Publications by authors named "H Handels"
Background: Measures of cortical topology are believed to characterize large-scale cortical networks. Previous studies used region of interest (ROI)-based approaches with predefined templates that limit analyses to linear pair-wise interactions between regions. As cortical topology is inherently complex, a non-linear dynamic model that measures the brain complexity at the voxel level is suggested to characterize topological complexities of brain regions and cortical folding.
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- The study investigates survival analysis methods for lung cancer using data from the Schleswig-Holstein Cancer Registry, comparing traditional Cox regression with newer machine learning methods such as Random Survival Forests and neural networks.
- Results indicate that the Cox Proportional Hazard model performs best when using the cancer stage classification, while the Random Survival Forests excel when considering additional tumor characteristics like size and metastasis.
- The findings highlight the importance of these models for providing insights into patient survival, aiding physicians in making better treatment decisions, and ultimately enhancing patient outcomes.
Objectives: In this paper, an artificial intelligence-based algorithm for predicting the optimal contrast medium dose for computed tomography (CT) angiography of the aorta is presented and evaluated in a clinical study. The prediction of the contrast dose reduction is modelled as a classification problem using the image contrast as the main feature.
Methods: This classification is performed by random decision forests (RDF) and k-nearest-neighbor methods (KNN).
This work aims to recognize the patient individual possibility of contrast dose reduction in CT angiography. This system should help to identify whether the dose of contrast agent in CT angiography can be reduced to avoid side effects. In a clinical study, 263 CT angiographies were performed and, in addition, 21 clinical parameters were recorded for each patient before contrast agent administration.
View Article and Find Full Text PDFBackground: Structural MRI studies in people with first-episode psychosis (FEP) and those in the clinical high-risk (CHR) state have consistently shown volumetric abnormalities that depict changes in the structural complexity of the cortical boundary. The aim of the present study was to employ chaos analysis in the identification of people with psychosis based on the structural complexity of the cortical boundary and subcortical areas.
Methods: We performed chaos analysis of the grey matter distribution on structural MRIs.