René Hosch

Exchange of High-Frequency Medical Monitoring Data Using Fast Healthcare Interoperability Resources for Interoperable Integration Into Clinical Practice and Research: Methodological Development Study

Abstract Background Continuous patient monitoring generates vast volumes of high-frequency monitoring data, such as electrocardiogram, electroencephalography, and invasive blood pressure tracings, that are often persisted in heterogeneous, noninteroperable formats, limiting integ...

CADS: a comprehensive anatomical dataset and segmentation for whole-body anatomy in computed tomography

Abstract Volumetric computed tomography (CT) segmentation supports quantitative image analysis, but most AI-based methods remain organ-specific, with fragmented models, inconsistent label definitions, and heterogeneous training and evaluation protocols. Here we present CADS, a un...

Topogram-based anatomical labelling of CT series: anatomy-aware CT data processing using deep learning

Abstract Objectives This study provides a Rapid Analysis and Processing of Image Data (RAPID) framework that combines deep learning-based CT topogram analysis with DICOM spatial geometry to enable reliable anatomical labelling of CT series independent of inconsistent textual meta...

A machine learning–derived sarcopenia index is associated with survival and nonrelapse mortality in DLBCL

Abstract Body composition analysis (BCA) provides an objective assessment of metabolic states, but its prognostic value in diffuse large B-cell lymphoma (DLBCL) remains unclear. We applied machine learning–supported BCA to computed tomography imaging from patients with newly diag...