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1,697 results on '"Fotiadis, Dimitrios I"'

1. Feasibility and Utility of mHealth for the Remote Monitoring of Parkinson Disease: Ancillary Study of the PD_manager Randomized Controlled Trial

Author

Gatsios, Dimitris, Antonini, Angelo, Gentile, Giovanni, Marcante, Andrea, Pellicano, Clelia, Macchiusi, Lucia, Assogna, Francesca, Spalletta, Gianfranco, Gage, Heather, Touray, Morro, Timotijevic, Lada, Hodgkins, Charo, Chondrogiorgi, Maria, Rigas, George, Fotiadis, Dimitrios I, and Konitsiotis, Spyridon

Subjects
Information technology, T58.5-58.64, Public aspects of medicine, RA1-1270
Abstract

BackgroundMobile health, predominantly wearable technology and mobile apps, have been considered in Parkinson disease to provide valuable ecological data between face-to-face visits and improve monitoring of motor symptoms remotely. ObjectiveWe explored the feasibility of using a technology-based mHealth platform comprising a smartphone in combination with a smartwatch and a pair of smart insoles, described in this study as the PD_manager system, to collect clinically meaningful data. We also explored outcomes and disease-related factors that are important determinants to establish feasibility. Finally, we further validated a tremor evaluation method with data collected while patients performed their daily activities. MethodsPD_manager trial was an open-label parallel group randomized study.The mHealth platform consists of a wristband, a pair of sensor insoles, a smartphone (with dedicated mobile Android apps) and a knowledge platform serving as the cloud backend. Compliance was assessed with statistical analysis and the factors affecting it using appropriate regression analysis. The correlation of the scores of our previous algorithm for tremor evaluation and the respective Unified Parkinson’s Disease Rating Scale estimations by clinicians were explored. ResultsOf the 75 study participants, 65 (87%) completed the protocol. They used the PD_manager system for a median 11.57 (SD 3.15) days. Regression analysis suggests that the main factor associated with high use was caregivers’ burden. Motor Aspects of Experiences of Daily Living and patients’ self-rated health status also influence the system’s use. Our algorithm provided clinically meaningful data for the detection and evaluation of tremor. ConclusionsWe found that PD patients, regardless of their demographics and disease characteristics, used the system for 11 to 14 days. The study further supports that mHealth can be an effective tool for the ecologically valid, passive, unobtrusive monitoring and evaluation of symptoms. Future studies will be required to demonstrate that an mHealth platform can improve disease management and care. Trial RegistrationISRCTN Registry ISRCTN17396879; http://www.isrctn.com/ISRCTN17396879 International Registered Report Identifier (IRRID)RR2-10.1186/s13063-018-2767-4

Published
2020
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2. FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare

Author

Lekadir, Karim, Feragen, Aasa, Fofanah, Abdul Joseph, Frangi, Alejandro F, Buyx, Alena, Emelie, Anais, Lara, Andrea, Porras, Antonio R, Chan, An-Wen, Navarro, Arcadi, Glocker, Ben, Botwe, Benard O, Khanal, Bishesh, Beger, Brigit, Wu, Carol C, Cintas, Celia, Langlotz, Curtis P, Rueckert, Daniel, Mzurikwao, Deogratias, Fotiadis, Dimitrios I, Zhussupov, Doszhan, Ferrante, Enzo, Meijering, Erik, Weicken, Eva, González, Fabio A, Asselbergs, Folkert W, Prior, Fred, Krestin, Gabriel P, Collins, Gary, Tegenaw, Geletaw S, Kaissis, Georgios, Misuraca, Gianluca, Tsakou, Gianna, Dwivedi, Girish, Kondylakis, Haridimos, Jayakody, Harsha, Woodruf, Henry C, Mayer, Horst Joachim, Aerts, Hugo JWL, Walsh, Ian, Chouvarda, Ioanna, Buvat, Irène, Tributsch, Isabell, Rekik, Islem, Duncan, James, Kalpathy-Cramer, Jayashree, Zahir, Jihad, Park, Jinah, Mongan, John, Gichoya, Judy W, Schnabel, Julia A, Kushibar, Kaisar, Riklund, Katrine, Mori, Kensaku, Marias, Kostas, Amugongo, Lameck M, Fromont, Lauren A, Maier-Hein, Lena, Alberich, Leonor Cerdá, Rittner, Leticia, Phiri, Lighton, Marrakchi-Kacem, Linda, Donoso-Bach, Lluís, Martí-Bonmatí, Luis, Cardoso, M Jorge, Bobowicz, Maciej, Shabani, Mahsa, Tsiknakis, Manolis, Zuluaga, Maria A, Bielikova, Maria, Fritzsche, Marie-Christine, Camacho, Marina, Linguraru, Marius George, Wenzel, Markus, De Bruijne, Marleen, Tolsgaard, Martin G, Ghassemi, Marzyeh, Ashrafuzzaman, Md, Goisauf, Melanie, Yaqub, Mohammad, Abadía, Mónica Cano, Mahmoud, Mukhtar M E, Elattar, Mustafa, Rieke, Nicola, Papanikolaou, Nikolaos, Lazrak, Noussair, Díaz, Oliver, Salvado, Olivier, Pujol, Oriol, Sall, Ousmane, Guevara, Pamela, Gordebeke, Peter, Lambin, Philippe, Brown, Pieta, Abolmaesumi, Purang, Dou, Qi, Lu, Qinghua, Osuala, Richard, Nakasi, Rose, Zhou, S Kevin, Napel, Sandy, Colantonio, Sara, Albarqouni, Shadi, Joshi, Smriti, Carter, Stacy, Klein, Stefan, Petersen, Steffen E, Aussó, Susanna, Awate, Suyash, Raviv, Tammy Riklin, Cook, Tessa, Mutsvangwa, Tinashe E M, Rogers, Wendy A, Niessen, Wiro J, Puig-Bosch, Xènia, Zeng, Yi, Mohammed, Yunusa G, Aquino, Yves Saint James, Salahuddin, Zohaib, and Starmans, Martijn P A

Subjects
Computer Science - Computers and Society, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, I.2.0, I.4.0, I.5.0
Abstract

Despite major advances in artificial intelligence (AI) for medicine and healthcare, the deployment and adoption of AI technologies remain limited in real-world clinical practice. In recent years, concerns have been raised about the technical, clinical, ethical and legal risks associated with medical AI. To increase real world adoption, it is essential that medical AI tools are trusted and accepted by patients, clinicians, health organisations and authorities. This work describes the FUTURE-AI guideline as the first international consensus framework for guiding the development and deployment of trustworthy AI tools in healthcare. The FUTURE-AI consortium was founded in 2021 and currently comprises 118 inter-disciplinary experts from 51 countries representing all continents, including AI scientists, clinicians, ethicists, and social scientists. Over a two-year period, the consortium defined guiding principles and best practices for trustworthy AI through an iterative process comprising an in-depth literature review, a modified Delphi survey, and online consensus meetings. The FUTURE-AI framework was established based on 6 guiding principles for trustworthy AI in healthcare, i.e. Fairness, Universality, Traceability, Usability, Robustness and Explainability. Through consensus, a set of 28 best practices were defined, addressing technical, clinical, legal and socio-ethical dimensions. The recommendations cover the entire lifecycle of medical AI, from design, development and validation to regulation, deployment, and monitoring. FUTURE-AI is a risk-informed, assumption-free guideline which provides a structured approach for constructing medical AI tools that will be trusted, deployed and adopted in real-world practice. Researchers are encouraged to take the recommendations into account in proof-of-concept stages to facilitate future translation towards clinical practice of medical AI.

Published
2023

3. Documenting the de-identification process of clinical and imaging data for AI for health imaging projects

Author

Kondylakis, Haridimos, Catalan, Rocio, Alabart, Sara Martinez, Barelle, Caroline, Bizopoulos, Paschalis, Bobowicz, Maciej, Bona, Jonathan, Fotiadis, Dimitrios I., Garcia, Teresa, Gomez, Ignacio, Jimenez-Pastor, Ana, Karatzanis, Giannis, Lekadir, Karim, Kogut-Czarkowska, Magdalena, Lalas, Antonios, Marias, Kostas, Marti-Bonmati, Luis, Munuera, Jose, Nikiforaki, Katerina, Pelissier, Manon, Prior, Fred, Rutherford, Michael, Saint-Aubert, Laure, Sakellariou, Zisis, Seymour, Karine, Trouillard, Thomas, Votis, Konstantinos, and Tsiknakis, Manolis

