June 23, 2017

Medical Curriculum Innovations: From theory to practice

Nicolaos Dombros's picture
Nicolaos Dombros
Aristotle University of Thessaloniki (GR)
Conference room
Session time
Friday, June 23, 2017 - 09:00 to 10:30

Exhibition & Posters I

Conference room
Session time
Friday, June 23, 2017 - 09:00 to 18:45
Simulation for Training Cochlear Implant Electrode Insertion
Abstract: 
Cochlear implant surgery is performed to restore hearing in patients with a range of hearing disorders. To optimise hearing outcomes, trauma during the insertion of a cochlear implant electrode has to be minimised. Factors that contribute to the degree of trauma caused during surgery include: the location of the electrode, type of electrode, and the competence level of the surgeon. Surgical competence depends on knowledge of anatomy and experience in a range of situations, along with technical skills.
Xingjun Ma's picture
Xingjun Ma
The University of Melbourne (AU)
Sudanthi Wijewickrema's picture
Sudanthi Wijewickrema
University of Melbourne (AU)
Yun Zhou's picture
Yun Zhou
Bridget Copson's picture
Bridget Copson
James Bailey's picture
James Bailey
Gregor Kennedy's picture
Gregor Kennedy
Stephen OLeary's picture
Stephen OLeary
Towards an effective and efficient learning for biomedical data classification
Abstract: 
Nowadays a huge volume of biomedical data (images, genes, etc) are daily generated. The interpretation of such data involves a considerable expertise. The misinterpretation and/or misdetection of a suspicious clinical finding leads to increasing the negligence claims, and redundant procedures (e.g. biopsies). The analysis of biomedical data is a complex task which are performed by specialists on whose expertise degree affects the accuracy of their diagnosis. Besides, due to the huge volume of data, it is a tiresome process.
Guilherme Camargo's picture
Guilherme Camargo
Rafael Staiger Bressan's picture
Rafael Staiger Bressan
Federal University of Technology Paraná
Pedro Henrique Bugatti's picture
Pedro Henrique Bugatti
Priscila Tiemi Maeda Saito's picture
Priscila Tiemi Maeda Saito
Dynamical Modelling of Pulmonary Compartment Response Using Differential Approach
Abstract: 
The article is focused on modeling and simulation of pulmonary compartment behaving and response during gas flowing in the lungs. The pulmonary system model includes the two ways: derivation of mathematical model for individual parts respiratory system, and numerical simulation of these parts. Dynamical model behavior is based on the set of differential equations allowing for dynamical description of entire pulmonary compartment. These simulations are especially important for understanding pulmonary system during gas cycle, and also serve for prediction of behaving pulmonary diseases.
Jan Kubicek's picture
Jan Kubicek
VSB-TU Ostrava (CZ)
Martin Augustynek's picture
Martin Augustynek
Marek Penhaker's picture
Marek Penhaker
Dissimilarity Measure of Consecutive Frames in Wireless Capsule Endoscopy Videos: a way of searching for abnormalities
Abstract: 
In a previous work we have shown that the curve representing the dissimilarity measure between consecutive frames of a wireless capsule endoscopic video of the small bowel, obtained by means of an image registration method, can be regarded as a rough indicator of the speed of the capsule, and simultaneously, it is also a valuable auxiliary medical tool.
Isabel Figueiredo's picture
Isabel Figueiredo
University of Coimbra (PT)
Carlos Leal's picture
Carlos Leal
Luis Pinto's picture
Luis Pinto
Pedro Figueiredo's picture
Pedro Figueiredo
Richard Tsai's picture
Richard Tsai
Wavelet based classification of epileptic seizures in EEG signals
Abstract: 
Epilepsy is a chronic neurological disorder characterized by recurrent, sudden discharges of cerebral neurons, called seizures. Seizures are not always clearly defined and have extremely varied morphologies. Neurophysiologists are not always able to discriminate seizures, especially in long-term EEG datasets. Affecting 1% of the world’s population with 1/3 of the epileptic patients not corresponding to anti-epileptic medication, epilepsy is constantly under the microscope and systems for automated detection of seizures are thoroughly examined.
Katerina D. Tzimourta's picture
Katerina D. Tzimourta
Markos G. Tsipouras's picture
Markos G. Tsipouras
University of Western Macedonia
Nikolaos Giannakeas's picture
Nikolaos Giannakeas
Loukas G. Astrakas's picture
Loukas G. Astrakas
Spyridon Konitsiotis's picture
Spyridon Konitsiotis
Alexandros Tzallas's picture
Alexandros Tzallas
Technological Educational Institute of Epirus (GR)
Measuring Steatosis in Liver Biopsies using Machine Learning and Morphological Imaging
Abstract: 
Non-Alcohol Liver Disease (NAFLD) is nowadays the most common liver disease in Western Countries. It is the chronic condition of fat expansion in liver, which is not associated with alcohol consumption. Quantitating steatosis in liver biopsies could provide objective measurement of the severity of the disease, instead of using semi-quantitative scoring systems. The current work, introduces an automated method for measuring steatosis in liver biopsies, using both machine learning and classical image processing techniques.
Nikolaos Giannakeas's picture
Nikolaos Giannakeas
Alexandros Tzallas's picture
Alexandros Tzallas
Technological Educational Institute of Epirus (GR)
Roberta Forlano's picture
Roberta Forlano
Maria Vavva's picture
Maria Vavva
Evaggelos C. Karvounis's picture
Evaggelos C. Karvounis
Maria Tsimplakidou's picture
Maria Tsimplakidou
Pinelopi Manousou's picture
Pinelopi Manousou
Longitudinal Monitoring and Detection of Alzheimer's Type Dementia from Spontaneous Speech Data
Abstract: 
A method for detection of Alzheimer's type dementia though analysis of vocalisation features that can be easily extracted from spontaneous speech is presented. Unlike existing approaches, this method does not rely on transcriptions of the patient's speech. Tests of the proposed method on a data set of spontaneous speech recordings of Alzheimer's patients (n=235) and elderly controls (n=242) show that accuracy of 68% can be achieved with a Bayesian classifier operating on features extracted through simple algorithms for voice activity detection and speech rate tracking.
Saturnino Luz's picture
Saturnino Luz
University of Edinburgh (UK)
A SIMULATION ENVIRONMENT FOR ACTIVE ENDOSCOPIC CAPSULES
Abstract: 
The best way for researchers to test their algorithms and design concepts, before experimenting on real human beings, is to create simulation environment platforms. In this paper, a virtual simulator for active endoscopic capsules is proposed. The simulator intends to provide researchers with an environment to test their vision and navigation algorithms applied to endoscopic capsule applications. The proposed simulation was created using Gazebo simulator under Robotic Operating System (ROS) environment.
Yasmeen Abu-Kheil's picture
Yasmeen Abu-Kheil
Khalifa University of Science and Technology (AE)
Lakmal Seneviratne's picture
Lakmal Seneviratne
Jorge Dias's picture
Jorge Dias
Differential diagnosis listing as relevance feedback: An essential user interface for clinical decision support systems
Abstract: 
"The user interface of the CDSS has not been well addressed by the field of research. In the previous study, we investigated the presentation of diagnostic output and proposed the hierarchical representation of differential diagnosis lists that provides an effective way of interfacing with a CDSS. To improve the input process, in this study, we proposed the incorporation of relevance feedback. Such feedback can be used to sharpen the search query, and to initiate a dialogue with the user, regarding the presence or absence of particular diagnostic information.
Takashi Okumura's picture
Takashi Okumura
National Institute of Public Health (JP)
Tomoko Kajiyama's picture
Tomoko Kajiyama
Noboru Sonehara's picture
Noboru Sonehara
Application of Evolutionary Algorithms on Unsupervised Segmentation of Lymphoma Histological Images
Abstract: 
Histological images analysis is widely used to carry out diagnoses of different types of cancer. Digital image processing methods can be used for this purpose, leading to more objective diagnoses. Segmentation techniques are applied in order to identify cellular structures capable of indicating the incidence of diseases. In addition, the extracted features from these specific regions can aid pathologists in diagnoses decision using classification techniques.
Thaina A. A. Tosta's picture
Thaina A. A. Tosta
Federal University of ABC
Marcelo Z. Do Nascimento's picture
Marcelo Z. Do Nascimento
Paulo R. Faria's picture
Paulo R. Faria
Leandro A. Neves's picture
Leandro A. Neves
Recognizing ureter and uterine artery in endoscopic images using a convolutional neural network
Abstract: 
