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Article: Fuzzy Cognitive Map Applications in Medicine over the Last Two Decades: A Review Study.

Apostolopoulos, Ioannis D / Papandrianos, Nikolaos I / Papathanasiou, Nikolaos D / Papageorgiou, Elpiniki I

2024 Volume 11, Issue 2

Abstract: Fuzzy Cognitive Maps (FCMs) have become an invaluable tool for healthcare providers because they can capture intricate associations among variables and generate precise predictions. FCMs have demonstrated their utility in diverse medical applications, ... ...

Abstract	Fuzzy Cognitive Maps (FCMs) have become an invaluable tool for healthcare providers because they can capture intricate associations among variables and generate precise predictions. FCMs have demonstrated their utility in diverse medical applications, from disease diagnosis to treatment planning and prognosis prediction. Their ability to model complex relationships between symptoms, biomarkers, risk factors, and treatments has enabled healthcare providers to make informed decisions, leading to better patient outcomes. This review article provides a thorough synopsis of using FCMs within the medical domain. A systematic examination of pertinent literature spanning the last two decades forms the basis of this overview, specifically delineating the diverse applications of FCMs in medical realms, including decision-making, diagnosis, prognosis, treatment optimisation, risk assessment, and pharmacovigilance. The limitations inherent in FCMs are also scrutinised, and avenues for potential future research and application are explored.
Language	English
Publishing date	2024-01-30
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2746191-9
ISSN	2306-5354
ISSN	2306-5354
DOI	10.3390/bioengineering11020139
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: A Deep Learning Methodology for the Detection of Abnormal Parathyroid Glands via Scintigraphy with

Apostolopoulos, Ioannis D / Papathanasiou, Nikolaos D / Apostolopoulos, Dimitris J

Diseases (Basel, Switzerland)

2022 Volume 10, Issue 3

Abstract: Background: Parathyroid proliferative disorder encompasses a wide spectrum of diseases, including parathyroid adenoma (PTA), parathyroid hyperplasia, and parathyroid carcinoma. Imaging modalities that deliver their results preoperatively help in the ... ...

Abstract	Background: Parathyroid proliferative disorder encompasses a wide spectrum of diseases, including parathyroid adenoma (PTA), parathyroid hyperplasia, and parathyroid carcinoma. Imaging modalities that deliver their results preoperatively help in the localisation of parathyroid glands (PGs) and assist in surgery. Artificial intelligence and, more specifically, image detection methods, can assist medical experts and reduce the workload in their everyday routine. Methods: The present study employs an innovative CNN topology called ParaNet, to analyse early MIBI, late MIBI, and TcO4 thyroid scan images simultaneously to perform first-level discrimination between patients with abnormal PGs (aPG) and patients with normal PGs (nPG). The study includes 632 parathyroid scans. Results: ParaNet exhibits a top performance, reaching an accuracy of 96.56% in distinguishing between aPG and nPG scans. Its sensitivity and specificity are 96.38% and 97.02%, respectively. PPV and NPV values are 98.76% and 91.57%, respectively. Conclusions: The proposed network is the first to introduce the automatic discrimination of PG and nPG scans acquired by scintigraphy with
Language	English
Publishing date	2022-08-23
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2720869-2
ISSN	2079-9721
ISSN	2079-9721
DOI	10.3390/diseases10030056
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Article ; Online: Applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging: A review.

Apostolopoulos, Ioannis D / Papathanasiou, Nikolaos D / Apostolopoulos, Dimitris J / Panayiotakis, George S

European journal of nuclear medicine and molecular imaging

2022 Volume 49, Issue 11, Page(s) 3717–3739

Abstract: Purpose: This paper reviews recent applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging. Recent advances in Deep Learning (DL) and GANs catalysed the research of their applications in medical imaging ... ...