Published
2024
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4. Ηand dexterities assessment in stroke patients based on augmented reality and machine learning through a box and block test

Author

Papagiannis, Georgios, Triantafyllou, Αthanasios, Yiannopoulou, Konstantina G., Georgoudis, George, Kyriakidou, Maria, Gkrilias, Panagiotis, Skouras, Apostolos Z., Bega, Xhoi, Stasinopoulos, Dimitrios, Matsopoulos, George, Syringas, Pantelis, Tselikas, Nikolaos, Zestas, Orestis, Potsika, Vassiliki, Pardalis, Athanasios, Papaioannou, Christoforos, Protopappas, Vasilios, Malizos, Nikolas, Tachos, Nikolaos, and Fotiadis, Dimitrios I.

Published
2024
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16. Personalized prediction of one-year mental health deterioration using adaptive learning algorithms: a multicenter breast cancer prospective study

Author

Kourou, Konstantina, Manikis, Georgios, Mylona, Eugenia, Poikonen-Saksela, Paula, Mazzocco, Ketti, Pat-Horenczyk, Ruth, Sousa, Berta, Oliveira-Maia, Albino J., Mattson, Johanna, Roziner, Ilan, Pettini, Greta, Kondylakis, Haridimos, Marias, Kostas, Nuutinen, Mikko, Karademas, Evangelos, Simos, Panagiotis, and Fotiadis, Dimitrios I.

Published
2023
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17. A new smart-cropping pipeline for prostate segmentation using deep learning networks

Author

Zaridis, Dimitrios G., Mylona, Eugenia, Tachos, Nikolaos S., Marias, Kostas, Papanikolaou, Nikolaos, Tsiknakis, Manolis, and Fotiadis, Dimitrios I.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract

Prostate segmentation from magnetic resonance imaging (MRI) is a challenging task. In recent years, several network architectures have been proposed to automate this process and alleviate the burden of manual annotation. Although the performance of these models has achieved promising results, there is still room for improvement before these models can be used safely and effectively in clinical practice. One of the major challenges in prostate MR image segmentation is the presence of class imbalance in the image labels where the background pixels dominate over the prostate. In the present work we propose a DL-based pipeline for cropping the region around the prostate from MRI images to produce a more balanced distribution of the foreground pixels (prostate) and the background pixels and improve segmentation accuracy. The effect of DL-cropping for improving the segmentation performance compared to standard center-cropping is assessed using five popular DL networks for prostate segmentation, namely U-net, U-net+, Res Unet++, Bridge U-net and Dense U-net. The proposed smart-cropping outperformed the standard center cropping in terms of segmentation accuracy for all the evaluated prostate segmentation networks. In terms of Dice score, the highest improvement was achieved for the U-net+ and ResU-net++ architectures corresponding to 8.9% and 8%, respectively., Comment: 8 pages, 6 figures, 1 table

Published
2021

18. An Exploratory Study of the Value of Vital Signs on the Short-Term Prediction of Subcutaneous Glucose Concentration in Type 1 Diabetes – The GlucoseML Study

Author

Katsarou, Daphne N., Georga, Eleni I., Christou, Maria, Tigas, Stelios, Papaloukas, Costas, Fotiadis, Dimitrios I., Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin, Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Tsanas, Athanasios, editor, and Triantafyllidis, Andreas, editor

Published
2023
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19. Multiclass Semantic Segmentation of Mediterranean Food Images

Author

Konstantakopoulos, Fotios S., Georga, Eleni I., Fotiadis, Dimitrios I., Akan, Ozgur, Editorial Board Member, Bellavista, Paolo, Editorial Board Member, Cao, Jiannong, Editorial Board Member, Coulson, Geoffrey, Editorial Board Member, Dressler, Falko, Editorial Board Member, Ferrari, Domenico, Editorial Board Member, Gerla, Mario, Editorial Board Member, Kobayashi, Hisashi, Editorial Board Member, Palazzo, Sergio, Editorial Board Member, Sahni, Sartaj, Editorial Board Member, Shen, Xuemin, Editorial Board Member, Stan, Mircea, Editorial Board Member, Jia, Xiaohua, Editorial Board Member, Zomaya, Albert Y., Editorial Board Member, Tsanas, Athanasios, editor, and Triantafyllidis, Andreas, editor