Though endoscope-based surgery is highly beneficial regarding the recovery of the patients, it may also raise difficult tasks during special surgical actions. The main disadvantage is the lack of the tactile information for the surgeon, who consequently has to rely on the visual information only. As a special scenario, some organs of the human body, like ureters and arteries, have similar visual appearances, so distinguishing them is very challenging in real-time endoscopic surgery in these days.
Balazs Harangi's picture
Balazs Harangi
University of Debrecen (HU)
Andras Hajdu's picture
Andras Hajdu
University of Debrecen (HU)
Rudolf Lampe's picture
Rudolf Lampe
Peter Torok's picture
Peter Torok
Towards Patterns for Defining and Changing Data Collection Instruments in Mobile Healthcare Scenarios
Abstract: 
Especially in healthcare scenarios or clinical trials, a huge amount of data needs to be collected in rather short time. In this context, smart mobile devices can be a powerful instrument to foster data collection scenarios. To enable domain experts to create and maintain mobile data collection applications themselves, the QuestionSys framework uses a model-driven approach to digitize paper-based questionnaires. This digital transformation, in turn, is based on manual as well as automated tasks. The manual tasks applied by the domain experts are called change patterns.
Johannes Schobel's picture
Johannes Schobel
Rudiger Pryss's picture
Rudiger Pryss
Ulm University (DE)
Marc Schickler's picture
Marc Schickler
Manfred Reichert's picture
Manfred Reichert
On the Ontological Modelling of Co-Medication and Drug Interactions in Medical Cancer Therapy Regimens for a Clinical Decision Support System
Abstract: 
In an ongoing project aiming at a comprehensive AI-based tool to support clinical decisions in medical cancer therapy, the ontology OCTA is being developed. Its purpose is to provide general knowledge about active ingredients, therapy regimens, etc. that can be used by such a clinical decision support system. In this paper, we present a new extension of OCTA modelling co-medication and drug interactions, enabling the answering of queries relevant for medical decision making dealing with these aspects.
Christoph Beierle's picture
Christoph Beierle
University of Hagen (DE)
Bettina Sader's picture
Bettina Sader
Christian Eichhorn's picture
Christian Eichhorn
Gabriele Kern-Isberner's picture
Gabriele Kern-Isberner
Ralf Georg Meyer's picture
Ralf Georg Meyer
Mathias Nietzke's picture
Mathias Nietzke
An IT Platform Enabling Remote Therapeutic Interventions
Abstract: 
The development of IT solutions for properly supporting homework in the context of therapeutic interventions has been neglected so far. Both psychologists and medical doctors crave for the widespread use of smart mobile devices (e.g., smartphones) to assist patients in performing homework. If a patient is assisted by a smart mobile device, for example, the latter can automatically inform the therapist of the homework outcome and, hence, enable him to timely adjust the therapeutic intervention if required.
Marc Schickler's picture
Marc Schickler
Rudiger Pryss's picture
Rudiger Pryss
Ulm University (DE)
Michael Stach's picture
Michael Stach
Johannes Schobel's picture
Johannes Schobel
Winfried Schlee's picture
Winfried Schlee
Thomas Probst's picture
Thomas Probst
Berthold Langguth's picture
Berthold Langguth
Manfred Reichert's picture
Manfred Reichert
Laugh and crying perception in patients with severe and moderate TBI using FFT analysis
Abstract: 
Post-traumatic emotional disorder event is a major cause of slow rehabilitation worldwide. The present study investigates the effects of emotional sound stimulation at different groups of subjects with TBI. At the base of our research strategy, we had used a group with diffuse axonal injury (DAI). We collected three groups of patients: 45 healthy adults, 20 patients with severe TBI and 19 patients with moderate TBI. Neurophysiology trials aim was to determine the processes that accompany emotional changes within traumatic brain damage.
Galina Portnova's picture
Galina Portnova
IHNA (RU)
Kseniya Gladun's picture
Kseniya Gladun
Detection and Management of Depression in Cancer Patients using Augmented Reality Technologies, Multimodal Signal Processing and Persuasive Interfaces
Abstract: 
This visual paper aims at proposing a framework for detecting depression in cancer patients using prosodic and statistical features extracted by speech, while chatting with an augmented reality virtual coach.
Alexandros Roniotis's picture
Alexandros Roniotis
Haridimos Kondylakis's picture
Haridimos Kondylakis
Institute of Computer Science FORTH (GR)
Manolis Tsiknakis's picture
Manolis Tsiknakis
Neural Networks Modelling after Myocardial Infarction in Rats
Abstract: 
Cardiac function is reduced after acute myocardial infarction due to myocardial injury and to changes in the viable non-ischemic myocardium, a process known as cardiac remodeling. Current treatment of patients with acute myocardial infarction (AMI) reduces infarct size, preserves left ventricular function, and improves survival. However, it does not prevent remodeling which leads to heart failure. The aim of the present study was to model the echocardiographically estimated data with respect to the surgically collected data using Neural Networks.
Ioanna Iliopoulou's picture
Ioanna Iliopoulou
Iordanis Mourouzis's picture
Iordanis Mourouzis
George Lambrou's picture
George Lambrou
National Technical University of Athens (GR)
Constantinos Pantos's picture
Constantinos Pantos
Dimitra Iliopoulou's picture
Dimitra Iliopoulou
Dimitrios Koutsouris's picture
Dimitrios Koutsouris
National Technical University of Athens (GR)
Automatic Segmentation of Melanoma in Dermoscopy Images using Fuzzy Numbers
Abstract: 
Melanoma is the most dangerous type of skin cancer, but when treated in its early stages the chance of cure is increased. However, the detection of melanoma is a challenging task even for specialists due to low contrast of skin lesions and presence of artifacts. Therefore, developing an automatic segmentation tool for skin lesion analysis using dermoscopy images is a critical step for improving the diagnosis. This work proposes an automatic melanoma segmentation approach, based on Fuzzy Numbers.
Filipe Rolim Cordeiro's picture
Filipe Rolim Cordeiro
Federal Rural University of Pernambuco (BR)
Jessica Barbosa Diniz's picture
Jessica Barbosa Diniz
Random Walker with Fuzzy Initialization Applied to Segment Masses in Mammography Images
Abstract: 
Segmentation of masses in mammography images is an important task in early detection of breast cancer. Although the quality of segmentation is crucial to avoid misdiagnosis, the segmentation process is a challenging task even for specialists, due to the presence of ill-defined edges and low contrast images. In this work, we propose an improvement on Random Walker algorithm to segment masses, by applying a fuzzy approach in the initialization stage. We evaluated the new approach compared with classical Random Walker, using 57 images of Mini-MIAS database.
Filipe Rolim Cordeiro's picture
Filipe Rolim Cordeiro
Federal Rural University of Pernambuco (BR)
Kallebe F. P. Bezerra's picture
Kallebe F. P. Bezerra
Wellington Pinheiro Dos Santos's picture
Wellington Pinheiro Dos Santos
Self-Adaptive Multi-Objective Evolutionary Algorithm for Molecular Design
Abstract: 
Self-adaptation is an efficient way to control the strategy parameters of an EA automatically during optimization. It is based on implicit evolutionary search in the space of strategy parameters, and has been proven well as on-line parameter control method for a variety of strategy parameters, from local to global ones. Our proposed SAMOEA is a two level algorithm. The outer level is the algorithm that is responsible for the self adaptive techniques and is based on a MOGA implementation. The inner level consists of eMEGA instances.
Christos Kannas's picture
Christos Kannas
Constantinos Pattichis's picture
Constantinos Pattichis
University of Cyprus (CY)
Towards a Conceptual Framework Model Fostering Process Comprehension in Healthcare
Abstract: 
Despite the widespread use of process models in healthcare organizations, there are many unresolved issues regarding the reading and comprehension of these models by domain experts. This is aggravated by the fact that there exists a plethora of process modeling languages for the graphical documentation of processes, whose use is often not consistent for various reasons. Hence, the identification of those factors fostering the comprehension of process models becomes crucial.
Michael Zimoch's picture
Michael Zimoch
Ulm University (DE)
Rudiger Pryss's picture
Rudiger Pryss
Ulm University (DE)
Thomas Probst's picture
Thomas Probst
Winfried Schlee's picture
Winfried Schlee
Manfred Reichert's picture
Manfred Reichert
ICE: Interactive Classification Rule Exploration on Epidemiological Data
