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Zafeiropoulos N, Bitilis P, Tsekouras GE, Kotis K. Graph Neural Networks for Parkinson's Disease Monitoring and Alerting. Sensors (Basel) 2023; 23:8936. [PMID: 37960634 PMCID: PMC10648881 DOI: 10.3390/s23218936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/24/2023] [Revised: 10/27/2023] [Accepted: 10/30/2023] [Indexed: 11/15/2023]
Abstract
Graph neural networks (GNNs) have been increasingly employed in the field of Parkinson's disease (PD) research. The use of GNNs provides a promising approach to address the complex relationship between various clinical and non-clinical factors that contribute to the progression of PD. This review paper aims to provide a comprehensive overview of the state-of-the-art research that is using GNNs for PD. It presents PD and the motivation behind using GNNs in this field. Background knowledge on the topic is also presented. Our research methodology is based on PRISMA, presenting a comprehensive overview of the current solutions using GNNs for PD, including the various types of GNNs employed and the results obtained. In addition, we discuss open issues and challenges that highlight the limitations of current GNN-based approaches and identify potential paths for future research. Finally, a new approach proposed in this paper presents the integration of new tasks for the engineering of GNNs for PD monitoring and alert solutions.
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Affiliation(s)
| | | | | | - Konstantinos Kotis
- Intelligent Systems Laboratory, Department of Cultural Technology and Communication, University of the Aegean, 81100 Mytilene, Greece; (N.Z.); (P.B.); (G.E.T.)
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Chlorogiannis DD, Verras GI, Tzelepi V, Chlorogiannis A, Apostolos A, Kotis K, Anagnostopoulos CN, Antzoulas A, Davakis S, Vailas M, Schizas D, Mulita F. Tissue classification and diagnosis of colorectal cancer histopathology images using deep learning algorithms. Is the time ripe for clinical practice implementation? Prz Gastroenterol 2023; 18:353-367. [PMID: 38572457 PMCID: PMC10985751 DOI: 10.5114/pg.2023.130337] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 05/20/2023] [Indexed: 04/05/2024]
Abstract
Colorectal cancer is one of the most prevalent types of cancer, with histopathologic examination of biopsied tissue samples remaining the gold standard for diagnosis. During the past years, artificial intelligence (AI) has steadily found its way into the field of medicine and pathology, especially with the introduction of whole slide imaging (WSI). The main outcome of interest was the composite balanced accuracy (ACC) as well as the F1 score. The average reported ACC from the collected studies was 95.8 ±3.8%. Reported F1 scores reached as high as 0.975, with an average of 89.7 ±9.8%, indicating that existing deep learning algorithms can achieve in silico distinction between malignant and benign. Overall, the available state-of-the-art algorithms are non-inferior to pathologists for image analysis and classification tasks. However, due to their inherent uniqueness in their training and lack of widely accepted external validation datasets, their generalization potential is still limited.
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Affiliation(s)
| | | | - Vasiliki Tzelepi
- Department of Pathology, School of Medicine, University of Patras, Patras, Greece
| | | | - Anastasios Apostolos
- First Department of Cardiology, Hippokration Hospital, University of Athens, Athens, Greece
| | - Konstantinos Kotis
- Intelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, Mytilene, Greece
| | | | - Andreas Antzoulas
- Department of Surgery, General University Hospital of Patras, Patras, Greece
| | - Spyridon Davakis
- Upper Gastrointestinal and General Surgery Unit, First Department of Surgery, National and Kapodistrian University of Athens, Laiko General Hospital, Athens, Greece
| | - Michail Vailas
- Upper Gastrointestinal and General Surgery Unit, First Department of Surgery, National and Kapodistrian University of Athens, Laiko General Hospital, Athens, Greece
| | - Dimitrios Schizas
- Upper Gastrointestinal and General Surgery Unit, First Department of Surgery, National and Kapodistrian University of Athens, Laiko General Hospital, Athens, Greece
| | - Francesk Mulita
- Department of Surgery, General University Hospital of Patras, Patras, Greece
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Bousis D, Verras GI, Bouchagier K, Antzoulas A, Panagiotopoulos I, Katinioti A, Kehagias D, Kaplanis C, Kotis K, Anagnostopoulos CN, Mulita F. The role of deep learning in diagnosing colorectal cancer. Prz Gastroenterol 2023; 18:266-273. [PMID: 37937113 PMCID: PMC10626379 DOI: 10.5114/pg.2023.129494] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 02/24/2023] [Indexed: 11/09/2023]
Abstract
Colon cancer is a major public health issue, affecting a growing number of individuals worldwide. Proper and early diagnosis of colon cancer is the necessary first step toward effective treatment and/or prevention of future disease relapse. Artificial intelligence and its subtypes, deep learning in particular, tend nowadays to have an expanding role in all fields of medicine, and diagnosing colon cancer is no exception. This report aims to summarize the entire application spectrum of deep learning in all diagnostic tests regarding colon cancer, from endoscopy and histologic examination to medical imaging and screening serologic tests.
