Papers by Serafeim Moustakidis
Aim: To test the feasibility and accuracy of a new attention-based deep learning (DL) method for ... more Aim: To test the feasibility and accuracy of a new attention-based deep learning (DL) method for right ventricular (RV) quantification using 2D echocardiography (2DE) with cardiac magnetic resonance imaging (CMR) as reference. Methods and results: We retrospectively analyzed images from 50 adult patients (median age 51, interquartile range 32-62 42% women) who had undergone CMR within 1 month of 2DE. RV planimetry of the myocardial border was performed in end-diastole (ED) and end-systole (ES) for 8 standardized 2DE RV views with calculation of areas. The DL model comprised a Feature Tokenizer module and a stack of Transformer layers. Age, gender and calculated areas were used as inputs, and the output was RV volume in ED/ES. The dataset was randomly split into training, validation and testing subsets (35, 5 and 10 patients respectively). Mean RVEDV, RVESV and RV ejection fraction (EF) were 163±70ml, 82±42ml and 51±8% respectively without differences among the subsets. The proposed ...
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Stroke remains a predominant cause of mortality and disability worldwide. The endeavor to diagnos... more Stroke remains a predominant cause of mortality and disability worldwide. The endeavor to diagnose stroke through biomechanical time-series data coupled with Artificial Intelligence (AI) poses a formidable challenge, especially amidst constrained participant numbers. The challenge escalates when dealing with small datasets, a common scenario in preliminary medical research. While recent advances have ushered in few-shot learning algorithms adept at handling sparse data, this paper pioneers a distinctive methodology involving a visualization-centric approach to navigating the small-data challenge in diagnosing stroke survivors based on gait-analysis-derived biomechanical data. Employing Siamese neural networks (SNNs), our method transforms a biomechanical time series into visually intuitive images, facilitating a unique analytical lens. The kinematic data encapsulated comprise a spectrum of gait metrics, including movements of the ankle, knee, hip, and center of mass in three dimensi...
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Expert Systems With Applications, Mar 1, 2023
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Lecture Notes in Computer Science, 2023
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Zenodo (CERN European Organization for Nuclear Research), Jul 22, 2021
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Clinical transplantation, Jan 18, 2021
BackgroundRecent studies demonstrated safety and efficacy of heart transplantation (HT) from hepa... more BackgroundRecent studies demonstrated safety and efficacy of heart transplantation (HT) from hepatitis C virus (HCV)‐positive donors. We sought to evaluate the impact of HCV donor status on the outcomes of patients undergoing HT in the United States.MethodsWe analyzed a retrospective cohort of adult patients from the United Network for Organ Sharing (UNOS) database who underwent isolated HT from 2015 until present. Primary outcomes were 30‐day and 1‐year overall mortality. Secondary outcomes included risk for graft failure and overall survival, incident stroke and need for dialysis during the available follow‐up period. All end points were evaluated according to HCV status.ResultsAll‐cause 30‐day and 1‐year mortality was similar between the two groups (3.4% vs 3.2%, P = .973 and 6.9% vs 7.8%, P = .769, respectively, for patients receiving heart grafts from HCV+ vs. HCV− donors). Graft failure was 12.8% (95% CI: 8%‐19%) and 15.2% (95 CI: 15%‐16%) in the HCV+ and HCV− groups, respectively (P = .92 and P = .68). Competing risk regression analysis for re‐operation showed a non‐significant trend for higher risk for re‐transplantation in the HCV+ group (HR: 2.71; 95% CI: 0.83, 8.80, P = .097).ConclusionHCV donor status does not seem to negatively affect the outcomes of HT in the U.S population.
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Medical Engineering & Physics, Dec 1, 2010
A novel fuzzy decision tree-based SVM (FDT-SVM) classifier is proposed in this paper, to distingu... more A novel fuzzy decision tree-based SVM (FDT-SVM) classifier is proposed in this paper, to distinguish between asymptotic (AS) and osteoarthritis (OA) knee gait patterns and to investigate OA severity using 3-D ground reaction force (GRF) measurements. FDT-SVM incorporates effective techniques for feature selection (FS) and class grouping (CG) at each non-leaf nodes of the tree structure, which reduce the overall complexity of DT building and alleviate the overfitting effect. The embedded FS and CG are based on the notion of fuzzy partition vector (FPV) that comprises the fuzzy membership degrees of every pattern in their target classes, serving as a local evaluation metric with respect to patterns. FS is driven by a fuzzy complementary criterion (FuzCoC) which assures that features are iteratively introduced, providing the maximum additional contribution in regard to the information content given by the previously selected features. A novel Wavelet Packet (WP) decomposition based on the FuzCoC principles is also introduced, to distinguish informative and complementary features from GRF data. The quality of our method is validated in terms of statistical metrics drawn by confusion matrices, such as sensitivity, specificity and total classification accuracy. In addition, we investigate the impact of each GRF component. Finally, comparative results with existing techniques are given, demonstrating the efficacy of the suggested approach.
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2022 International Joint Conference on Neural Networks (IJCNN), Jul 18, 2022
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Journal of Control and Decision, Nov 6, 2022
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Research Square (Research Square), Aug 10, 2021
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Medical Physics, Aug 6, 2019
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Applied sciences, Jul 12, 2023
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Zenodo (CERN European Organization for Nuclear Research), Jun 19, 2023
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Scientific Reports, Apr 24, 2023
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Computer Methods in Biomechanics and Biomedical Engineering, Jun 1, 2012
An efficient wavelet-based feature selection (FS) method is proposed in this paper for subject re... more An efficient wavelet-based feature selection (FS) method is proposed in this paper for subject recognition using ground reaction force measurements. Our approach relies on a local fuzzy evaluation measure with respect to patterns that reveal the adequacy of data coverage for each feature. Furthermore, FS is driven by a fuzzy complementary criterion (FuzCoC) which assures that those features are iteratively introduced, providing the maximum additional contribution with regard to the information content given by the previously selected features. On the basis of the principles of FuzCoC, we develop two novel techniques. At Stage 1, wavelet packet (WP) decomposition of gaits is accomplished to obtain a set of discriminating frequency sub-bands. A computationally simple FS method is then applied at Stage 2, providing a compact set of powerful and complementary features, from WP coefficients. The quality of our approach is validated via comparative analysis against existing methods on gait recognition.
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Every year, the rate at which technology is applied on areas of our everyday life is increasing a... more Every year, the rate at which technology is applied on areas of our everyday life is increasing at a steady pace. This rapid development drives the technology companies to design and fabricate their integrated circuits (ICs) in non-trustworthy outsourcing foundries in order to reduce the cost. Thus, a synchronous form of virus, known as Hardware Trojans (HTs), was developed. HTs leak encrypted information, degrade device performance or lead to total destruction. To reduce the risks associated with these viruses, various approaches have been developed aiming to prevent and detect them, based on conventional or machine learning methods. Ideally, any undesired modification made to an IC should be detectable by pre-silicon verification/simulation and post-silicon testing. The infected circuit can be inserted in different stages of the manufacturing process, rendering the detection of HTs a complicated procedure. In this paper, we present a comprehensive review of research dedicated to applications based on Machine Learning for the detection of HTs in ICs. The literature is categorized in (a) reverse-engineering development for the imaging phase, (b) real-time detection, (c) golden model-free approaches, (d) detection based on gate-level netlists features and (e) classification approaches.
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Elsevier eBooks, 2023
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BioMed, Dec 27, 2022
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Computational Materials Science, Mar 1, 2023
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Papers by Serafeim Moustakidis