Abstract: 
Personalized medicine benefits from the identification of subpopulations that exhibit higher prevalence of a disease than the general population: such subpopulations can become the target of more intensive investigations to identify risk factors and to develop dedicated therapies. Classification rule discovery algorithms are an appropriate tool for discovering such subpopulations: they scale well, even for multi-dimensional data, are not affected by missing values, and deliver comprehensible patterns.
Miro Schleicher's picture
Miro Schleicher
Till Ittermann's picture
Till Ittermann
Uli Niemann's picture
Uli Niemann
University of Magdeburg (DE)
Henry VOlzke's picture
Henry VOlzke
Myra Spiliopoulou's picture
Myra Spiliopoulou
Otto-von-Guericke University Magdeburg (DE)
A digital pen-pased tool for instant digitisation and digitalisation of biopsy protocols
Abstract: 
In order to improve medical processes in nephrology, we present an application that allows doctors to create biopsy protocols by using a digital pen on a tablet. The biopsy protocol app is seamlessly integrated into existing infrastructure at the hospital (see figure 1). Compared to other reporting tools, we provide (1) real-time hand-writing/gesture recognition and real-time feedback on the recognition results on the screen; (2) a real-time digitisation into structured data and PDF documents; and (3) the mapping of the transcribed contents into concepts of the Banff classification.
Alexander Prange's picture
Alexander Prange
Danilo Schmidt's picture
Danilo Schmidt
Daniel Sonntag's picture
Daniel Sonntag
German Research Center for AI (DE)
Travelers’ Perceptions about m-Health Technology
Abstract: 
The aim of this paper is to present the perceptions of the Travelers about the m-Health Technology. An empirical study was conducted to record travelers’ mobile technology usage behavior and their perceptions about a proposed idea for an m-Health application. A pilot mobile application which informs travelers for health issues automatically, according to their current location was developed based on the finding of the study and evaluated by a small group of users.
Parisis Gallos's picture
Parisis Gallos
National and Kapodistrian University of Athens
John Mantas's picture
John Mantas
Lesion Segmentation in Dermoscopy Images Using Particle Swarm Optimization and Markov Random Field
Abstract: 
Malignant melanoma is one of the most rapidly increasing cancers globally and it is the most dangerous form of human skin cancer. Dermoscopy is one of the major imaging modalities used in the diagnosis of melanoma. Early detection of melanoma can be helpful and usually curable. Due to the difficulty for dermatologist’s in the interpretation of dermoscopy images, Computer Aided Diagnosis systems can be very helpful to facilitate the early detection. The automated detection of the lesion borders is one of the most important steps in dermoscopic image analysis.
Khalid Eltayef's picture
Khalid Eltayef
Brunel Unevirsity London (UK)
Yongmin Li's picture
Yongmin Li
Xiaohui Liu's picture
Xiaohui Liu
A Low Bit Rate Wearable Motion Sensing Platform for Gesture Classification
Abstract: 
A body motion sensing platform is presented. The target application for this platform is gesture recognition. The platform consists of wearable sensor nodes built using off-the-shelf components. Each sensor node includes inertial sensors as well as a low- power microcontroller and a 2.4 GHz radio transceiver. Signal classifiers such as Fisher’s linear discriminant classifiers, static neural networks and focused time delay neural networks (fTDNN) are employed to classify the signals obtained from the wearable sensor nodes.
Fahad Moiz's picture
Fahad Moiz
Walter D. Leon-Salas's picture
Walter D. Leon-Salas
Reza Derakhshani's picture
Reza Derakhshani
University of Missouri-Kansas City (USA)
Yugyung Lee's picture
Yugyung Lee
An innovative gaming approach to prevent anxiety disorders and promote youth resilience
Abstract: 
As the stressed adolescents of today are the adults of tomorrow, it is important to prevent and diagnose early anxiety disorders and enhance overall self esteem and wellbeing of the youth. A novel intervention for Generalized Anxiety Disorders in adolescents is proposed, that incorporates current clinical guidelines and state-of-the art ICT technology.
Vassilia Costarides's picture
Vassilia Costarides
National Technical University of Athens (GR)
Athanasios Anastasiou's picture
Athanasios Anastasiou
Ioannis Kouris's picture
Ioannis Kouris
Dimitrios Koutsouris's picture
Dimitrios Koutsouris
National Technical University of Athens (GR)
Ecoepidemiological Simulation as a Serious Game Engine Module
Abstract: 
The integration of an agent-based simulation model as a component of a game engine for serious games targeting prevention and health promotion in the context of infectious diseases is described. It is argued that a combination of agent-based modelling and serious games can help provide a more realistic picture of disease spread than conventional ecoepidemiological models by facilitating the integration of more detailed multidisciplinary expert knowledge.
Masood Masoodian's picture
Masood Masoodian
Aalto University (FI)
Saturnino Luz's picture
Saturnino Luz
University of Edinburgh (UK)
WeightBit : an advancement in wearables technology
Abstract: 
Wearable devices are becoming an important interface between users and fitness activities. Their capabilities are improving exponentially and new strategies are being developed to track sports using sensors that are widely used in robotics. In combination with these wearable gadgets, smartphone applications are normally created to allow the user to visualise data, share data through social networks, or compete with other users. The technology behind these devices is often simple and uses sensors that can be found in a smartphone, such as GPS, accelerometer and gyroscope.
Dario Guida's picture
Dario Guida
University of Westminster (UK)
Artie Basukoski's picture
Artie Basukoski
Automatic Lymphocyte Detection on Gastric Cancer IHC Images Using Deep Learning
Abstract: 
Tumor infiltrating immune cells in cancer, such as lymphocytes, have been studied in last years and evidence shows them to be related with the disease prognosis. Knowing the distribution and localization of these cells is of especial interest for pathologists and nowadays involves manual counting on Immunohistochemistry (IHC) Images. This work presents a model based on Deep Convolutional Neural Networks to automatically detect and count lymphocytes on IHC Images of gastric cancer as well as an lymphocyte images database for future work.
Emilio Garcia's picture
Emilio Garcia
Renato Hermoza's picture
Renato Hermoza
Pontifical Catholic University of Peru (PE)
Cesar Beltran Castanon's picture
Cesar Beltran Castanon
Pontificia Universidad Católica del Perú (PE)
Luis Cano's picture
Luis Cano
Miluska Castillo's picture
Miluska Castillo
Carlos Castanñeda's picture
Carlos Castanñeda
Predicting Comorbidities using Resampling and Dynamic Bayesian Networks with Latent Variables
Abstract: 
Comorbidities such as hypertension and lipid metabolism are often associated in diseases such as diabetes, and the early prediction of these is of great value when trying to manage progression. This is the start of a project to model multiple comorbidities in diabetes using dynamic Bayesian networks with latent variables in order to stratify patient cohorts. In this paper, we demonstrate some initial results on a dataset where the class imbalance problem poses an issue due to the rare occurrence of different individual comorbidities on a visit-by-visit basis.
Leila Yousefi's picture
Leila Yousefi
Lucia Saachi's picture
Lucia Saachi
Riccardo Bellazzi's picture
Riccardo Bellazzi
Luca Chiovato's picture
Luca Chiovato
Allan Tucker's picture
Allan Tucker
Brunel University London (UK)
Automatic extraction of breast cancer information from clinical reports
Abstract: 
The majority of clinical data is only available in unstructured text documents. Thus, their automated usage in data-based clinical application scenarios, like quality assurance and clinical decision support by treatment suggestions, is hindered because it requires high manual annotation efforts. In this work, we introduce a system for the automated processing of clinical reports of mamma carcinoma patients that allows for the automatic extraction and seamless processing of relevant textual features.
Claudia Breischneider's picture
Claudia Breischneider
Sonja Zillner's picture
Sonja Zillner
Siemens AG (DE)
Matthias Hammon's picture
Matthias Hammon
Paul Gass's picture
Paul Gass
Daniel Sonntag's picture
Daniel Sonntag
German Research Center for AI (DE)
Toward a Network-based Approach to Modeling Epistatic Interactions in Genome-wide Association Studies
Abstract: 