Published
2023
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20. LoockMe: An Ever Evolving Artificial Intelligence Platform for Location Scouting in Greece

Author

Trivizakis, Eleftherios, Aidonis, Vassilios, Pezoulas, Vassilios C., Goletsis, Yorgos, Oikonomou, Nikolaos, Stefanis, Ioannis, Chondromatidou, Leoni, Fotiadis, Dimitrios I., Tsiknakis, Manolis, Marias, Kostas, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Barbosa, Simone Diniz Junqueira, Editorial Board Member, Chen, Phoebe, Editorial Board Member, Cuzzocrea, Alfredo, Editorial Board Member, Du, Xiaoyong, Editorial Board Member, Kara, Orhun, Editorial Board Member, Liu, Ting, Editorial Board Member, Sivalingam, Krishna M., Editorial Board Member, Slezak, Dominik, Editorial Board Member, Washio, Takashi, Editorial Board Member, Yang, Xiaokang, Editorial Board Member, Yuan, Junsong, Editorial Board Member, Iliadis, Lazaros, editor, Maglogiannis, Ilias, editor, Alonso, Serafin, editor, Jayne, Chrisina, editor, and Pimenidis, Elias, editor

Published
2023
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22. Coronary computed tomography angiography-based endothelial wall shear stress in normal coronary arteries

Author

Schultz, Jussi, van den Hoogen, Inge J., Kuneman, Jurrien H., de Graaf, Michiel A., Kamperidis, Vasileios, Broersen, Alexander, Jukema, J. Wouter, Sakellarios, Antonis, Nikopoulos, Sotirios, Tsarapatsani, Konstantina, Naka, Katerina, Michalis, Lampros, Fotiadis, Dimitrios I., Maaniitty, Teemu, Saraste, Antti, Bax, Jeroen J., and Knuuti, Juhani

Published
2023
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24. Prevalence and comorbidities of Sjogren's syndrome patients in the Community of Madrid: A population-based cross-sectional study

Author

Barrio-Cortes, Jaime, López-Rodríguez, Juan Antonio, Gómez-Gascón, Tomas, Rayo-Gómez, Ángeles, del Cura-González, Isabel, Domínguez-Berjón, Felicitas, Esteban-Vasallo, Dolores, Chalco-Orrego, Juan Pablo, Vicente-Rabaneda, Esther, Baldini, Chiara, Seghieri, Chiara, Goules, Andreas V., Fotiadis, Dimitrios I., and Tzioufas, Athanasios G.

Published
2023
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25. 3D Reconstruction of Coronary Arteries and Atherosclerotic Plaques based on Computed Tomography Angiography Images

Author

Kigka, Vassiliki I., Rigas, George, Sakellarios, Antonis, Panagiotis, Siogkas, Andrikos, Ioannis O., Exarchos, Themis P., Loggitsi, Dimitra, Anagnostopoulos, Constantinos D., Michalis, Lampros K., Neglia, Danilo, Pelosi, Gualtriero, Parodi, Oberdan, and Fotiadis, Dimitrios I.

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Physics - Medical Physics
Abstract

The purpose of this study is to present a new semi-automated methodology for three-dimensional (3D) reconstruction of coronary arteries and their plaque morphology using Computed Tomography Angiography (CTA) images. The methodology is summarized in seven stages: pre-processing of the acquired CTA images, extraction of the vessel tree centerline, estimation of a weight function for lumen, outer wall and calcified plaque, lumen segmentation, outer wall segmentation, plaque detection, and finally 3D surfaces construction. The methodology was evaluated using both expert manual annotations and estimations of a recently presented Intravascular Ultrasound (IVUS) reconstruction method. As far as the manual annotation validation process is concerned, the mean value of the comparison metrics for the 3D segmentation were 0.749 and 1.746 for the Dice coefficient and Hausdorff distance, respectively. On the other hand, the correlation coefficients for the degree of stenosis 1, the degree of stenosis 2, the plaque burden, the minimal lumen area and the minimal lumen diameter, when comparing the derived from the proposed methodology 3D models with the IVUS reconstructed models, were 0.79, 0.77, 0.75, 0.85, 0.81, respectively. The proposed methodology is an innovative approach for reconstruction of coronary arteries, since it provides 3D models of the lumen, the outer wall and the CP plaques, using the minimal user interaction. Its first implementation demonstrated that it provides an accurate reconstruction of coronary arteries and thus, it may have a wide clinical applicability, Comment: This is the pre-final version. The final version must be cited using the DOI: 10.1016/j.bspc.2017.09.009. \c{opyright} <2018>. This manuscript version is made available under the CC-BY-NC-ND 4.0 license: http://creativecommons.org/licenses/by-nc-nd/4.0/

