A5037399251- Radiomics and Machine Learning in Medical Imaging
- Advanced X-ray and CT Imaging
- Medical Imaging Techniques and Applications
- Artificial Intelligence in Healthcare and Education
- COVID-19 diagnosis using AI
- COVID-19 and healthcare impacts
- COVID-19 Clinical Research Studies
- Radiation Dose and Imaging
- Venous Thromboembolism Diagnosis and Management
- Long-Term Effects of COVID-19
- Radiology practices and education
- Medical Imaging and Analysis
- Acute Ischemic Stroke Management
- Lung Cancer Diagnosis and Treatment
- Shoulder Injury and Treatment
- Orthopedic Surgery and Rehabilitation
- Signaling Pathways in Disease
- SARS-CoV-2 and COVID-19 Research
- Cerebrovascular and Carotid Artery Diseases
- Trypanosoma species research and implications
- Ultrasound in Clinical Applications
- Multiple Myeloma Research and Treatments
- Orthopedic Infections and Treatments
- Nuclear Physics and Applications
- Radiopharmaceutical Chemistry and Applications
University of Cologne
2021-2025
University Hospital Cologne
2020-2025
Integrated Oncology (United States)
2024
Heinrich Heine University Düsseldorf
2024
Düsseldorf University Hospital
2024
University of Bonn
2024
University Hospital Bonn
2024
Centrum für Integrierte Onkologie
2023
University of Münster
2018
Background Errors in radiology reports may occur because of resident-to-attending discrepancies, speech recognition inaccuracies, and large workload. Large language models, such as GPT-4 (ChatGPT; OpenAI), assist generating reports. Purpose To assess effectiveness identifying common errors reports, focusing on performance, time, cost-efficiency. Materials Methods In this retrospective study, 200 (radiography cross-sectional imaging [CT MRI]) were compiled between June 2023 December at one...
Compressed sensing accelerates magnetic resonance imaging (MRI) acquisition by undersampling of the k-space. Yet, excessive impairs image quality when using conventional reconstruction techniques. Deep-learning-based methods might allow for stronger and thus faster MRI scans without loss crucial quality. We compared approaches parallel (SENSE), a combination compressed (COMPRESSED SENSE, CS), CS deep-learning-based (CS AI) on raw k-space data acquired at different factors. 3D T2-weighted...
Background To investigate the feasibility of large language model (LLM) ChatGPT for classifying liver lesions according to Liver Imaging Reporting and Data System (LI-RADS) based on MRI reports, compare classification performance structured vs. unstructured reports. Methods LI-RADS classifiable were included from German written reports with report size, location, arterial phase contrast enhancement as minimum inclusion requirements. The findings sections propagated (GPT-3.5), which was...
Background The latest advancement of artificial intelligence (AI) is generative pretrained transformer large language models (LLMs). They have been trained on massive amounts text, enabling humanlike and semantical responses to text-based inputs requests. Foreshadowing numerous possible applications in various fields, the potential such tools for medical data integration clinical decision-making not yet clear. Objective In this study, we investigate LLMs report-based example acute ischemic...
Low-dose computed tomography (LDCT) of the chest is a recommended diagnostic tool in early stage COVID-19 pneumonia. High age, several comorbidities as well poor physical fitness can negatively influence outcome within infection. We investigated whether ratio fat to muscle area, measured initial LDCT, predict severe progression follow-up period.We analyzed 58 individuals with confirmed infection that underwent an LDCT one two included centers due Using waist circumference per paravertebral...
Abstract Objectives To demonstrate the feasibility of an automated, non-invasive approach to estimate bone marrow (BM) infiltration multiple myeloma (MM) by dual-energy computed tomography (DECT) after virtual non-calcium (VNCa) post-processing. Methods Individuals with MM and monoclonal gammopathy unknown significance (MGUS) concurrent DECT BM biopsy between May 2018 July 2020 were included in this retrospective observational study. Two pathologists three radiologists reported presence...
To evaluate the association between coronavirus disease 2019 (COVID-19) and post-inflammatory emphysematous lung alterations on follow-up low-dose CT scans.Consecutive patients with proven COVID-19 infection a were retrospectively reviewed. The severity of pulmonary involvement was classified as mild, moderate severe. Total volume, emphysema volume ratio emphysema/-to-lung quantified semi-automatically compared inter-individually initial to control group healthy, age- sex-matched patients....