In genome-wide association studies, genetic variants can be analyzed within statistical frameworks that take the underlying genetic model into consideration (e.g., the Cochran–Armitage test for trend or the logistic regression model). Should a researcher was to investigate the role played by the interaction between two or more genetic variants (epistasis), it would suffice to add interaction terms to a logistic regression model, where these terms correspond to the product of the genetic variants. However, such a model would not capture the subtlety of the interactions in the human genome.
Camilo Palazuelos's picture
Camilo Palazuelos
University of Cantabria (SP)
Marta Zorrilla's picture
Marta Zorrilla
Javier Llorca's picture
Javier Llorca
Relevant lifelong nutrition information for the prevention and treatment of childhood obesity – Design and creation of new openEHR archetype set
Abstract: 
"Introduction: The worldwide prevalence of obesity had a drastic increase in childhood/ adolescents. In 2014, it was estimated that 41 million children in all world, under the age of 5 were overweight or obese. Despite all the efforts, the number of overweight and obese children/adolescents is rising in almost all countries. Meanwhile, some points impaired the healthcare professionals to follow obesity guideline clinical practice (CPG). Information systems have an important role to play in the maintaining of this reality.
Priscila Maranhão's picture
Priscila Maranhão
Gustavo Marísio Bacelar-Silva's picture
Gustavo Marísio Bacelar-Silva
Duarte Nuno Gonçalves-Ferreira's picture
Duarte Nuno Gonçalves-Ferreira
Pedro Vieira-Marques's picture
Pedro Vieira-Marques
Ricardo João Cruz-Correia's picture
Ricardo João Cruz-Correia
Quality of information about Physical Activity in Breast Cancer Facebook Pages. A Preliminary Content Review
Abstract: 
INTRODUCTION: Adherence to physical activity might be the most important behavior associated with lower mortality and higher quality of life in cancer survivors. Presencial support groups are valuable and have been largely recommended, however, it may not be available for all patients. As an alternative, it is common that many women access Internet and social media resources looking for information, advice, and support in blogs, chat groups, Facebook, and Twitter.
Gabriel R. Signorelli's picture
Gabriel R. Signorelli
Oncoavanze (SP)
Matilde M. Fernandez's picture
Matilde M. Fernandez
Francisco Monteiro-Guerra's picture
Francisco Monteiro-Guerra
Salumedia Tecnologias (SP)
Luis Fernandez-Luque's picture
Luis Fernandez-Luque
Qatar Computing Research Institute (QA)
Brian Caulfield's picture
Brian Caulfield
Method for intra-surgical phase detection by using real-time medical device data
Abstract: 
"The analysis of surgical activities became a popular field of research in recent years and various methods have been published to detect surgical phases from manifold data sources in operating rooms. Goal of this research is to develop a method for extracting realtime information of surgical activities. In this work we use fine-grained data of surgical devices and operating room equipment which is produced permanently during a surgery.
Norman Spangenberg's picture
Norman Spangenberg
Leipzig University (DE)
Christoph Augenstein's picture
Christoph Augenstein
Bogdan Franczyk's picture
Bogdan Franczyk
Martin Wagner's picture
Martin Wagner
Martin Apitz's picture
Martin Apitz
Hannes Kenngott's picture
Hannes Kenngott
Developing a collaborative knowledge system for Cancer Diseases
Abstract: 
In this paper we present a collaborative consultative knowledge system for cancer diseases. The «Worldwide Collaborative Consultative Knowledge System» aims to meet four goals. The first one is to be a tool which allows experts in a particular domain to develop a common knowledge base in a collaborative way. A second goal is the support of multiple knowledge bases. Each knowledge base is constructed to be used in a consultative way either from simple users or from experts of the domain offering high quality consultation services. In this paper we focus in cancer diseases.
Mihalis Giannoulis's picture
Mihalis Giannoulis
Emmanouil Marakakis's picture
Emmanouil Marakakis
Haridimos Kondylakis's picture
Haridimos Kondylakis
Institute of Computer Science FORTH (GR)
Studying the Potential of Multi-Target Classification to Characterize Combinations of Classes with Skewed Distribution
Abstract: 
The identification of subpopulations with particular characteristics with respect to a disease is important for personalized diagnostics and therapy design. But what if the manifestation of the disease is not described by one target variable but of many? Multi-target classification algorithms are the straightforward choice in this context and have been successfully applied in different application scenarios. However, most investigations do not focus on the effects of a skewed class distribution, where the prevalence of one of the multi-target combinations is more rare than the others.
Arne Schneck's picture
Arne Schneck
Myra Spiliopoulou's picture
Myra Spiliopoulou
Otto-von-Guericke University Magdeburg (DE)
Sven Kalle's picture
Sven Kalle
Rudiger Pryss's picture
Rudiger Pryss
Ulm University (DE)
Winfried Schlee's picture
Winfried Schlee
Thomas Probst's picture
Thomas Probst
Berthold Langguth's picture
Berthold Langguth
Manfred Reichert's picture
Manfred Reichert
Michael Landgrebe's picture
Michael Landgrebe
Conceptualising a Targeted Rehabilitation Exercise Biofeedback System for a Cancer Survivorship Population
Abstract: 
"Introduction The increased prevalence of cancer survivors requires a focus on developing long-term, cost-effective management strategies to prevent and limit disability and morbidity. Background Cancer survivors with pain, weakness and restricted movement often benefit from targeted exercise programmes provided by a Physiotherapist. Physical, psychological and situational factors can impact on patients’ abilities to complete these exercises.
Louise Brennan's picture
Louise Brennan
University College Dublin (IE)
Ailish Daly's picture
Ailish Daly
Brian Caulfield's picture
Brian Caulfield
Towards Flexible Remote Therapeutic Interventions
Abstract: 
During the last years, an increasing number of patients have been suffering from mental disorders, making the introduction of efficient therapies absolutely essential. In this context, therapeutic interventions with homework play a crucial role. In line with this, the widespread dissemination of smart mobile devices offers promising opportunities for the support of a variety of remote therapeutic interventions. First, smart mobile devices can assist patients in performing homework. Second, they may be used to monitor homework outcomes by therapists.
Marc Schickler's picture
Marc Schickler
Rudiger Pryss's picture
Rudiger Pryss
Ulm University (DE)
Johannes Schobel's picture
Johannes Schobel
Winfried Schlee's picture
Winfried Schlee
Thomas Probst's picture
Thomas Probst
Manfred Reichert's picture
Manfred Reichert
Integrating a PACS Network to a Statewide Telemedicine System - A case study of the Santa Catarina State Integrated Telemedicine and Telehealth System
Abstract: 
In the literature, PACS and Telemedicine are located in different or complementary works. However, when integrated as part of the same solution, both contribute to the healthcare process as a whole. This work describes the experience on integrating a PACS infrastructure to a statewide telemedicine system, allowing to the latter to benefit from concepts defined by the former in terms of acquisition, transmission and storage of digital images.
Alexandre Savaris's picture
Alexandre Savaris
Alexandre Augusto Gimenes Marquez Filho's picture
Alexandre Augusto Gimenes Marquez Filho
Rodrigo Rodrigues Pires de Mello's picture
Rodrigo Rodrigues Pires de Mello
Gabriela Bussolo Colonetti's picture
Gabriela Bussolo Colonetti
Aldo von Wangenheim's picture
Aldo von Wangenheim
Dirk Krechel's picture
Dirk Krechel
RheinMain University of Applied Science (DE)
Efficiently Indexing Multiple Repositories of Medical Image Databases
Abstract: 
Performing content-based image retrieval over large repositories of medical images demands efficient computational techniques. The use of such techniques is intended to speed up the work of physicians, who often have to deal with information from multiple data repositories. When dealing with multiple data repositories, the common computational approach is to search each repository separately and merge the multiple results into one final response, which slows down the whole process.
Paulo H. Oliveira's picture
Paulo H. Oliveira
Lucas C. Scabora's picture
Lucas C. Scabora
Mirela T. Cazzolato's picture
Mirela T. Cazzolato
University of Sao Paulo - ICMC (BR)
Willian D. Oliveira's picture
Willian D. Oliveira
Agma J. M. Traina's picture
Agma J. M. Traina
University of Sao Paulo (BR)
Caetano Traina-Jr's picture
Caetano Traina-Jr
University of Sao Paulo (BR)