Abstract	Purpose: This paper reviews recent applications of Generative Adversarial Networks (GANs) in Positron Emission Tomography (PET) imaging. Recent advances in Deep Learning (DL) and GANs catalysed the research of their applications in medical imaging modalities. As a result, several unique GAN topologies have emerged and been assessed in an experimental environment over the last two years. Methods: The present work extensively describes GAN architectures and their applications in PET imaging. The identification of relevant publications was performed via approved publication indexing websites and repositories. Web of Science, Scopus, and Google Scholar were the major sources of information. Results: The research identified a hundred articles that address PET imaging applications such as attenuation correction, de-noising, scatter correction, removal of artefacts, image fusion, high-dose image estimation, super-resolution, segmentation, and cross-modality synthesis. These applications are presented and accompanied by the corresponding research works. Conclusion: GANs are rapidly employed in PET imaging tasks. However, specific limitations must be eliminated to reach their full potential and gain the medical community's trust in everyday clinical practice.
MeSH term(s)	Artifacts ; Humans ; Image Processing, Computer-Assisted/methods ; Positron-Emission Tomography/methods
Language	English
Publishing date	2022-04-22
Publishing country	Germany
Document type	Journal Article ; Review
ZDB-ID	8236-3
ISSN	1619-7089 ; 0340-6997 ; 1619-7070
ISSN (online)	1619-7089
ISSN	0340-6997 ; 1619-7070
DOI	10.1007/s00259-022-05805-w
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Article ; Online: Fuzzy Cognitive Maps

Ioannis D. Apostolopoulos / Peter P. Groumpos

Applied Sciences, Vol 13, Iss 3412, p

Their Role in Explainable Artificial Intelligence

2023 Volume 3412

Abstract: Currently, artificial intelligence is facing several problems with its practical implementation in various application domains. The explainability of advanced artificial intelligence algorithms is a topic of paramount importance, and many discussions ... ...

Abstract	Currently, artificial intelligence is facing several problems with its practical implementation in various application domains. The explainability of advanced artificial intelligence algorithms is a topic of paramount importance, and many discussions have been held recently. Pioneering and classical machine learning and deep learning models behave as black boxes, constraining the logical interpretations that the end users desire. Artificial intelligence applications in industry, medicine, agriculture, and social sciences require the users’ trust in the systems. Users are always entitled to know why and how each method has made a decision and which factors play a critical role. Otherwise, they will always be wary of using new techniques. This paper discusses the nature of fuzzy cognitive maps (FCMs), a soft computational method to model human knowledge and provide decisions handling uncertainty. Though FCMs are not new to the field, they are evolving and incorporate recent advancements in artificial intelligence, such as learning algorithms and convolutional neural networks. The nature of FCMs reveals their supremacy in transparency, interpretability, transferability, and other aspects of explainable artificial intelligence (XAI) methods. The present study aims to reveal and defend the explainability properties of FCMs and to highlight their successful implementation in many domains. Subsequently, the present study discusses how FCMs cope with XAI directions and presents critical examples from the literature that demonstrate their superiority. The study results demonstrate that FCMs are both in accordance with the XAI directives and have many successful applications in domains such as medical decision-support systems, precision agriculture, energy savings, environmental monitoring, and policy-making for the public sector.
Keywords	fuzzy cognitive maps ; explainable artificial intelligence ; interpretability ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
Subject code	401
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A General Machine Learning Model for Assessing Fruit Quality Using Deep Image Features

Ioannis D. Apostolopoulos / Mpesi Tzani / Sokratis I. Aznaouridis

AI, Vol 4, Iss 4, Pp 812-

2023 Volume 830

Abstract: Fruit quality is a critical factor in the produce industry, affecting producers, distributors, consumers, and the economy. High-quality fruits are more appealing, nutritious, and safe, boosting consumer satisfaction and revenue for producers. Artificial ... ...