Published
2019
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27. Machine Learning: A Tool to Shape the Future of Medicine

Author

Hazapi, Orsalia, Lagopati, Nefeli, Pezoulas, Vasileios C., Papayiannis, G. I., Fotiadis, Dimitrios I., Skaltsas, Dimitrios, Vergetis, Vangelis, Tsirigos, Aristotelis, Stratis, Ioannis G., Yannacopoulos, Athanasios N., Gorgoulis, Vassilis G., Kacprzyk, Janusz, Series Editor, Roy, Sanjiban Sekhar, editor, and Taguchi, Y.-H., editor

Published
2022
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33. A systematic review of miRNAs as biomarkers for chemotherapy-induced cardiotoxicity in breast cancer patients reveals potentially clinically informative panels as well as key challenges in miRNA research

Author

Brown, Cameron, Mantzaris, Michael, Nicolaou, Elpiniki, Karanasiou, Georgia, Papageorgiou, Elisavet, Curigliano, Giuseppe, Cardinale, Daniela, Filippatos, Gerasimos, Memos, Nikolaos, Naka, Katerina K., Papakostantinou, Andri, Vogazianos, Paris, Ioulianou, Erietta, Shammas, Christos, Constantinidou, Anastasia, Tozzi, Federica, Fotiadis, Dimitrios I., and Antoniades, Athos

Published
2022
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34. Combining Computational Fluid Dynamics, Structural Analysis, and Machine Learning to Predict Cerebrovascular Events: A Mild ML Approach.

Author

Siogkas, Panagiotis K., Pleouras, Dimitrios, Pezoulas, Vasileios, Kigka, Vassiliki, Tsakanikas, Vassilis, Fotiou, Evangelos, Potsika, Vassiliki, Charalampopoulos, George, Galyfos, George, Sigala, Fragkiska, Koncar, Igor, and Fotiadis, Dimitrios I.

Subjects
COMPUTATIONAL fluid dynamics, CAROTID artery diseases, CAROTID artery, MACHINE learning, ATHEROSCLEROTIC plaque
Abstract

Background/Objectives: Cerebrovascular events, such as strokes, are often preceded by the rupture of atherosclerotic plaques in the carotid arteries. This work introduces a novel approach to predict the occurrence of such events by integrating computational fluid dynamics (CFD), structural analysis, and machine learning (ML) techniques. The objective is to develop a predictive model that combines both imaging and non-imaging data to assess the risk of carotid atherosclerosis and subsequent cerebrovascular events, ultimately improving clinical decision-making. Methods: A multidisciplinary approach was employed, utilizing 3D reconstruction techniques and blood-flow simulations to extract key plaque characteristics. These were combined with patient-specific clinical data for risk evaluation. The study involved 134 asymptomatic individuals diagnosed with carotid artery disease. Data imbalance was addressed using two distinct approaches, with the optimal method chosen for training a Gradient Boosting Tree (GBT) classifier. The model's performance was evaluated in terms of accuracy, sensitivity, specificity, and ROC AUC. Results: The best-performing GBT model achieved a balanced accuracy of 88%, with a ROC AUC of 0.92, a sensitivity of 0.88, and a specificity of 0.91. This demonstrates the model's high predictive power in identifying patients at risk for cerebrovascular events. Conclusions: The proposed method effectively combines CFD, structural analysis, and ML to predict cerebrovascular event risk in patients with carotid artery disease. By providing clinicians with a tool for better risk assessment, this approach has the potential to significantly enhance clinical decision-making and patient outcomes. [ABSTRACT FROM AUTHOR]

Published
2024
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35. Effect of Body Weight on Glycaemic Indices in People with Type 1 Diabetes Using Continuous Glucose Monitoring.