GT3 Decision Support and Recommendation Systems

Alexander Astaras's picture
Alexander Astaras
American College of Thessaloniki (GR)
Daniel Sonntag's picture
Daniel Sonntag
German Research Center for AI (DE)
Conference room
Session time
Friday, June 23, 2017 - 09:00 to 10:30
Prognosis of abdominal aortic aneurysms: A machine learning-enabled approach merging clinical, morphometric, biomechanical and texture information
Abstract: 
An effective surveillance strategy for the progression of abdominal aortic aneurysms (AAAs) may be achieved by assessing its expected growth rate in a personalized manner. Given the variety of factors with an impact on AAA growth, an integrative approach to the problem could potentially benefit from incorporating clinical and morphometric data, as well as mechanical stress characterizations. In addition, here we investigated the use of texture information on computed tomography angiography images within the AAA sac.
Fernando Garcia-Garcia's picture
Fernando Garcia-Garcia
ARTORG Center & University of Bern (CH)
Eleni Metaxa's picture
Eleni Metaxa
Stergios Christodoulidis's picture
Stergios Christodoulidis
Marios Anthimopoulos's picture
Marios Anthimopoulos
Nikolaos Kontopodis's picture
Nikolaos Kontopodis
Martina Correa-Londono's picture
Martina Correa-Londono
Thomas R. Wyss's picture
Thomas R. Wyss
Yannis Papaharilaou's picture
Yannis Papaharilaou
Christos V. Ioannou's picture
Christos V. Ioannou
Hendrik von Tengg-Kobligk's picture
Hendrik von Tengg-Kobligk
Stavroula Mougiakakou's picture
Stavroula Mougiakakou
A non-invasive medical decision support prototype system for Dermatology based on electrical impedance spectroscopy (DermaSense)
Abstract: 
Premature detection of malignant melanoma remains the primary factor in reducing mortality from this form of cancer. During the last decade diagnostic sensitivity and specificity have improved through the utilization of new computer-based technologies, which help improve lesion selection for pathology review and biopsy. Despite these advances in melanoma diagnosis, initial detection, timely recognition and quick treatment of melanoma remain crucial.
Alexander Zogkas's picture
Alexander Zogkas
Sotiria Gilou's picture
Sotiria Gilou
Aristotle University of Thessaloniki (GR)
Inessa Kirsanidou's picture
Inessa Kirsanidou
Chrysovalantis Korfitis's picture
Chrysovalantis Korfitis
Christina Kemanetzi's picture
Christina Kemanetzi
Elizabeth Lazaridou's picture
Elizabeth Lazaridou
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)
Alexander Astaras's picture
Alexander Astaras
American College of Thessaloniki (GR)
Is a Decision Support System Based on Robson's Classification Enough to Reduce Cesarean Section Rates?
Abstract: 
The cesarean section (CS) rates are important global indicators for measuring the access to obstetric services. In 2001, Robson proposed a CS classification in ten-groups as the most appropriate to compare surgery rates. However, having a decisional support system from Robson's Classification is enough to reduce CS rates? The births analysis that occurred in 2016, inside a public hospital maternity, showed 1,946 deliveries of which 35.7% were CS with a positive growth trend (R2 = 0.137).
Juliano Gaspar's picture
Juliano Gaspar
Universidade Federal de Minas Gerais (BR)
Zilma Reis's picture
Zilma Reis
Juliana Barra's picture
Juliana Barra
Design and Development of a Mobile Decision Support System: Guiding Clinicians Regarding Law in the Practice of Psychiatry in Emergency Department
Abstract: 
Decision-making in an emergency department needs to be efficient. It does not allow observation of the patient for a prolonged period of time, especially if the patients harm themselves or others, or refuses treatment. This includes suicidal, violent, intentional self-inflicted or non-consenting to treatments’ patient. Clinicians have to quickly decide whether to call the police, admit the patient to the psychiatric ward, according to recommended, predefined procedures.
Soudabeh Khodambashi's picture
Soudabeh Khodambashi
Norwegian University of Science and Technology (NO)
Florentin Moser's picture
Florentin Moser
Jon Atle Gulla's picture
Jon Atle Gulla
Pekka Abrahamsson's picture
Pekka Abrahamsson
Integrated decision support by combining textual information extraction, facetted search and information visualisation
Abstract: 
This work focusses on our integration steps of complex and partly unstructured medical data into a clinical research database with subsequent decision support. Our main application is an integrated facetted search tool, followed by information visualisation based on automatic information extraction results from textual documents. We describe the details of our technical architecture (open-source tools), to be replicated at other universities, research institutes, or hospitals.
Daniel Sonntag's picture
Daniel Sonntag
German Research Center for AI (DE)
Hans-Jurgen Profitlich's picture
Hans-Jurgen Profitlich
Predicting Sepsis Biomarker Progression under Therapy
Abstract: 
Sepsis is a serious, life-threatening condition that presents a growing problem in medicine and health-care. It is characterized by quick progression and high variability in the disease manifestation, so treatment should be personalized and tailored to fit individual characteristics of a particular subject. That requires close monitoring of the patient's state and reliable predictions of how the targeted therapy will affect sepsis progression over time.
Ivan Stojkovic's picture
Ivan Stojkovic
Temple University (USA)
Zoran Obradovic's picture
Zoran Obradovic
Temple University (USA)

ST1 - Ambient Assisted Living based on IoT Technologies

Dimitrios Koutsouris's picture
Dimitrios Koutsouris
National Technical University of Athens (GR)
Kostas Giokas's picture
Kostas Giokas
AiM Research Biomedical Engineering Lab NTUA (GR)
Conference room
Session time
Friday, June 23, 2017 - 11:00 to 12:30
Applied technologies and Smart Home applications in the health sector
Abstract: 
Smart homes are no longer design concepts of the future. They are being built now, and they are having a direct impact on the lifestyles of people living in them. The aim of smart home systems is to create an environment that is aware of the activities taking place within it. Beside the healthy people, disabled people also need such systems to make their life easier, because they encounter with a lot of difficulties in their everyday life especially when they are at home.
Nikolaos Katsivelis's picture
Nikolaos Katsivelis
Athanasios Anastasiou's picture
Athanasios Anastasiou
Ourania Petropoulou's picture
Ourania Petropoulou
George Lambrou's picture
George Lambrou
National Technical University of Athens (GR)
Kostas Giokas's picture
Kostas Giokas
AiM Research Biomedical Engineering Lab NTUA (GR)
Dimitrios Koutsouris's picture
Dimitrios Koutsouris
National Technical University of Athens (GR)
A Fall Prevention System for the Elderly: Preliminary Results
Abstract: 
The fall prevention in the elderly population is a field of growing interest. This paper presents the preliminary results of a fall prevention system based on a customized exergame program. Results show that the participants involved in the experiments evaluate positively the system usability. Moreover, in order to evaluate the efficiency of the system, a global improvement of around 8.8% has been observed in the postural response after just two sessions with the system.
Giuseppe Palestra's picture
Giuseppe Palestra
StreamVision sas (FR)
Mohamed Rebiai's picture
Mohamed Rebiai
Estelle Corutial's picture
Estelle Corutial
Kostas Giokas's picture
Kostas Giokas
AiM Research Biomedical Engineering Lab NTUA (GR)
Dimitrios Koutsouris's picture
Dimitrios Koutsouris
National Technical University of Athens (GR)
Indoor Localization: A Cost-Effectiveness vs. Accuracy Study
Abstract: 
"Recognizing the occupants’ movement and locations within a home is a basic functionality, underlying a variety of smart-home services, including energy management, ambient environment control, and assistive-living services for seniors and people with disabilities. The outdoor-localization variant of the problem is effectively addressed with the use of GPS; however, GPS does not work well inside buildings, which makes the indoor positioning problem a very active research topic.
Parisa Mohebbi's picture
Parisa Mohebbi
Eleni Stroulia's picture
Eleni Stroulia
University of Alberta (CA)
Ioanis Nikolaidis's picture
Ioanis Nikolaidis
University of Alberta (CA)
Quality of Context Evaluating Approach in AAL Environment using IoT Technology
Abstract: 
This paper presents an approach to the evaluation of Quality of Context (QoC) parameters in a ubiquitous Ambient Assisted Living (AAL) e-Health platform, supporting the care of people with special needs (elderly or with health problems) thus improving their quality of life. The proposal is initially verified with the Siafu simulator in an AAL scenario where the user’s health is monitored with information about blood pressure and body temperature. The research proceeded with the use of IoT technology, the e-Health Sensor Platform, a differentiated real environment.
Debora Cabral Nazario's picture
Debora Cabral Nazario
Pedro J. Campos's picture
Pedro J. Campos
Eduardo C. Inacio's picture
Eduardo C. Inacio
Federal University of Santa Catarina (BR)
Mario A. R. Dantas's picture
Mario A. R. Dantas
Robotic applications towards an interactive alerting system for medical purposes
Abstract: 
"Social consumer robots are slowly but strongly invading our everyday lives as their prices are becoming lower and lower, constituting them affordable for a wide range of civilians. There has been a lot of research concerning the potential applications of social robots, some of which may implement companionship or proxying technology-related tasks and assisting in everyday household endeavors, among others.
Konstantinos Panayiotou's picture
Konstantinos Panayiotou
Aristotle University of Thessaloniki (GR)
Sofia E. Reppou's picture
Sofia E. Reppou
George Karagiannis's picture
George Karagiannis
Emmanouil Tsardoulias's picture
Emmanouil Tsardoulias
Aristeidis G. Thallas's picture
Aristeidis G. Thallas
Andreas L. Symeonidis's picture
Andreas L. Symeonidis
HEAR?INFO: A Modern Mobile-Web Platform Addressed to Hard-of-Hearing Elderly Individuals
Abstract: 
In the concept of the hearing loss awareness, a modern mobile-web platform is hereby presented, aiming to offer constant online access to individuals who are hard of hearing, while presenting them regularly updated information concerning their condition. This information is presented via a specific modified interface, taking into account the special needs of the specific community. After a thorough research in GUI, the software requirements substitute or supplement the lack of integrated sound systems, with visual modifications, caption text and even specially chosen colours.
Penelope Ioannidou's picture
Penelope Ioannidou
National Technical University of Athens (GR)
Panagiotis Katrakazas's picture
Panagiotis Katrakazas
Stefanos Kollias's picture
Stefanos Kollias
Sarafidis Michail's picture
Sarafidis Michail
National Technical University of Athens (GR)
Dimitrios Koutsouris's picture
Dimitrios Koutsouris
National Technical University of Athens (GR)