Abstract	Fruit quality is a critical factor in the produce industry, affecting producers, distributors, consumers, and the economy. High-quality fruits are more appealing, nutritious, and safe, boosting consumer satisfaction and revenue for producers. Artificial intelligence can aid in assessing the quality of fruit using images. This paper presents a general machine learning model for assessing fruit quality using deep image features. This model leverages the learning capabilities of the recent successful networks for image classification called vision transformers (ViT). The ViT model is built and trained with a combination of various fruit datasets and taught to distinguish between good and rotten fruit images based on their visual appearance and not predefined quality attributes. The general model demonstrated impressive results in accurately identifying the quality of various fruits, such as apples (with a 99.50% accuracy), cucumbers (99%), grapes (100%), kakis (99.50%), oranges (99.50%), papayas (98%), peaches (98%), tomatoes (99.50%), and watermelons (98%). However, it showed slightly lower performance in identifying guavas (97%), lemons (97%), limes (97.50%), mangoes (97.50%), pears (97%), and pomegranates (97%).
Keywords	fruit quality ; machine learning ; deep learning ; Electronic computers. Computer science ; QA75.5-76.95
Subject code	006
Language	English
Publishing date	2023-09-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Advanced fuzzy cognitive maps: state-space and rule-based methodology for coronary artery disease detection.

Apostolopoulos, Ioannis D / Groumpos, Peter P / Apostolopoulos, Dimitris J

Biomedical physics & engineering express

2021 Volume 7, Issue 4

Abstract: According to the World Health Organization, 50% of deaths in European Union are caused by Cardiovascular Diseases (CVD), while 80% of premature heart diseases and strokes can be prevented. In this study, a Computer-Aided Diagnostic model for a precise ... ...

Abstract	According to the World Health Organization, 50% of deaths in European Union are caused by Cardiovascular Diseases (CVD), while 80% of premature heart diseases and strokes can be prevented. In this study, a Computer-Aided Diagnostic model for a precise diagnosis of Coronary Artery Disease (CAD) is proposed. The methodology is based on State Space Advanced Fuzzy Cognitive Maps (AFCMs), an evolution of the traditional Fuzzy Cognitive Maps. Also, a rule-based mechanism is incorporated, to further increase the knowledge of the proposed system and the interpretability of the decision mechanism. The proposed method is evaluated utilizing a CAD dataset from the Department of Nuclear Medicine of the University Hospital of Patras, in Greece. Several experiments are conducted to define the optimal parameters of the proposed AFCM. Furthermore, the proposed AFCM is compared with the traditional FCM approach and the literature. The experiments highlight the effectiveness of the AFCM approach, obtaining 85.47% accuracy in CAD diagnosis, showing an improvement of +7% over the traditional approach. It is demonstrated that the AFCM approach in developing Fuzzy Cognitive Maps outperforms the conventional approach, while it constitutes a reliable method for the diagnosis of Coronary Artery Disease.
MeSH term(s)	Algorithms ; Cognition ; Computer Simulation ; Coronary Artery Disease/diagnosis ; Fuzzy Logic ; Humans
Language	English
Publishing date	2021-05-19
Publishing country	England
Document type	Journal Article
ISSN	2057-1976
ISSN (online)	2057-1976
DOI	10.1088/2057-1976/abfd83
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Book ; Online: Experimenting with Convolutional Neural Network Architectures for the automatic characterization of Solitary Pulmonary Nodules' malignancy rating

Apostolopoulos, Ioannis D.

2020

Abstract: Lung Cancer is the most common cause of cancer-related death worldwide. Early and automatic diagnosis of Solitary Pulmonary Nodules (SPN) in Computer Tomography (CT) chest scans can provide early treatment as well as doctor liberation from time-consuming ...

Abstract	Lung Cancer is the most common cause of cancer-related death worldwide. Early and automatic diagnosis of Solitary Pulmonary Nodules (SPN) in Computer Tomography (CT) chest scans can provide early treatment as well as doctor liberation from time-consuming procedures. Deep Learning has been proven as a popular and influential method in many medical imaging diagnosis areas. In this study, we consider the problem of diagnostic classification between benign and malignant lung nodules in CT images derived from a PET/CT scanner. More specifically, we intend to develop experimental Convolutional Neural Network (CNN) architectures and conduct experiments, by tuning their parameters, to investigate their behavior, and to define the optimal setup for the accurate classification. For the experiments, we utilize PET/CT images obtained from the Laboratory of Nuclear Medicine of the University of Patras, and the publically available database called Lung Image Database Consortium Image Collection (LIDC-IDRI). Furthermore, we apply simple data augmentation to generate new instances and to inspect the performance of the developed networks. Classification accuracy of 91% and 93% on the PET/CT dataset and on a selection of nodule images form the LIDC-IDRI dataset, is achieved accordingly. The results demonstrate that CNNs are a trustworth method for nodule classification. Also, the experiment confirms that data augmentation enhances the robustness of the CNNs. Comment: 22 pages. arXiv admin note: text overlap with arXiv:1409.4842, arXiv:1512.07108 by other authors
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; Physics - Data Analysis ; Statistics and Probability ; Physics - Medical Physics
Subject code	006
Publishing date	2020-03-15
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Explainable Artificial Intelligence Method (ParaNet+) Localises Abnormal Parathyroid Glands in Scintigraphic Scans of Patients with Primary Hyperparathyroidism