Author

Christou, Maria A., Christou, Panagiota A., Katsarou, Daphne N., Georga, Eleni I., Kyriakopoulos, Christos, Markozannes, Georgios, Christou, Georgios A., Fotiadis, Dimitrios I., and Tigas, Stelios

Subjects
CONTINUOUS glucose monitoring, GLYCEMIC index, BODY mass index, BODY weight, TYPE 1 diabetes
Abstract

Background/Objectives: Obesity and overweight have become increasingly prevalent in different populations of people with type 1 diabetes (PwT1D). This study aimed to assess the effect of body weight on glycaemic indices in PwT1D. Methods: Adult PwT1D using continuous glucose monitoring (CGM) and followed up at a regional academic diabetes centre were included. Body weight, body mass index (BMI), waist circumference, glycated haemoglobin (HbA1c), and standard CGM glycaemic indices were recorded. Glycaemic indices were compared according to BMI, and correlation and linear regression analysis were performed to estimate the association between measures of adiposity and glycaemic indices. Results: A total of 73 PwT1D were included (48% normal weight, 33% overweight, and 19% obese). HbA1c was 7.2% (5.6–10), glucose management indicator (GMI) 6.9% (5.7–8.9), coefficient of variation (CV) for glucose 39.5% ± 6.4, mean glucose 148 (101–235) mg/dL, TIR (time in range, glucose 70–180 mg/dL) 66% (25–94), TBR70 (time below range, 54–69 mg/dL) 4% (0–16), TBR54 (<54 mg/dL) 1% (0–11), TAR180 (time above range, 181–250 mg/dL) 20% ± 7, and TAR250 (>250 mg/dL) 6% (0–40). Glycaemic indices and achievement (%) of optimal glycaemic targets were similar between normal weight, overweight, and obese patients. BMI was associated negatively with GMI, mean glucose, TAR180, and TAR250 and positively with TIR; waist circumference was negatively associated with TAR250. Conclusions: CGM-derived glycaemic indices were similar in overweight/obese and normal weight PwT1D. Body weight and BMI were positively associated with better glycaemic control. PwT1D should receive appropriate ongoing support to achieve optimal glycaemic targets whilst maintaining a healthy body weight. [ABSTRACT FROM AUTHOR]

Published
2024
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36. Modeling Approach of Blood Hemodynamics in the Left Ventricle

Author

Katsarou, Dafni, Sakellarios, Antonis I., Agathopoulos, Simeon, Fotiadis, Dimitrios I., Magjarevic, Ratko, Series Editor, Ładyżyński, Piotr, Associate Editor, Ibrahim, Fatimah, Associate Editor, Lackovic, Igor, Associate Editor, Rock, Emilio Sacristan, Associate Editor, Jarm, Tomaz, editor, Cvetkoska, Aleksandra, editor, Mahnič-Kalamiza, Samo, editor, and Miklavcic, Damijan, editor

Published
2021
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37. Investigation of Drug Eluting Stents Performance Through in silico Modeling

Author

Loukas, Vasileios S., Pleouras, Dimitrios S., Karanasiou, Georgia S., Kyriakidis, Savvas, Sakellarios, Antonis I., Semertzioglou, Arsen, Michalis, Lambros K., Fotiadis, Dimitrios I., Magjarevic, Ratko, Series Editor, Ładyżyński, Piotr, Associate Editor, Ibrahim, Fatimah, Associate Editor, Lackovic, Igor, Associate Editor, Rock, Emilio Sacristan, Associate Editor, Jarm, Tomaz, editor, Cvetkoska, Aleksandra, editor, Mahnič-Kalamiza, Samo, editor, and Miklavcic, Damijan, editor

Published
2021
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38. Dealing with Open Issues and Unmet Needs in Healthcare Through Ontology Matching and Federated Learning

Author

Pezoulas, Vasileios C., Kalatzis, Fanis, Exarchos, Themis P., Goules, Andreas, Gandolfo, Saviana, Zampeli, Evi, Skopouli, Fotini, De Vita, Salvatore, Tzioufas, Athanasios G., Fotiadis, Dimitrios I., Magjarevic, Ratko, Series Editor, Ładyżyński, Piotr, Associate Editor, Ibrahim, Fatimah, Associate Editor, Lackovic, Igor, Associate Editor, Rock, Emilio Sacristan, Associate Editor, Jarm, Tomaz, editor, Cvetkoska, Aleksandra, editor, Mahnič-Kalamiza, Samo, editor, and Miklavcic, Damijan, editor