Medical Curriculum Innovations: From theory to practice

Nicolaos Dombros's picture
Nicolaos Dombros
Aristotle University of Thessaloniki (GR)
Conference room
Session time
Friday, June 23, 2017 - 11:00 to 12:30
A Pilot Medical Curriculum Analysis and Visualization According to Medbiquitous Standards
Abstract: 
Curriculum design and implementation in higher medical education can be a great challenge. Although there are well-defined standards, such as the Curriculum Inventory and Competency Framework by MedBiquitous Consortium, existing systems are incapable of a visual representation of the various components, attributes, and relations. In this paper, we present the MEDCIN platform, a pilot tool which uses a standard-compliant curriculum data model to offer comprehensive and thorough analysis of a given curriculum.
Martin Komenda's picture
Martin Komenda
Masaryk University (CZ)
Matej Karolyi's picture
Matej Karolyi
Masaryk University (CZ)
Christos Vaitsis's picture
Christos Vaitsis
dspachos's picture
dspachos
Aristotle University of Thessaloniki (GR)
Luke Woodham's picture
Luke Woodham
St George's University of London (UK)

Keynote Speech

Nikos Chrisochoides's picture
Nikos Chrisochoides
Old Dominion University Norfolk Virginia (USA)
Anastasios Siountas's picture
Anastasios Siountas
Aristotle University of Thessaloniki (GR)
Conference room
Session time
Friday, June 23, 2017 - 12:30 to 13:15

In image-guided neurosurgery, co-registered preoperative anatomical, functional, and diffusion tensor imaging can be used to facilitate a maximally safe resection of brain tumors in eloquent areas of the brain. However, because the brain can deform significantly during surgery, particularly in the presence of tumor resection, non-rigid registration of the preoperative image data to the patient is required. This talk reports the results (using clinical data) of a comparison of the accuracy and performance among four open-source non-rigid registration methods for handling brain deformation in the presence of tumor resection, including a new adaptive method that automatically removes mesh elements in the area of the resected tumor, thereby automatically handling deformation in the presence of resection.

 

In this talk data from 30 glioma surgeries performed at two different hospitals, many of which involved the resection of significant tumor volumes. Three measures aid in assessing the accuracy of the registration methods: (i) visual assessment, (ii) a Hausdorff Distance-based metric, and (iii) a landmark-based approach using anatomical points identified by a neurosurgeon. Performance analysis showed that the adaptive method could be applied, on average, in less than two minutes, achieving desirable speed for use in a clinical setting and significantly better than other readily-available registration methods at modeling deformation in the presence of resection. Both the registration accuracy and performance proved sufficient to be of clinical value in the operating room.

 

Given time availability a brief description of related real-time Image-to-Mesh conversion technologies at developed CRTC to facility adaptive method will be presented.

 

GT4 Technology Enhanced Medical Education and Simulation

Iraklis Paraskakis's picture
Iraklis Paraskakis
SEERC (GR) & The University of Sheffield (UK)
Meni Tsitouridou's picture
Meni Tsitouridou
Aristotle University of Thessaloniki (GR)
Conference room
Session time
Friday, June 23, 2017 - 14:00 to 15:30
Design and Evaluation of a Virtual Reality Simulation Module for Training Advanced Temporal Bone Surgery
Abstract: 
Surgical education has traditionally relied on cadaveric dissection and supervised training in the operating theatre. However, both these forms of training have become inefficient due to issues such as scarcity of cadavers and competing priorities taking up surgeons' time. Within this context, computer-based simulations such as virtual reality have gained popularity as supplemental modes of training.
Sudanthi Wijewickrema's picture
Sudanthi Wijewickrema
University of Melbourne (AU)
Bridget Copson's picture
Bridget Copson
Yun Zhou's picture
Yun Zhou
Xingjun Ma's picture
Xingjun Ma
The University of Melbourne (AU)
Robert Briggs's picture
Robert Briggs
James Bailey's picture
James Bailey
Gregor Kennedy's picture
Gregor Kennedy
Stephen OLeary's picture
Stephen OLeary
Inductive learning of the surgical workflow model through video annotations
Abstract: 
Surgical workflow modeling is becoming increasingly useful to train surgical residents for complex surgical procedures. Rule-based surgical workflows have shown to be useful to create context-aware systems. However, manually constructing production rules is a time-intensive and laborious task. With the expansion of new technologies, large video archive can be created and annotated exploiting and storing the expert’s knowledge.
Hirenkumar Nakawala's picture
Hirenkumar Nakawala
Politecnico di Milano (IT)
Elena De Momi's picture
Elena De Momi
Laura Erica Pescatori's picture
Laura Erica Pescatori
Anna Morelli's picture
Anna Morelli
Giancarlo Ferrigno's picture
Giancarlo Ferrigno
LiveBook: Competence Assessment with Virtual-Patient Simulations
Abstract: 
Virtual-patient simulators play an important role in modern medical education. These simulators provide a safe environment for learning, give contextual feedback to learners, and allow the learner to move beyond the time and space constraints of traditional face-to-face medical instruction. In this paper, we present an interactive simulation system, LiveBook. This system interacts with students in natural language, and provides detailed feedback on the student's performance after a case has been studied.
Sina Jalali's picture
Sina Jalali
Eleni Stroulia's picture
Eleni Stroulia
University of Alberta (CA)
Sarah Foster's picture
Sarah Foster
Sarah Forgie's picture
Sarah Forgie
Amit Persad's picture
Amit Persad
Diya Shi's picture
Diya Shi
Implementation of process-oriented feedback in a clinical reasoning tool for virtual patients
Abstract: 
Virtual patients (VPs) offer a safe environment to teach clinical reasoning skills, but feedback is often provided in outcome-, rather than process-oriented fashion. For complex cognitive skills, such as clinical reasoning, the process itself is often more important then the end result, especially during learning phase. We have developed a tool that can be integrated into VP systems to specifically support the clinical reasoning process and provide both, outcome- and process-oriented feedback.
Inga Hege's picture
Inga Hege
Ludwig-Maximilians-Universität München (DE)
Andrzej A Kononowicz's picture
Andrzej A Kononowicz
Michal Nowakowski's picture
Michal Nowakowski
Martin Adler's picture
Martin Adler
Novel Method for Storyboarding Biomedical Videos for Medical Informatics
Abstract: 
We propose a novel method for developing static storyboard for video clips included with biomedical research literature. The technique uses both visual and audio content in the video to select candidate key frames for the storyboard. From the visual channel, the intra-frames are extracted using FFmpeg tool. IBM Watson speech-to-text service is used to extract words from the audio channel, from which clinically significant concepts (key concepts) are identified using the U.S. National Library of Medicine‘s Repository for Informed Decision Making (RIDEM) service.
Sema Candemir's picture
Sema Candemir
Sameer Antani's picture
Sameer Antani
U.S. National Library of Medicine / NIH (USA)
Zhiyun Xue's picture
Zhiyun Xue
George Thoma's picture
George Thoma
A Proposed Learner Activity Taxonomy and a Framework for Analysing Learner Engagement versus Performance using Big Educational Data
Abstract: 
The inclusion of information and communication technologies in Healthcare and Medical Education is a fact nowadays. Furthermore numerous virtual learning environments have been established in order to host both educational material and learner’s online activities. Online modules in a VLE can be designed in very different ways being part of different types of courses, while different models can be used to design the course based on what the creator aims to achieve. Thus, the types and the importance of the different elements of the online course may vary a lot.
Stathis Th. Konstantinidis's picture
Stathis Th. Konstantinidis
University of Nottingham (UK)
Aaron Fecowycz's picture
Aaron Fecowycz
Kirstie Coolin's picture
Kirstie Coolin
Heather Wharrad's picture
Heather Wharrad
George Konstantinidis's picture
George Konstantinidis
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)

