Zobrazeno 1 - 10
of 28
pro vyhledávání: '"Kerstin Ritter"'
Publikováno v:
Frontiers in Aging Neuroscience, Vol 16 (2024)
Machine Learning (ML) is considered a promising tool to aid and accelerate diagnosis in various medical areas, including neuroimaging. However, its success is set back by the lack of large-scale public datasets. Indeed, medical institutions possess a
Externí odkaz:
https://doaj.org/article/3e5bf3c8c4f346f8a3a6a72130b77d46
Autor:
Marta Oliveira, Rick Wilming, Benedict Clark, Céline Budding, Fabian Eitel, Kerstin Ritter, Stefan Haufe
Publikováno v:
Frontiers in Artificial Intelligence, Vol 7 (2024)
Convolutional Neural Networks (CNNs) are frequently and successfully used in medical prediction tasks. They are often used in combination with transfer learning, leading to improved performance when training data for the task are scarce. The resultin
Externí odkaz:
https://doaj.org/article/eaf3c82921f043d6bc7005abefaf1dbf
Publikováno v:
Cell Reports, Vol 43, Iss 1, Pp 113597- (2024)
Summary: This study examines the impact of sample size on predicting cognitive and mental health phenotypes from brain imaging via machine learning. Our analysis shows a 3- to 9-fold improvement in prediction performance when sample size increases fr
Externí odkaz:
https://doaj.org/article/916f9b7a662240369d99447f9ba59acf
Autor:
Malte Klingenberg, Didem Stark, Fabian Eitel, Céline Budding, Mohamad Habes, Kerstin Ritter, for the Alzheimer’s Disease Neuroimaging Initiative
Publikováno v:
Alzheimer’s Research & Therapy, Vol 15, Iss 1, Pp 1-13 (2023)
Abstract Introduction Although machine learning classifiers have been frequently used to detect Alzheimer’s disease (AD) based on structural brain MRI data, potential bias with respect to sex and age has not yet been addressed. Here, we examine a s
Externí odkaz:
https://doaj.org/article/5a680cba0c1246c8a22359e22a42f2a8
Autor:
Marc-Andre Schulz, Stefan Hetzer, Fabian Eitel, Susanna Asseyer, Lil Meyer-Arndt, Tanja Schmitz-Hübsch, Judith Bellmann-Strobl, James H. Cole, Stefan M. Gold, Friedemann Paul, Kerstin Ritter, Martin Weygandt
Publikováno v:
iScience, Vol 26, Iss 9, Pp 107679- (2023)
Summary: Clinical and neuroscientific studies suggest a link between psychological stress and reduced brain health in health and neurological disease but it is unclear whether mediating pathways are similar. Consequently, we applied an arterial-spin-
Externí odkaz:
https://doaj.org/article/dfc4848e2c224cfcb083a15e33d1ba18
Autor:
Carina Nina Vorisek, Caroline Stellmach, Paula Josephine Mayer, Sophie Anne Ines Klopfenstein, Dominik Martin Bures, Anke Diehl, Maike Henningsen, Kerstin Ritter, Sylvia Thun
Publikováno v:
Journal of Medical Internet Research, Vol 25, p e41089 (2023)
BackgroundResources are increasingly spent on artificial intelligence (AI) solutions for medical applications aiming to improve diagnosis, treatment, and prevention of diseases. While the need for transparency and reduction of bias in data and algori
Externí odkaz:
https://doaj.org/article/9ab27f7f99cf4d1b8116445b7d454d3f
Autor:
Di Wang, Nicolas Honnorat, Peter T. Fox, Kerstin Ritter, Simon B. Eickhoff, Sudha Seshadri, Mohamad Habes
Publikováno v:
NeuroImage, Vol 269, Iss , Pp 119929- (2023)
Deep neural networks currently provide the most advanced and accurate machine learning models to distinguish between structural MRI scans of subjects with Alzheimer’s disease and healthy controls. Unfortunately, the subtle brain alterations capture
Externí odkaz:
https://doaj.org/article/b3c5cc6b9fe24fc8bff3a569a4368263
Autor:
Lea Fast, Uchralt Temuulen, Kersten Villringer, Anna Kufner, Huma Fatima Ali, Eberhard Siebert, Shufan Huo, Sophie K. Piper, Pia Sophie Sperber, Thomas Liman, Matthias Endres, Kerstin Ritter
Publikováno v:
Frontiers in Neurology, Vol 14 (2023)
BackgroundAccurate prediction of clinical outcomes in individual patients following acute stroke is vital for healthcare providers to optimize treatment strategies and plan further patient care. Here, we use advanced machine learning (ML) techniques
Externí odkaz:
https://doaj.org/article/75790871a1454ce1acae9bcd32d13fec
Autor:
Roshan Prakash Rane, Milena Philomena Maria Musial, Anne Beck, Michael Rapp, Florian Schlagenhauf, Tobias Banaschewski, Arun L.W. Bokde, Marie-Laure Paillère Martinot, Eric Artiges, Frauke Nees, Herve Lemaitre, Sarah Hohmann, Gunter Schumann, Henrik Walter, Andreas Heinz, Kerstin Ritter
Publikováno v:
NeuroImage: Clinical, Vol 40, Iss , Pp 103520- (2023)
Binge drinking behavior in early adulthood can be predicted from brain structure during early adolescence with an accuracy of above 70%. We investigated whether this accurate prospective prediction of alcohol misuse behavior can be explained by psych
Externí odkaz:
https://doaj.org/article/c9717a3e7341433e8af349cace1c7b98
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-13 (2021)
Abstract Convolutional neural networks (CNNs)—as a type of deep learning—have been specifically designed for highly heterogeneous data, such as natural images. Neuroimaging data, however, is comparably homogeneous due to (1) the uniform structure
Externí odkaz:
https://doaj.org/article/e03dc7614987450f983266f90941ca1c