Dimitris J. Apostolopoulos / Ioannis D. Apostolopoulos / Nikolaos D. Papathanasiou / Trifon Spyridonidis / George S. Panayiotakis

Algorithms, Vol 16, Iss 435, p

2023 Volume 435

Abstract: The pre-operative localisation of abnormal parathyroid glands (PG) in parathyroid scintigraphy is essential for suggesting treatment and assisting surgery. Human experts examine the scintigraphic image outputs. An assisting diagnostic framework for ... ...

Abstract	The pre-operative localisation of abnormal parathyroid glands (PG) in parathyroid scintigraphy is essential for suggesting treatment and assisting surgery. Human experts examine the scintigraphic image outputs. An assisting diagnostic framework for localisation reduces the workload of physicians and can serve educational purposes. Former studies from the authors suggested a successful deep learning model, but it produced many false positives. Between 2010 and 2020, 648 participants were enrolled in the Department of Nuclear Medicine of the University Hospital of Patras, Greece. An innovative modification of the well-known VGG19 network (ParaNet+) is proposed to classify scintigraphic images into normal and abnormal classes. The Grad-CAM++ algorithm is applied to localise the abnormal PGs. An external dataset of 100 patients imaged at the same department who underwent parathyroidectomy in 2021 and 2022 was used for evaluation. ParaNet+ agreed with the human readers, showing 0.9861 on a patient-level and 0.8831 on a PG-level basis under a 10-fold cross-validation on the training set of 648 participants. Regarding the external dataset, the experts identified 93 of 100 abnormal patient cases and 99 of 118 surgically excised abnormal PGs. The human-reader false-positive rate (FPR) was 10% on a PG basis. ParaNet+ identified 99/100 abnormal cases and 103/118 PGs, with an 11.2% FPR. The model achieved higher sensitivity on both patient and PG bases than the human reader (99.0% vs. 93% and 87.3% vs. 83.9%, respectively), with comparable FPRs. Deep learning can assist in detecting and localising abnormal PGs in scintigraphic scans of patients with primary hyperparathyroidism and can be adapted to the everyday routine.
Keywords	deep learning ; explainable artificial intelligence ; parathyroid glands ; hyperparathyroidism ; Industrial engineering. Management engineering ; T55.4-60.8 ; Electronic computers. Computer science ; QA75.5-76.95
Language	English
Publishing date	2023-09-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Innovative Attention-Based Explainable Feature-Fusion VGG19 Network for Characterising Myocardial Perfusion Imaging SPECT Polar Maps in Patients with Suspected Coronary Artery Disease

Ioannis D. Apostolopoulos / Nikolaοs D. Papathanasiou / Nikolaos Papandrianos / Elpiniki Papageorgiou / Dimitris J. Apostolopoulos

Applied Sciences, Vol 13, Iss 8839, p

2023 Volume 8839

Abstract: Greece is among the European Union members topping the list of deaths related to coronary artery disease. Myocardial Perfusion Imaging (MPI) with Single-Photon Emission Computed Tomography (SPECT) is a non-invasive test used to detect abnormalities in ... ...