Published
2021
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39. Three-Dimensional Reconstruction of Carotid Arteries Using Computed Tomography Angiography

Author

Kigka, Vassiliki I., Kyriakidis, Savvas, Sakellarios, Antonis, Potsika, Vassiliki, Tsakanikas, Vasilis, Loggitsi, Dimitra, Michalis, Lampros K., Fotiadis, Dimitrios I., Magjarevic, Ratko, Series Editor, Ładyżyński, Piotr, Associate Editor, Ibrahim, Fatimah, Associate Editor, Lackovic, Igor, Associate Editor, Rock, Emilio Sacristan, Associate Editor, Jarm, Tomaz, editor, Cvetkoska, Aleksandra, editor, Mahnič-Kalamiza, Samo, editor, and Miklavcic, Damijan, editor

Published
2021
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40. Addressing the clinical unmet needs in primary Sjögren’s Syndrome through the sharing, harmonization and federated analysis of 21 European cohorts

Author

Pezoulas, Vasileios C., Goules, Andreas, Kalatzis, Fanis, Chatzis, Luke, Kourou, Konstantina D., Venetsanopoulou, Aliki, Exarchos, Themis P., Gandolfo, Saviana, Votis, Konstantinos, Zampeli, Evi, Burmeister, Jan, May, Thorsten, Marcelino Pérez, Manuel, Lishchuk, Iryna, Chondrogiannis, Thymios, Andronikou, Vassiliki, Varvarigou, Theodora, Filipovic, Nenad, Tsiknakis, Manolis, Baldini, Chiara, Bombardieri, Michele, Bootsma, Hendrika, Bowman, Simon J., Soyfoo, Muhammad Shahnawaz, Parisis, Dorian, Delporte, Christine, Devauchelle-Pensec, Valérie, Pers, Jacques-Olivier, Dörner, Thomas, Bartoloni, Elena, Gerli, Roberto, Giacomelli, Roberto, Jonsson, Roland, Ng, Wan-Fai, Priori, Roberta, Ramos-Casals, Manuel, Sivils, Kathy, Skopouli, Fotini, Torsten, Witte, A. G. van Roon, Joel, Xavier, Mariette, De Vita, Salvatore, Tzioufas, Athanasios G., and Fotiadis, Dimitrios I.

Published
2022
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41. Virtual Hemodynamic Assessment of Coronary Lesions: The Advent of Functional Angiography and Coronary Imaging

Author

Nikopoulos, Sotirios, primary, Papafaklis, Michail I., additional, Tsompou, Panagiota, additional, Sakellarios, Antonis, additional, Siogkas, Panagiotis, additional, Sioros, Spyros, additional, Fotiadis, Dimitrios I., additional, Katsouras, Christos S., additional, Naka, Katerina K., additional, Nikas, Dimitrios, additional, and Michalis, Lampros, additional

Published
2024
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44. Empowering Community Dwelling Older Citizens to Improve Their Balance with a Novel Technology Platform

Author

Gatsios, Dimitrios, Bamiou, Doris Eva, Costafreda, Sergi, Georga, Eleni I., Kourou, Konstantina K., Exarchos, Themis, Tsiouris, Kostas M., Fotiadis, Dimitrios I., Magjarevic, Ratko, Series Editor, Ładyżyński, Piotr, Associate Editor, Ibrahim, Fatimah, Associate Editor, Lackovic, Igor, Associate Editor, Rock, Emilio Sacristan, Associate Editor, Henriques, Jorge, editor, Neves, Nuno, editor, and de Carvalho, Paulo, editor

Published
2020
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45. Computational Fluid Dynamics of Blood Flow at the Left Atrium and Left Atrium Appendage

Author

Grigoriadis, Grigoris I., Sakellarios, Antonis I., Naka, Katerina, Kosmidou, Ioanna, Ellis, Christopher, Michalis, Lampros K., Fotiadis, Dimitrios I., Magjarevic, Ratko, Series Editor, Ładyżyński, Piotr, Associate Editor, Ibrahim, Fatimah, Associate Editor, Lackovic, Igor, Associate Editor, Rock, Emilio Sacristan, Associate Editor, Henriques, Jorge, editor, Neves, Nuno, editor, and de Carvalho, Paulo, editor

