citizens who travel by bus. This is achieved by processing in real-time the data streams about bus arrivals in stations and the incidents reported by citizens. The proof of concept application was presented to the representatives of Braşov municipality and public transport company.

The article focuses on the development of an OPC UA (object linking and embedding (OLE) for process control unified architecture) server. One of the major concerns regarding this new specification is the migration from the old component object model (COM) to the new UA specification. From the various migration strategies described in this article, the authors&apos; server represents a special adapter software solution which aggregates several COM servers, one for each flexible manufacturing system (FMS) modelled in the address space of the UA server. The article focuses on the advantages introduced by the new specification like the unified modelling capability and the extensible meta model. The address space is exemplified by means of a screw fitting station, which is part of the flexible line, on which the UA server has been tested. From the four generally accepted use cases, the server implements the first two: observation and control. They are mainly supported through the variables and the methods of the address space. During the testing phase, the minimum sampling interval regarding the communication with the underlying COM servers has been determined for a different number of FMSs. As a result the special adapter software solution is a fast but also a well-performing approach, which works well even in very complex environments.

INTRODUCTION: Breast and prostate cancer survivors can experience impaired quality of life (QoL) in several QoL domains. The current strategy to support cancer survivors with impaired QoL is suboptimal, leading to unmet patient needs. ASCAPE aims to provide personalized- and artificial intelligence (AI)-based predictions for QoL issues in breast- and prostate cancer patients as well as to suggest potential interventions to their physicians to offer a more modern and holistic approach on cancer rehabilitation. METHODS AND ANALYSES: An AI-based platform aiming to predict QoL issues and suggest appropriate interventions to clinicians will be built based on patient data gathered through medical records, questionnaires, apps, and wearables. This platform will be prospectively evaluated through a longitudinal study where breast and prostate cancer survivors from four different study sites across the Europe will be enrolled. The evaluation of the AI-based follow-up strategy through the ASCAPE platform will be based on patients' experience, engagement, and potential improvement in QoL during the study as well as on clinicians' view on how ASCAPE platform impacts their clinical practice and doctor-patient relationship, and their experience in using the platform. ETHICS AND DISSEMINATION: ASCAPE is the first research project that will prospectively investigate an AI-based approach for an individualized follow-up strategy for patients with breast- or prostate cancer focusing on patients' QoL issues. ASCAPE represents a paradigm shift both in terms of a more individualized approach for follow-up based on QoL issues, which is an unmet need for cancer survivors, and in terms of how to use Big Data in cancer care through democratizing the knowledge and the access to AI and Big Data related innovations. TRIAL REGISTRATION: Trial Registration on clinicaltrials.gov: NCT04879563.

We propose a numerical implementation based on a Graphics Processing Unit (GPU) for the acceleration of the execution time of the Lattice Boltzmann Method (LBM). The study focuses on the application of the LBM for patient-specific blood flow computations, and hence, to obtain higher accuracy, double precision computations are employed. The LBM specific operations are grouped into two kernels, whereas only one of them uses information from neighboring nodes. Since for blood flow computations regularly only 1/5 or less of the nodes represent fluid nodes, an indirect addressing scheme is used to reduce the memory requirements. Three GPU cards are evaluated with different 3D benchmark applications (Poisseuille flow, lid-driven cavity flow and flow in an elbow shaped domain) and the best performing card is used to compute blood flow in a patient-specific aorta geometry with coarctation. The speed-up over a multi-threaded CPU code is of 19.42x. The comparison with a basic GPU based LBM implementation demonstrates the importance of the optimization activities.