GT5 Biomedical Signal

Behnaz Ghoraani's picture
Behnaz Ghoraani
Florida Atlantic University (USA)
Christos Frantzidis's picture
Christos Frantzidis
Aristotle University of Thessaloniki (GR)
Conference room
Session time
Friday, June 23, 2017 - 14:00 to 15:30
Towards multi-parametric hub scoring of functional cortical brain networks: An electroencephalographic (EEG) study across lifespan
Abstract: 
The attractiveness and robustness of graph theory has stimulated an unprecedented increase in studies aiming to understand the functional organization and dynamics of the brain. The investigation of brain connectomics produces a tremendous amount of data which may be examined at both a macroscopic and microscopic level. However, the interpretation of findings is still challenging. Novel methodological approaches should enhance the arsenal of the tools employed towards the understanding of the interaction of the distinct brain components.
Vasiliki Zilidou's picture
Vasiliki Zilidou
Aristotle University of Thessaloniki (GR)
Christos Frantzidis's picture
Christos Frantzidis
Aristotle University of Thessaloniki (GR)
Ana Vivas's picture
Ana Vivas
Maria Karagianni's picture
Maria Karagianni
Panagiotis Bamidis's picture
Panagiotis Bamidis
Aristotle University of Thessaloniki (GR)
Discrimination of Preictal and Interictal Brain States from Long-Term EEG Data
Abstract: 
Discriminating the preictal state in EEG signals is of great importance in neuroscience and the epileptic seizure prediction field has yet to provide conclusive evidence. In this study, three different classification approaches, including the Repeated Incremental Pruning to Produce Error Reduction (RIPPER) algorithm, Support Vector Machines (SVMs) and Neural Networks (NNs), are investigated for their ability to discriminate preictal from interictal EEG segments. Using public EEG data, a wide range of features is extracted from each segment and then applied to the classifiers.
Kostas M. Tsiouris's picture
Kostas M. Tsiouris
National Technical University of Athens (GR)
Vasileios C. Pezoulas's picture
Vasileios C. Pezoulas
Dimitrios Koutsouris's picture
Dimitrios Koutsouris
National Technical University of Athens (GR)
Dimitris Fotiadis's picture
Dimitris Fotiadis
University of Ioannina (GR)
Michalis Zervakis's picture
Michalis Zervakis
Detection of Alcoholism based on EEG Signals and Functional Brain Network Features Extraction
Abstract: 
Alcohol abuse disorder or alcoholism is a common disorder that leads to brain defects and associated cognitive, emotional and behavioural impairments. Finding and extracting discriminative biological markers, which are correlated to healthy brain pattern and alcoholic brain pattern, helps us to utilize automatic methods for detecting and classifying alcoholism. Many brain disorders could be detected by analyzing the Electroencephalography (EEG) signals. In this paper, for extracting the required markers we analyze the EEG signals for two groups of alcoholic and control subjects.
Negar Ahmadi's picture
Negar Ahmadi
Yulong Pei's picture
Yulong Pei
Technische Universiteit Eindhoven (NL)
Mykola Pechenizkiy's picture
Mykola Pechenizkiy
EEG Classification and Short-Term Epilepsy Prognosis using Brain Computer Interface Software
Abstract: 
The recent advances of Brain Computer Interfaces (BCI) systems, can provide effective assistance for real time prognosis systems for patients who suffered from epileptic seizures. This paper presents an EEG classification strategy for short-term epilepsy prognosis, using software for Brain-Computer Interface (BCI) systems. A training scenario is presented, where significant features are extracted and a classification algorithm is trained. The training procedure extracts knowledge in terms of a classification model, which is employed in a real-time testing.
Alexandros Tzallas's picture
Alexandros Tzallas
Technological Educational Institute of Epirus (GR)
Nikolaos Giannakeas's picture
Nikolaos Giannakeas
Konstantinos Zoulis's picture
Konstantinos Zoulis
Euripidis Glavas's picture
Euripidis Glavas
Technological Educational Institute of Epirus (GR)
Katerina D. Tzimourta's picture
Katerina D. Tzimourta
Loukas G. Astrakas's picture
Loukas G. Astrakas
Spyridon Konitsiotis's picture
Spyridon Konitsiotis
An Intermixed Color Paradigm for P300 Spellers: A Comparison with Gray-scale Spellers
Abstract: 
P300 speller systems represent one of the most basic applications of Brain-Computer Interfaces (BCIs). A traditional P300 speller consists of a 6 by 6 grid of characters in which each column or row in this grid intensifies at random. During such intensification process, the electroencephalography (EEG) data of the subject is recorded and analyzed to determine the character to be spelled.
Mina Meshriky's picture
Mina Meshriky
Ain Shams University (EG)
Seif Eldawlatly's picture
Seif Eldawlatly
Gamal Aly's picture
Gamal Aly
Simulation of Spiral Waves and Point Sources in Atrial Fibrillation with Application to Rotor Localization
Abstract: 
Cardiac simulations play an important role in studies involving understanding and investigating the mechanisms of cardiac arrhythmias. Today, studies of arrhythmogenesis and maintenance are largely being performed by creating simulations of a particular arrhythmia with high accuracy comparable to the results of clinical experiments. Atrial fibrillation (AF), the most common arrhythmia in the United States and many other parts of the world, is one of the major field where simulation and modeling is largely used.
Prasanth Ganesan's picture
Prasanth Ganesan
Kristina Shillieto's picture
Kristina Shillieto
Behnaz Ghoraani's picture
Behnaz Ghoraani
Florida Atlantic University (USA)

Keynote Speech

Vangelis Karkaletsis's picture
Vangelis Karkaletsis
NCSR Demokritos (GR)
Dimitrios Koutsouris's picture
Dimitrios Koutsouris
National Technical University of Athens (GR)
Conference room
Session time
Friday, June 23, 2017 - 15:30 to 16:15

Unobtrusive clinical monitoring with robots in AAL environments: the RADIO ecosystem
Technical advancements in ICT, including robotics, bring new opportunities to improve the quality of life of the elderly, their family and care-givers, and to mimimize the invonvenience and cost of clinical monitoring. Automatically detecting early symptoms of cognitive impairment, frailty and social exclusion would extend people's ability to safely and comportably live independently. 
The ecosystem proposed by the H2020 RADIO project (http://radio-project.eu/) aims to integrate multiple RADIO Home deployments, medical institutions, and informal care givers into an information management and sharing Ecosystem that is by design scalable, secure and privacy-preserving. RADIO Home is, effectively, a robot operating inside a Smart Home. In this environment, Smart Home and robot functionalities accommodate the user’s needs, while assuming interaction with the users as an opportunity for clinical monitoring. In this manner, clinical monitoring sensors do not need to be masked but become an obvious, yet discrete and accepted, part of the user’s daily life. Moreover, a robot has a dynamic presence in the user’s space, which can increase the feeling of safety by being in the right place at the right time. 
The talk will present the project outcomes so far, focusing on the clinical and unobtrusiveness requirements which guided the design of the RADIO architecture, and on the architectural and methodological work to ensure that multiple RADIO Homes and care-givers can be interconnected in a scalable, secure and privacy-preserving ecosystem. It will also present the outcomes of real-environment piloting performed in the premises of our clinical partners, to evaluate the usability of the system, its fitness for its medical purpose and the compliance with unobtrusiveness.