Published
2020
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46. Utilizing Incremental Learning for the Prediction of Disease Outcomes Across Distributed Clinical Data: A Framework and a Case Study

Author

Pezoulas, Vasileios C., Exarchos, Themis P., Kourou, Konstantina D., Tzioufas, Athanasios G., De Vita, Salvatore, Fotiadis, Dimitrios I., Magjarevic, Ratko, Series Editor, Ładyżyński, Piotr, Associate Editor, Ibrahim, Fatimah, Associate Editor, Lackovic, Igor, Associate Editor, Rock, Emilio Sacristan, Associate Editor, Henriques, Jorge, editor, Neves, Nuno, editor, and de Carvalho, Paulo, editor

Published
2020
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47. A Risk Stratification Model for Early Cognitive Impairment After Diagnosis of Parkinson’s Disease

Author

Tsiouris, Kostas M., Konitsiotis, Spiros, Koutsouris, Dimitrios D., Fotiadis, Dimitrios I., Magjarevic, Ratko, Series Editor, Ładyżyński, Piotr, Associate Editor, Ibrahim, Fatimah, Associate Editor, Lackovic, Igor, Associate Editor, Rock, Emilio Sacristan, Associate Editor, Henriques, Jorge, editor, Neves, Nuno, editor, and de Carvalho, Paulo, editor

Published
2020
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48. Computational Models for Predicting Resilience Levels of Women with Breast Cancer

Author

Kourou, Konstantina, Kondylakis, Haridimos, Koumakis, Lefteris, Manikis, Georgios C., Marias, Kostas, Tsiknakis, Manolis, Simos, Panagiotis G., Karademas, Evangelos, Fotiadis, Dimitrios I., Magjarevic, Ratko, Series Editor, Ładyżyński, Piotr, Associate Editor, Ibrahim, Fatimah, Associate Editor, Lackovic, Igor, Associate Editor, Rock, Emilio Sacristan, Associate Editor, Henriques, Jorge, editor, Neves, Nuno, editor, and de Carvalho, Paulo, editor

Published
2020
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49. Modeling Functional Processes of Brain Tissue: An fMRI Study on Patients with Un-Medicated Late-Onset Restless Leg Syndrome

Author

Ntemou, Amalia K., Tripoliti, Evanthia E., Margariti, Persefoni N., Argyropoulou, Maria I., Fotiadis, Dimitrios I., Magjarevic, Ratko, Series Editor, Ładyżyński, Piotr, Associate Editor, Ibrahim, Fatimah, Associate Editor, Lackovic, Igor, Associate Editor, Rock, Emilio Sacristan, Associate Editor, Henriques, Jorge, editor, Neves, Nuno, editor, and de Carvalho, Paulo, editor

Published
2020
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50. 3-D Registration on Carotid Artery imaging data: MRI for different timesteps

Author

Bizopoulos, Paschalis A., Sakellarios, Antonis, Michalis, Lampros K., Koutsouris, Dimitrios D., and Fotiadis, Dimitrios I.

Subjects
Physics - Medical Physics
Abstract

A common problem which is faced by the researchers when dealing with arterial carotid imaging data is the registration of the geometrical structures between different imaging modalities or different timesteps. The use of the "Patient Position" DICOM field is not adequate to achieve accurate results due to the fact that the carotid artery is a relatively small structure and even imperceptible changes in patient position and/or direction make it difficult. While there is a wide range of simple/advanced registration techniques in the literature, there is a considerable number of studies which address the geometrical structure of the carotid artery without using any registration technique. On the other hand the existence of various registration techniques prohibits an objective comparison of the results using different registration techniques. In this paper we present a method for estimating the statistical significance that the choice of the registration technique has on the carotid geometry. One-Way Analysis of Variance(ANOVA) showed that the p-values were <0.0001 for the distances of the lumen from the centerline for both right and left carotids of the patient case that was studied., Comment: 4 pages, 4 figures, 1 table, preprint submitted to IEEE-EMBC 2016

Published
2016
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