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Affiliation(s)
- Dimitrios Bousis
- Department of Internal Medicine, General University Hospital of Patras, Patras, Greece
| | | | | | - Andreas Antzoulas
- Department of Surgery, General University Hospital of Patras, Patras, Greece
| | | | | | - Dimitrios Kehagias
- Department of Surgery, General University Hospital of Patras, Patras, Greece
| | | | - Konstantinos Kotis
- Intelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, Mytilene, Greece
| | | | - Francesk Mulita
- Department of Surgery, General University Hospital of Patras, Patras, Greece
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Mulita F, Verras GI, Kotis K, Anagnostopoulos CN. Deep Learning for Colon Cancer: Our Experience and a Review of the Literature. European Journal of Surgical Oncology 2023. [DOI: 10.1016/j.ejso.2022.11.087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
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Mulita F, Verras GI, Anagnostopoulos CN, Kotis K. A Smarter Health through the Internet of Surgical Things. Sensors (Basel) 2022; 22:s22124577. [PMID: 35746359 PMCID: PMC9231158 DOI: 10.3390/s22124577] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 06/10/2022] [Accepted: 06/14/2022] [Indexed: 05/14/2023]
Abstract
(1) Background: In the last few years, technological developments in the surgical field have been rapid and are continuously evolving. One of the most revolutionizing breakthroughs was the introduction of the IoT concept within surgical practice. Our systematic review aims to summarize the most important studies evaluating the IoT concept within surgical practice, focusing on Telesurgery and surgical Telementoring. (2) Methods: We conducted a systematic review of the current literature, focusing on the Internet of Surgical Things in Telesurgery and Telementoring. Forty-eight (48) studies were included in this review. As secondary research questions, we also included brief overviews of the use of IoT in image-guided surgery, and patient Telemonitoring, by systematically analyzing fourteen (14) and nineteen (19) studies, respectively. (3) Results: Data from 219 patients and 757 healthcare professionals were quantitively analyzed. Study designs were primarily observational or based on model development. Palpable advantages from the IoT incorporation mainly include less surgical hours, accessibility to high quality treatment, and safer and more effective surgical education. Despite the described technological advances, and proposed benefits of the systems presented, there are still identifiable gaps in the literature that need to be further explored in a systematic manner. (4) Conclusions: The use of the IoT concept within the surgery domain is a widely incorporated but less investigated concept. Advantages have become palpable over the past decade, yet further research is warranted.
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Affiliation(s)
- Francesk Mulita
- Intelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, 81100 Mytilene, Greece;
- Department of Surgery, General University Hospital of Patras, 26504 Rio, Greece;
- Correspondence: (F.M.); (K.K.); Tel.: +30-6974822712 (K.K.)
| | | | | | - Konstantinos Kotis
- Intelligent Systems Lab, Department of Cultural Technology and Communication, University of the Aegean, 81100 Mytilene, Greece;
- Correspondence: (F.M.); (K.K.); Tel.: +30-6974822712 (K.K.)
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Tsekouras GE, Rigos A, Chatzistamatis S, Tsimikas J, Kotis K, Caridakis G, Anagnostopoulos CN. A Novel Approach to Image Recoloring for Color Vision Deficiency. Sensors (Basel) 2021; 21:2740. [PMID: 33924510 PMCID: PMC8069325 DOI: 10.3390/s21082740] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 04/07/2021] [Accepted: 04/09/2021] [Indexed: 11/16/2022]
Abstract
In this paper, a novel method to modify color images for the protanopia and deuteranopia color vision deficiencies is proposed. The method admits certain criteria, such as preserving image naturalness and color contrast enhancement. Four modules are employed in the process. First, fuzzy clustering-based color segmentation extracts key colors (which are the cluster centers) of the input image. Second, the key colors are mapped onto the CIE 1931 chromaticity diagram. Then, using the concept of confusion line (i.e., loci of colors confused by the color-blind), a sophisticated mechanism translates (i.e., removes) key colors lying on the same confusion line to different confusion lines so that they can be discriminated by the color-blind. In the third module, the key colors are further adapted by optimizing a regularized objective function that combines the aforementioned criteria. Fourth, the recolored image is obtained by color transfer that involves the adapted key colors and the associated fuzzy clusters. Three related methods are compared with the proposed one, using two performance indices, and evaluated by several experiments over 195 natural images and six digitized art paintings. The main outcomes of the comparative analysis are as follows. (a) Quantitative evaluation based on nonparametric statistical analysis is conducted by comparing the proposed method to each one of the other three methods for protanopia and deuteranopia, and for each index. In most of the comparisons, the Bonferroni adjusted p-values are <0.015, favoring the superiority of the proposed method. (b) Qualitative evaluation verifies the aesthetic appearance of the recolored images.
