Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Time Distributed Classification of Alzheimer’s Disease on MRI Scans(John Wiley and Sons Ltd, 2026) Dundar, Mehmet Sait; Yilmaz, BulentThe diagnosis of Alzheimer's disease (AD) has progressively depended on sophisticated neuroimaging methods alongside cognitive assessments. This study combines volumetric feature analysis with computational modeling techniques, focusing on spatial and temporal analysis, to categorize individuals as cognitively normal (CN), mild cognitive impairment (MCI), or AD using magnetic resonance imaging (MRI) data. In the initial phase, volumetric changes, comprising cortical thickness, white matter, grey matter, cerebrospinal fluid, and total intracranial volume, were derived from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset utilizing the CAT12 toolbox in statistical parametric mapping (SPM). Linear regression was utilized on these variables over time to create slopes that reflect volumetric change rates, which then served as inputs for machine learning classifiers. The slopes of cortical thickness exhibited the greatest classification accuracy, reaching 82.5% with a random forest model for differentiating AD from CN individuals. During the second phase, a deep learning methodology was utilized, relying solely on the MRI scans and excluding the outcomes from the first phase. A pre-trained 3D ResNet-101 convolutional neural network (CNN) model extracted spatial characteristics from MRI volumes, whereas long short-term memory (LSTM) networks recorded temporal dynamics across subsequent annual scans. This hybrid CNN-LSTM design markedly improved classification performance, attaining 96.7% accuracy for AD against CN and enhancing the distinction of MCI cases. Nonetheless, discrepancies in MCI categorization were chiefly ascribed to the restricted access to annual MRI data and the model's pre-training on CN and AD cohorts. These findings highlight the potential of integrating volumetric statistical analysis with deep learning for automated AD categorization. This work enhances neuroimaging diagnostic methods by utilizing both spatial and temporal MRI data, enabling early diagnosis and better evaluation of disease development.Article Citation - WoS: 9Citation - Scopus: 13The Impact and Future of Artificial Intelligence in Medical Genetics and Molecular Medicine: An Ongoing Revolution(Springer Heidelberg, 2024-08) Ozcelik, Firat; Dundar, Mehmet Sait; Yildirim, A. Baki; Henehan, Gary; Vicente, Oscar; Sanchez-Alcazar, Jose A.; Dundar, MunisArtificial intelligence (AI) platforms have emerged as pivotal tools in genetics and molecular medicine, as in many other fields. The growth in patient data, identification of new diseases and phenotypes, discovery of new intracellular pathways, availability of greater sets of omics data, and the need to continuously analyse them have led to the development of new AI platforms. AI continues to weave its way into the fabric of genetics with the potential to unlock new discoveries and enhance patient care. This technology is setting the stage for breakthroughs across various domains, including dysmorphology, rare hereditary diseases, cancers, clinical microbiomics, the investigation of zoonotic diseases, omics studies in all medical disciplines. AI's role in facilitating a deeper understanding of these areas heralds a new era of personalised medicine, where treatments and diagnoses are tailored to the individual's molecular features, offering a more precise approach to combating genetic or acquired disorders. The significance of these AI platforms is growing as they assist healthcare professionals in the diagnostic and treatment processes, marking a pivotal shift towards more informed, efficient, and effective medical practice. In this review, we will explore the range of AI tools available and show how they have become vital in various sectors of genomic research supporting clinical decisions.Article Citation - WoS: 6Citation - Scopus: 7Genetic Variants in Genes Correlated to the PI3K/AKT Pathway: The Role of ARAP3, CDH5, KIF and RELN Primary Lymphedema(International Society of Lymphology, 2024-08-28) Dundar, Mehmet Sait; Belanová, I.; Bonetti, Gabriele; Gelanová, V.; Kozáčiková, R.; Vešelényiová, Dominika; Donato, Kevin; Michelini, S.Genetic anomalies affecting lymphatic development and function can lead to lymphatic dysfunction, which could manifest as lymphedema- Understanding the signaling pathways governing lymphatics function is crucial for developing targeted diagnostic and therapeutic interventions. This study aims to characterize genetic variants in genes involved in the PUKIAKT signaling pathway, which plays a critical role in lymphangiogenesis. 408 patients diagnosed with primary lymphedema were sequenced usinga next-generation sequencing (NGS) gene panel composed of 28 diagnostic genes and 71 candidate genes. The analysis revealed six variants in genes RFLN, ARAP3,CDHS and K1F11. Five of these variants have never been reported in the literature. All these genes have been correlated to lymphatic activity and are involved in the P13K/AKT pathway. As the P13K/AKT signaling pathway plays an essential role in lymphangiogenesis and lymphatic function, genetic variants in genes correlated to this pathway could lead to lymphedema. Our findings underscore the potential of the P13K/AKT pathway in lymphedema pathogenesis, supporting the role of RELN,ARAP3,CDH5,and KIF11 as diagnostic and therapeutic targets. © 2024 Elsevier B.V., All rights reserved.Article 3D Sampling of K-Space With Non-Cartesian Trajectories in MR Imaging(Gazi Univ, Fac Engineering Architecture, 2025-02-03) Dundar, Mehmet Sait; Gumus, Kazim Z.; Yilmaz, BulentThis study presents an innovative approach to 3D k-space sampling in MR imaging using non-Cartesian concentric shell trajectories. The method involves 32 concentric shells of varying radii, allowing for rapid data acquisition through undersampling techniques. Simulations using IDEA software demonstrate that this approach can fill the k-space in less than one second, a significant time reduction compared to traditional FLASH sequences that can take 3-4 minutes. The concentric shell model enhances imaging efficiency by minimizing artifacts and ensuring uniform k-space filling, leading to higher resolution and faster scans. This technique shows promise for clinical applications, particularly in dynamic imaging scenarios such as acute stroke and pediatric radiology, where speed and precision are critical. As illustrated in Figure A, the concentric shell trajectories enable uniform k-space filling, significantly reducing scan times and improving image quality. These results are based on the simulations conducted with IDEA software.