ST4 - Computer-aided and robotic endoscopy systems

Dimitris Iakovidis's picture
Dimitris Iakovidis
University of Thessaly (GR)
Anastasios Koulaouzidis's picture
Anastasios Koulaouzidis
Conference room
Session time
Friday, June 23, 2017 - 16:45 to 18:00
Improved Barrett's Cancer Detection in Volumetric Laser Endomicroscopy scans using Multiple-Frame Voting
Abstract: 
This paper explores the feasibility of using multi-frame analysis to increase the classification performance of machine learning methods for cancer detection in Volumetric Laser Endomicroscopy (VLE). VLE is a novel and promising modality for the detection of neoplasia in patients with Barett's Esophagus (BE). It produces hundreds of high-resolution, cross-sectional images of the esophagus and offers considerable advantages compared to current methods.
Alexandros Rikos's picture
Alexandros Rikos
Fons van der Sommen's picture
Fons van der Sommen
Eindhoven Univeristy of Technology (NL)
Anne-Fre Swager's picture
Anne-Fre Swager
Svitlana Zinger's picture
Svitlana Zinger
Wouter Curvers's picture
Wouter Curvers
Erik Schoon's picture
Erik Schoon
Jacques Bergman's picture
Jacques Bergman
Peter de With's picture
Peter de With
Real-Time Instrument Scene Detection in Screening GI Endoscopic Procedures
Abstract: 
Here we describe a new and effective real-time solution for detecting video segments showing an instrument used during diagnostic or therapeutic operations in endoscopic procedures. In addition, we present a new method to create training data: similarity-based data augmentation. This method automates most of the creation of a large training dataset and prevents extensive manual effort to collect and annotate training data by domain experts.
Chuanhai Zhang's picture
Chuanhai Zhang
Iowa State University (USA)
Wallapak Tavanapong's picture
Wallapak Tavanapong
Johnny Wong's picture
Johnny Wong
Piet C. de Groen's picture
Piet C. de Groen
Junghwan Oh's picture
Junghwan Oh
Sparse coded handcrafted and deep features for colon capsule video summarization
Abstract: 
Abstract—Capsule endoscopy, which uses a wireless camera to take images of the digestive track, is emerging as an alternative to traditional wired colonoscopy. A single examination produces a sequence of approximately 50,000 frames. These sequences are manually reviewed, which is time consuming and typically takes about 45–90 minutes and requires the undivided concentration of the reviewer. In this paper, we propose a novel capsule video summarization framework using sparse coding and dictionary learning in feature space.
Ahmed Mohammed's picture
Ahmed Mohammed
Norwegian University of Science and Technology (NO)
Sule Yildirim's picture
Sule Yildirim
Marius Pedersen's picture
Marius Pedersen
Oistein Hovde's picture
Oistein Hovde
Faouzi Cheikh's picture
Faouzi Cheikh
Visual Localization of Wireless Capsule Endoscopes Aided by Artificial Neural Networks
Abstract: 
Various modalities are used for the examination of the gastrointestinal (GI) tract. One such modality is Wireless Capsule Endoscopy (WCE), a non- invasive technique which consists of a swallowable color camera that enables the detection of GI pathology with only minimal patient discomfort. Currently, tracking of the capsule position is estimated in the 3D abdominal space, using radio-frequency (RF) triangulation.
George Dimas's picture
George Dimas
Dimitris Iakovidis's picture
Dimitris Iakovidis
University of Thessaly (GR)
Alexandros Karargyris's picture
Alexandros Karargyris
Gastone Ciuti's picture
Gastone Ciuti
Anastasios Koulaouzidis's picture
Anastasios Koulaouzidis
Retinal OCT Image Segmentation Using Fuzzy Histogram Hyperbolization and Continuous Max-Flow
Abstract: 
The segmentation of retinal layers is vital for tracking progress of medication and diagnosis of various eye diseases. To date many methods for the analysis exists, however the speckle noise and shadows of retinal blood vessel remains a challenge, with negative influence on the performance of segmentation algorithms. Previous attempts have been focused on image pre-processing or developing sophisticated models for segmentation to address this problem, but it still remains an area of active research.
Bashir Dodo's picture
Bashir Dodo
Brunel Unevirsity London (UK)
Yongmin Li's picture
Yongmin Li
Xiaohui Liu's picture
Xiaohui Liu

GT6: eHealth studies

Carolyn Mcgregor's picture
Carolyn Mcgregor
University of Ontario (CA)
Bridget Kane's picture
Bridget Kane
Karlstad University Business School (SE)
Conference room
Session time
Friday, June 23, 2017 - 16:45 to 18:00
Comparing the Quality of Numeracy Assessment Methods in Healthcare
Abstract: 
Numeracy skill level of patients has great influence on their preferences and priorities for the treatment options concerning their healthcare. There have been different methods for assessment of numeracy skill in healthcare domain. In our previous research we proposed a new Confidence-based Patient Numeracy Assessment (C-PNA) method. In this paper we compare it with other numeracy assessment methods in terms of newly proposed quality characteristics.
Mandana Omidbakhsh's picture
Mandana Omidbakhsh
Olga Ormandjieva's picture
Olga Ormandjieva
Concordia University
Pattern-Based Statechart Modeling Approach for Medical Best Practice Guidelines - A Case Study
Abstract: 
Improving effectiveness and safety of patient care is an ultimate objective for medical cyber-physical systems. Many medical best practice guidelines exist in the format of hospital handbooks which are often lengthy and difficult for medical staff to remember and apply clinically. Statechart is an effective tool to model medical guidelines and enables clinical validation with medical staffs. However, some advanced statechart elements could result in high cost, such as low understandability, high difficulty in clinical validation, formal verification, and failure trace back.
Chunhui Guo's picture
Chunhui Guo
Illinois Institute of Technology (USA)
Zhicheng Fu's picture
Zhicheng Fu
Shangping Ren's picture
Shangping Ren
Yu Jiang's picture
Yu Jiang
Maryam Rahmaniheris's picture
Maryam Rahmaniheris
Lui Sha's picture
Lui Sha
Trust, Ethics and Access: Challenges in studying the work of Multidisciplinary Medical Teams
Abstract: 
This paper highlights the challenges for researchers when undertaking research on multidisciplinary medical teams (MDTs) in real-world healthcare settings, and suggests ways in which these challenges may be addressed.
Bridget Kane's picture
Bridget Kane
Karlstad University Business School (SE)
Saturnino Luz's picture
Saturnino Luz
University of Edinburgh (UK)
Using Affective Computing to automatically adapt serious games for rehabilitation
Abstract: 
Although many studies investigate the automatic adaptation in serious games with the goal to improve the user's motivation, the most part of Affective Computing approaches requires a high development cost and usually does not consider an intervention of health professionals in the control of adaptations that will be executed in a game. This paper describes an approach to enable affective adaptation in serious games for motor rehabilitation with the involvement of physiotherapists.
Renan Vinicius Aranha's picture
Renan Vinicius Aranha
University of Sao Paulo (BR)
Leonardo Souza Silva's picture
Leonardo Souza Silva
Marcos Lordello Chaim's picture
Marcos Lordello Chaim
Fatima De Lourdes Dos Santos Nunes's picture
Fatima De Lourdes Dos Santos Nunes