Konstantinos Margetis

Oct 30, 2024 | nature.com | Konstantinos Margetis

AbstractThis study aims to develop and evaluate radiomics-based machine learning (ML) models for predicting meningioma grades using multiparametric magnetic resonance imaging (MRI). The study utilized the BraTS-MEN dataset’s training split, including 698 patients (524 with grade 1 and 174 with grade 2–3 meningiomas). We extracted 4872 radiomic features from T1, T1 with contrast, T2, and FLAIR MRI sequences using PyRadiomics. LASSO regression reduced features to 176.

Nov 1, 2023 | mdpi.com | Burak B. Ozkara |Mert Karabacak |Konstantinos Margetis |Vivek S. Yedavalli

1. IntroductionThe quantity of scholarly articles is consistently increasing, demonstrating a yearly growth rate of 4% in publications and 1.8% in the number of references per publication [1]. The continuous increase in scientific output makes it challenging for researchers, as they need significant time to gather and understand these results.

Oct 2, 2023 | jamanetwork.com | Mert Karabacak |Konstantinos Margetis

To the Editor We are writing in response to the Viewpoint by Finlayson et al.1 While the authors highlight the potential of machine learning (ML) in clinical research, we would like to address some points that warrant further discussion. First, the authors present the dichotomy between ML and statistics as a false one, suggesting that ML methods are not inherently distinct from statistical methods.

Sep 14, 2023 | paperity.org | Mert Karabacak |Konstantinos Margetis

PLOS ONE, Jul 2023 By predicting short-term postoperative outcomes before surgery, patients who undergo posterior cervical fusion (PCF) surgery may benefit from more precise patient care plans that reduce the likelihood of unfavorable outcomes. We developed machine learning models for predicting short-term postoperative outcomes and incorporate these models into an open-source web application in this study.

Jul 21, 2023 | journals.plos.org | Mert Karabacak |Konstantinos Margetis

Loading metrics Open Access Peer-reviewedResearch Article AbstractBy predicting short-term postoperative outcomes before surgery, patients who undergo posterior cervical fusion (PCF) surgery may benefit from more precise patient care plans that reduce the likelihood of unfavorable outcomes. We developed machine learning models for predicting short-term postoperative outcomes and incorporate these models into an open-source web application in this study.