Andrei Vasilateanu

Steps towards a digital health ecosystem

In the paper an IT infrastructure for supporting the shift from organization-centric to patient-centric model of healthcare service delivery to facilitate collaborative, multidisciplinary and cross-organizational healthcare delivery processes is presented. The core of this infrastructure is an internet platform that provides e-services and promotes the interoperability by enabling not only inter-communication among authorized healthcare professionals, but also sharing the Virtual Healthcare Record, an authoritative, multi-dimensional view on the patient health state. The platform is implemented in the LuMiR project for Basilicata, an Italian region where integration of healthcare applications is required. The LUMIR approach, its origin and peculiarities are briefly presented. The projects final target is the regional health digital ecosystem that interacts with the healthcare system for better supporting it. The agent-oriented paradigm emerges as a promising approach to map the autonomic healthcare systems and their users in virtual entities, and to add values such as flexibility, adaptability, and reusability. over traditional object- or service-oriented approaches.

3D dental reconstruction from CBCT data

We will present in this paper a method for the reconstruction of the oral 3D structure using Computed Tomography (CT) sections obtained from Cone Beam Computed Tomography (CBCT). A high-quality visualization of the dental tissues, based nowadays on fine 3D reconstruction, is essential for accurate diagnosis and treatments. Dental CBCT images present several major problems because of the noisiness and of the configuration of the system. We tried several algorithms in order to overcome these issues and to obtain a feasible and efficient method that can be used directly in the clinical procedures. Our approach includes novel procedures like the delimitation of the region of interest and the combination of registration and segmentation steps, which grant faster results and open the route to better representations.

Knowledge Based Segmentation for Fast 3D Dental Reconstruction from CBCT

We present in this paper a fast method for the reconstruction of the dental 3D structure using the images obtained from Cone Beam Computed Tomography (CBCT). A high-quality visualization of the dental tissues, including 3D reconstruction, is essential for the accurate diagnosis and treatment in dentistry. The segmentation for the dental CBCT images presents several major problems because of the noisiness of the images. We analyze the existing methods and propose one that uses the information for the dental structure and distribution to reduce the computational load and to provide good results in an automatic procedure.

Automatic segmentation for 3D dental reconstruction

Cone Beam Computed Tomography (CBCT) is one of the favorite imaging technologies in dentistry because if offers 3D images in conditions of reduced irradiation. High quality 3D imaging is essential for first-rate diagnostic and treatment, despite the rather low quality images, with low contrast and high noise. The paper presents an automatic method for 3D reconstruction of the oral cavity. We analyze the existing state of the art in the field and propose a segmentation method that uses the domain knowledge related to mouth and teeth to reduce the computational load and to provide good results in an automatic procedure. The method is based on a Canny type edge detector, that was found to offer better control than active contour methods.

Evaluating indoor localization using WiFi for patient tracking

Our paper presents the first phase in developing an indoor localization technique based on WiFi for patient monitoring scenarios in patient empowerment and ambient assisted living settings. We describe an experiment that measures the accuracy for positioning using WiFi signals and the results show that decent accuracy can be obtained using existing equipment and with a minimum setting effort.

Virtual Agent organizations Supporting disease specific communities

Shifting the common one-to-one doctor-patient dialogue into many-to-many discussions carried between people suffering from the same conditions enables the formation of communities of sharing and support where patients can find treatments suitable for their illnesses. The xVASSal project serves as a platform of communication in a virtual organization in digital healthcare. Having an agent oriented approach, it offers a personalized experience for each user, helping with the selection of treatments without compromising the user`s identity.

Teeth labeling from CBCT data using the Circular Hough Transform

In this paper, we explore the possibilities to detect and label straightforward the teeth in Cone Beam Computed Tomography images, without performing complex segmentation procedures on teeth. We are using the Circular Hough Transform (CHT) to find the teeth positions, segmentation using intensity level to determine the mandible and deformable templates to find the best fit position for the teeth on the mandible. The dental arch is split into 16 regions and the teeth are labeled by their position in a given region.

Improved GROMACS algorithms using the MPI parallelization

Molecular dynamics (MD) studies the structure of molecular systems which are subject to certain constraints and forces. The simulation of particles is a tool for examining atomic systems during a period of nanoseconds in which the trajectory of atoms and the state of the system is analyzed. GROMACS is a package which supports molecular dynamics simulations and energy minimization, being started the University of Groningen. Usually molecular dynamics simulations are time consuming, sometimes taking weeks and even months. In order to obtain the output of the simulation in less time, parallelization is used through the use of MPI (Message Passing Interface). The article presents the MPI parallelization of a novel thermostat algorithm for molecular dynamics and experimental results.

Neural network based edge detection for CBCT segmentation

Edge detection is an important task in image processing, many times as part of the segmentation process. When segmentation is performed in medical imaging, one of the preferred tools is neural networks, because of their capabilities of adaptive learning and non-linear mapping. We present in this paper the neural network tools used for edge detection and we propose one that is able to perform edge detection in dental Cone Beam Computer Tomography (CBCT) images, a necessary step for the teeth 3D reconstruction.

AsthMate -- Supporting Patient Empowerment through Location-Based Smartphone Applications

The ever changing challenges and pressures to the healthcare domain have introduced the urgency of finding a replacement for traditional systems. Breakthroughs registered by information systems, advances in data storage and processing solutions sustained by the ubiquity of gadgets and an efficient infrastructure for network and services have sustained a shift of medical systems towards digital healthcare. Asth Mate application is an e-health tool for asthma patients, acting as an enabler for patient empowerment. The contributions brought by the application are both to the individual and to the community exposing a web application that allows citizens to check the state of the air for the area they live in. The ongoing implementation can benefit of the advantages of cloud computing solutions in order to ensure a better deployment and data accessibility. However, data privacy is a key aspect for such systems. In consideration of this reason, a proper trade-off between the functionality, data openness and security should be reached.