A5070340780- Coronary Interventions and Diagnostics
- Cardiac Imaging and Diagnostics
- Artificial Intelligence in Healthcare and Education
- Optical Coherence Tomography Applications
- Machine Learning in Healthcare
- Cardiac Valve Diseases and Treatments
- Radiomics and Machine Learning in Medical Imaging
- Advanced X-ray and CT Imaging
- Privacy-Preserving Technologies in Data
- Cryptography and Data Security
- Medical Imaging Techniques and Applications
- Cardiovascular Function and Risk Factors
- Advanced Image Processing Techniques
- Aortic Disease and Treatment Approaches
- ECG Monitoring and Analysis
- Retinal Imaging and Analysis
- Cardiac, Anesthesia and Surgical Outcomes
- Cardiac pacing and defibrillation studies
- Medical Image Segmentation Techniques
- Cardiovascular Disease and Adiposity
- COVID-19 diagnosis using AI
- Chaos-based Image/Signal Encryption
- Acute Myocardial Infarction Research
- Artificial Intelligence in Healthcare
- Advanced MRI Techniques and Applications
Siemens (Romania)
2018-2024
Transylvania University of Brașov
2018-2023
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 confidentiality patient health information have many cases hindered adoption deep learning-based clinical workflows. To allow for processing sensitive without disclosing underlying data, we propose solution based fully homomorphic...
Abstract While neutrinos are often treated as a background for many dark matter experiments, these particles offer new avenue physics: the detection of core-collapse supernovae. Supernovae extremely energetic, violent and complex events that mark death massive stars. During their collapse stars emit large number in short burst. These carry 99% emitted energy which makes fundamental understanding This paper illustrates how COSINUS (Cryogenic Observatory SIgnatures seen Next-generation...
Following the reports of breakthrough performances, machine learning based applications have become very popular in medical field. However, with recent increase concerns related to data privacy, and publication specific regulations (e.g. GDPR), development and, thus, exploitation deep clinical decision making processes, has been rendered impossible many cases. Herein, we describe evaluate an approach that employs Fully Homo-morphic Encryption for allowing computations be performed on...
Ischemic heart disease represent a heavy burden for the medical systems irrespective of methods used diagnosis and treatment such patients in daily routine. The present paper depicts protocol study whose main aim is to develop, implement test an artificial intelligence algorithm cloud based platform fully automated PCI guidance using coronary angiography images. We propose utilisation multiple models produce three-dimensional anatomy reconstruction assess function- post-PCI FFR computation-...
Background In acute coronary syndrome (ACS), a number of previous studies tried to identify the risk factors that are most likely influence rate in-stent restenosis (ISR), but contribution these ISR is not clearly defined. Thus, need for better way identifying independent predictors ISR, which comes in form Machine Learning (ML). Objectives The aim this study evaluate relationship between and associated with ACS develop validate nomogram predict probability through use ML patients undergoing...
Motivated by state-of-the-art performances across a wide variety of areas, over the last few years Machine Learning has drawn significant amount attention from healthcare domain. Despite their potential in enabling person-alized medicine applications, adoption Deep based solutions clinical workflows been hindered many cases strict regulations concerning privacy patient health data. We propose solution that relies on Fully Homomorphic Encryption, particularly MORE scheme, as mechanism for...
Medical imaging provides valuable input for managing cardiovascular disease (CVD), ranging from risk assessment to diagnosis, therapy planning and follow-up.Artificial intelligence (AI) based medical image analysis algorithms provide nowadays state-of-the-art results in CVD management, mainly due the increase computational power data storage capacities.Various challenges remain be addressed speed-up adoption of AI solutions routine management.Although general health are abundant, access...
Cardiovascular disease (CVD) is the number one cause of death worldwide, and coronary artery (CAD) most prevalent CVD, accounting for 42% these deaths. In view limitations anatomical evaluation CAD, Fractional Flow Reserve (FFR) has been introduced as a functional diagnostic index. Herein, we evaluate feasibility using deep neural networks (DNN) in an ensemble approach to predict invasively measured FFR from raw information that extracted optical coherence tomography (OCT). We performance...
Intraoperative Computer Tomographs (iCT) provide near real time visualizations which can be registered with high-quality preoperative images to improve the confidence of surgical instrument navigation. However, intraoperative have a small field view making registration process error prone due reduced amount mutual information. We herein propose method extrapolate thin acquisitions as prior step registration, increase images, and hence also robustness guiding system. The is based on deep...
Early screening for cancer has proven to improve the survival rate and spare patients from intensive costly treatments due late diagnosis. Cancer in healthy population involves an initial risk stratification step determine method frequency, primarily optimize resource allocation by targeting towards individuals who draw most benefit. For programs, age clinical factors such as family history are part of algorithm. In this paper, we focus on developing a blood marker-based approach, which...
One of the most active research areas in computed tomography (CT) is to devise a strategy reduce radiation exposure, while maintaining high image quality, required for accurate diagnosis. The recent advancements offered by deep learning based data-driven approaches solving inverse problems biomedical imaging have led development an alternative method producing high-quality reconstructed images from low-dose CT data. While reconstruction tackle problem post-processing perspective, this paper,...
Atherosclerosis is one of the most frequent cardiovascular diseases. The dilemma faced by physicians whether to treat or postpone revascularization lesions that fall within intermediate range given an invasive fractional flow reserve (FFR) measurement. paper presents a monocentric study for significance assessment can potentially cause ischemia on large coronary arteries.A new dataset acquired, comprising optical coherence tomography (OCT) images, clinical parameters, echocardiography and...
Abstract Funding Acknowledgements This research has been funded by the grant PlaqueImage, contract number 26/01.09.2016, SMIS code 103544, Project European Union Aims Coronary shear stress (CSS) recently recognized to play a significant role in coronary plaque progression and vulnerabilisation. However, evolution of CSS after implantation different types stents is still under investigation. The aim this study was assess along lesions following bioabsorbable vascular scaffolds (BVS),...