Author

A. Marka

5 papers indexed here

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access Sep 2026

Assessment of emphysema with simulated low-dose dark-field radiography

Dark-field radiography allows the visualization of lung tissue changes associated with pulmonary emphysema. Simulations were used to investigate the impact of radiation dose reduction on the diagnostic accuracy of emphysema detection. Dark-field and attenuation chest radiographs acquired during an ongoing clinical stud...

H. Bast, Rafael C. Schick, T. Koehler et al. · European Radiology Experimen... · 0 citations

Open access Oct 2026

Multidisciplinary attitudes towards LLM-structured oncologic CT reports: a prospective fixed-sequence questionnaire study

Large language models (LLMs) can automate the conversion of radiologic free text into structured reports, but multidisciplinary reader preferences for the resulting output remain insufficiently studied. This prospective, two-phase, fixed-sequence within-participant study evaluated reader assessments of LLM-structured v...

Felix Busch, A. Marka, Lilly F. Wingberg et al. · Cancer Imaging · 0 citations

Open access Sep 2026

Correlation of CT-Derived Quantitative Image Features and Inflammatory Laboratory Markers with Length of Hospital Stay in Patients with Pyelonephritis

Background/Objectives: To assess associations between quantitative computed tomography (CT) features, inflammatory markers, and length of hospital stay (LOS) in acute pyelonephritis (APN). Methods: This retrospective single-center study included 82 patients with CT-confirmed APN. Two radiologists quantified renal perfu...

Markus M. Graf, T. Lemke, A. Marka et al. · Diagnostics · 0 citations

Review Open access Aug 2026

Artificial Intelligence-Assisted Chest Radiography: A Prospective Crossover Multi-Reader Study on Diagnostic Performance and Workflow Efficiency.

RATIONALE AND OBJECTIVES To evaluate the impact of four commercially available AI solutions for chest radiography on diagnostic performance, workflow efficiency, and clinical decision-making in a real-world setting. MATERIALS AND METHODS In this prospective, monocentric, crossover reader study, five readers (one to s...

T. Lemke, A. Marka, P. Prucker et al. · Academic Radiology · 0 citations

Open access Jul 2026

The New Munich Elbow Soft Tissue (MEST) classification is a reliable MRI-based injury severity grading system for acute simple elbow dislocations.

The MEST classification represents a comprehensive and reliable classification for injury patterns after acute SED, offering additional guidance in clinical decision-making and showed substantial to excellent intrarater reliability.

Anja M. Wackerle, R. Vieider, Pavel M. Kadantsev et al. · Knee Surgery, Sports Traumat... · 0 citations