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Affiliation(s)
- George E. Tsekouras
- Department of Cultural Technology and Communications, University of the Aegean, 811 00 Mitilini, Greece; (A.R.); (S.C.); (K.K.); (G.C.); (C.-N.A.)
| | - Anastasios Rigos
- Department of Cultural Technology and Communications, University of the Aegean, 811 00 Mitilini, Greece; (A.R.); (S.C.); (K.K.); (G.C.); (C.-N.A.)
| | - Stamatis Chatzistamatis
- Department of Cultural Technology and Communications, University of the Aegean, 811 00 Mitilini, Greece; (A.R.); (S.C.); (K.K.); (G.C.); (C.-N.A.)
| | - John Tsimikas
- Department of Statistics and Actuarial-Financial Mathematics, University of the Aegean, 811 00 Mitilini, Greece;
| | - Konstantinos Kotis
- Department of Cultural Technology and Communications, University of the Aegean, 811 00 Mitilini, Greece; (A.R.); (S.C.); (K.K.); (G.C.); (C.-N.A.)
| | - George Caridakis
- Department of Cultural Technology and Communications, University of the Aegean, 811 00 Mitilini, Greece; (A.R.); (S.C.); (K.K.); (G.C.); (C.-N.A.)
| | - Christos-Nikolaos Anagnostopoulos
- Department of Cultural Technology and Communications, University of the Aegean, 811 00 Mitilini, Greece; (A.R.); (S.C.); (K.K.); (G.C.); (C.-N.A.)
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Abstract
ARTIST is a research approach introducing novel methods for real-time multi-entity interaction between human and non-human entities, to create reusable and optimized Mixed Reality (MR) experiences with low-effort, towards a Shared MR Experiences Ecosystem (SMRE2). As a result, ARTIST delivers high quality MR experiences, facilitating the interaction between a variety of entities which interact in a virtual and symbiotic way within a mega, virtual and fully-experiential world. Specifically, ARTIST aims to develop novel methods for low-effort (code-free) implementation and deployment of open and reusable MR content, applications and tools, introducing the novel concept of an Experience as a Trajectory (EaaT). In addition, ARTIST will provide tools for the tracking, monitoring and analysis of user behaviour and their interaction with the environment and with other users, towards optimizing MR experiences by recommending their reconfiguration, dynamically (at run-time) or statically (at development time). Finally, it will provide tools for synthesizing experiences into new mega and still reconfigurable EaaTs, enhancing them at the same time using semantically integrated related data/information available in disparate and heterogeneous resources.
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Kotis K. A reAl-time low-effoRt mulTi-entity Interaction System for creaTing reusable and optimized MR experiences. RIO 2019. [DOI: 10.3897/rio.5.e36128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
ARTIST is a research approach introducing novel methods for real-time multi-entity interaction between human and non-human entities, to create reusable and optimized Mixed Reality (MR) experiences with low-effort, towards a Shared MR Experiences Ecosystem (SMRE2). As a result, ARTIST delivers high quality MR experiences, facilitating the interaction between a variety of entities which interact in a virtual and symbiotic way within a mega, virtual and fully-experiential world. Specifically, ARTIST aims to develop novel methods for low-effort (code-free) implementation and deployment of open and reusable MR content, applications and tools, introducing the novel concept of an Experience as a Trajectory (EaaT). In addition, ARTIST will provide tools for the tracking, monitoring and analysis of user behaviour and their interaction with the environment and with other users, towards optimizing MR experiences by recommending their reconfiguration, dynamically (at run-time) or statically (at development time). Finally, it will provide tools for synthesizing experiences into new mega and still reconfigurable EaaTs, enhancing them at the same time using semantically integrated related data/information available in disparate and heterogeneous resources.
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Abstract
Internet of Things should be able to integrate an extremely large amount of distributed and heterogeneous entities. To tackle heterogeneity, these entities will need to be consistently and formally represented and managed (registered, aligned, composed and queried) trough suitable abstraction technologies. Two distinct types of these entities are a) sensing/actuating devices that observe some features of interest or act on some other entities (call it ‘smart entities’), and b) applications that utilize the data sensed from or sent to the smart entities (call it ‘control entities’). The aim of this paper is to present the Semantic Smart Gateway Framework for supporting semantic interoperability between these types of heterogeneous IoT entities. More specifically, the paper describes an ontology as the key technology for the abstraction and semantic registration of these entities, towards supporting their automated deployment. The paper also described the alignment of IoT entities and of their exchanged messages. More important, the paper presents a use case scenario and a proof-of-concept implementation.
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Karetsos S, Haralampopoulos D, Kotis K. An Ontology-Based Framework for Authoring Tools in the Domain of Sustainable Energy Education. International Journal of Agricultural and Environmental Information Systems 2011. [DOI: 10.4018/jaeis.2011010103] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
This paper presents an ontology-based framework for the production of learning designs, focusing on the domain of sustainable energy education. An ontology of the sustainable energy development domain and educational model designed in compliance with widespread standards constitute the proposed framework to disprove the vagueness of “sustainable development” and enhance reusability and shareability of learning material respectively. The authors envisage this framework both as a means to support the authoring of learning scenarios and as a provisioning of a field for conversation about the appropriate authoring tool in this area. In this sense, an integrating approach for the representation of the learning design domain is kept, focusing on the concept of “mediating artefacts”.
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