Abstract	Greece is among the European Union members topping the list of deaths related to coronary artery disease. Myocardial Perfusion Imaging (MPI) with Single-Photon Emission Computed Tomography (SPECT) is a non-invasive test used to detect abnormalities in CAD screening. The study proposes an explainable deep learning (DL) method for characterising MPI SPECT Polar Map images in patients with suspected CAD. Patient data were recorded at the Department of Nuclear Medicine of the University Hospital of Patras from 16 February 2018 to 28 February 2022. The final study population included 486 patients. An attention-based feature-fusion network (AFF-VGG19) was proposed to perform the diagnosis, and the Grad-CAM++ algorithm was employed to reveal potentially significant regions. AFF-VGG19’s agreement with the medical experts was found to be 89.92%. When training and assessing using the ICA findings as a reference, AFF-VGG19 achieved good diagnostic strength (accuracy of 0.789) similar to that of the human expert (0.784) and with more balanced sensitivity and specificity rates (0.873 and 0.722, respectively) compared to the human expert (0.958 and 0.648, respectively). The visual inspection of the Grad-CAM++ regions showed that the model produced 77 meaningful explanations over the 100 selected samples, resulting in a slight accuracy decrease (0.77). In conclusion, this research introduced a novel and interpretable DL approach for characterising MPI SPECT Polar Map images in patients with suspected CAD. The high agreement with medical experts, robust diagnostic performance, and meaningful interpretability of the model support the notion that attention-based networks hold significant promise in CAD screening and may revolutionise medical decision-making in the near future.
Keywords	explainable artificial intelligence ; coronary artery disease ; Myocardial Perfusion Imaging ; Polar Maps ; deep learning ; Technology ; T ; Engineering (General). Civil engineering (General) ; TA1-2040 ; Biology (General) ; QH301-705.5 ; Physics ; QC1-999 ; Chemistry ; QD1-999
Subject code	610
Language	English
Publishing date	2023-07-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Deep Learning Methods to Reveal Important X-ray Features in COVID-19 Detection

Ioannis D. Apostolopoulos / Dimitris J. Apostolopoulos / Nikolaos D. Papathanasiou

Reports, Vol 5, Iss 20, p

Investigation of Explainability and Feature Reproducibility

2022 Volume 20

Abstract: X-ray technology has been recently employed for the detection of the lethal human coronavirus disease 2019 (COVID-19) as a timely, cheap, and helpful ancillary method for diagnosis. The scientific community evaluated deep learning methods to aid in the ... ...

Abstract	X-ray technology has been recently employed for the detection of the lethal human coronavirus disease 2019 (COVID-19) as a timely, cheap, and helpful ancillary method for diagnosis. The scientific community evaluated deep learning methods to aid in the automatic detection of the disease, utilizing publicly available small samples of X-ray images. In the majority of cases, the results demonstrate the effectiveness of deep learning and suggest valid detection of the disease from X-ray scans. However, little has been investigated regarding the actual findings of deep learning through the image process. In the present study, a large-scale dataset of pulmonary diseases, including COVID-19, was utilized for experiments, aiming to shed light on this issue. For the detection task, MobileNet (v2) was employed, which has been proven very effective in our previous works. Through analytical experiments utilizing feature visualization techniques and altering the input dataset classes, it was suggested that MobileNet (v2) discovers important image findings and not only features. It was demonstrated that MobileNet (v2) is an effective, accurate, and low-computational-cost solution for distinguishing COVID-19 from 12 various other pulmonary abnormalities and normal subjects. This study offers an analysis of image features extracted from MobileNet (v2), aiming to investigate the validity of those features and their medical importance. The pipeline can detect abnormal X-rays with an accuracy of 95.45 ± 1.54% and can distinguish COVID-19 with an accuracy of 89.88 ± 3.66%. The visualized results of the Grad-CAM algorithm provide evidence that the methodology identifies meaningful areas on the images. Finally, the detected image features were reproducible in 98% of the times after repeating the experiment for three times.
Keywords	deep learning ; COVID-19 ; explainable artificial intelligence ; Medicine (General) ; R5-920 ; Medical physics. Medical radiology. Nuclear medicine ; R895-920
Subject code	006
Language	English
Publishing date	2022-05-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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