Radiological reporting is transitioning to quantitative analysis, requiring large-scale multi-center validation of biomarkers. A major prerequisite and bottleneck for this task the voxelwise annotation image data, which time-consuming large cohorts. In study, we propose an iterative training workflow support facilitate such segmentation tasks, specifically high-resolution thoracic CT data.
Abstract The COVID-19 pandemic has worldwide individual and socioeconomic consequences. Chest computed tomography been found to support diagnostics disease monitoring. A standardized approach generate, collect, analyze, share clinical imaging information in the highest quality possible is urgently needed. We developed systematic, computer-assisted context-guided electronic data capture on FDA-approved mint Lesion TM software platform enable cloud-based collection real-time analysis....
The extent of lung involvement in coronavirus disease 2019 (COVID-19) pneumonia, quantified on computed tomography (CT), is an established biomarker for prognosis and guides clinical decision-making. standard semi-quantitative scoring by experienced reader. We aim to compare the performance automated deep-learning- threshold-based methods manual scoring. Further, we investigate optimal threshold quantification involved COVID pneumonia chest CT, using a multi-center dataset.In total 250...
Life expectancy of patients with multiple myeloma (MM) has increased over the past decades, underlining importance local tumor control and avoidance dose-dependent side effects palliative radiotherapy (RT). Virtual noncalcium (VNCa) imaging from dual-energy computed tomography (DECT) been suggested to estimate cellularity metabolic activity lytic bone lesions (LBLs) in MM.To explore feasibility RT response monitoring DECT-derived VNCa attenuation measurements MM.Thirty-three 85 LBLs that had...
Purpose Cardiovascular comorbidity anticipates severe progression of COVID-19 and becomes evident by coronary artery calcification (CAC) on low-dose chest computed tomography (LDCT). The purpose this study was to predict a patient’s obligation intensive care treatment evaluating the calcium burden initial diagnostic LDCT. Methods Eighty-nine consecutive patients with parallel LDCT positive RT-PCR for SARS-CoV-2 were included from three centers. primary endpoint admission ICU, tracheal...
Background: in magnetic resonance imaging (MRI), automated detection of brain metastases with convolutional neural networks (CNN) represents an extraordinary challenge due to small lesions sometimes posing as vessels well other confounders. Literature reporting high false positive rates when using conventional contrast enhanced (CE) T1 sequences questions their usefulness clinical routine. CE black blood (BB) may overcome these limitations by suppressing contrast-enhanced structures, thus...
Background Cardiovascular comorbidity anticipates poor prognosis of SARS-CoV-2 disease (COVID-19) and correlates with the systemic atherosclerotic transformation arterial vessels. The amount aortic wall calcification (AWC) can be estimated on low-dose chest CT. We suggest quantification AWC CT, which is initially performed for diagnosis COVID-19, to screen patients at risk severe COVID-19. Methods Seventy consecutive (46 in center 1, 24 2) parallel CT positive RT-PCR were included our...
Virtual non-calcium (VNCa) images from dual-energy computed tomography (DECT) have shown high potential to diagnose bone marrow disease of the spine, which is frequently disguised by dense trabecular on conventional CT. In this study, we aimed define reference values for VNCa spine in a large-scale cohort healthy individuals. DECT was performed after resection malignant skin tumor without evidence metastatic disease. Image analysis fully automated and did not require specific user...
Background: Diagnosing a coronavirus disease 2019 (COVID-19) infection with high specificity in chest computed tomography (CT) imaging is considered possible due to distinctive features of COVID-19 pneumonia. Since other viral non-COVID pneumonia show mostly different distribution pattern, it reasonable assume that the patterns observed caused by novel severe acute respiratory syndrome 2 (SARS-CoV-2) are consequence its genetically encoded molecular properties when interacting tissue. As...
The presented work explores the regulatory influence of upstream open reading frames (uORFs) on gene expression in Trypanosoma congolense. More than 31,000 uORFs total were identified and characterized here. We found evidence for uORFs’ appearance transcriptome to be correlated with proteomic data, clearly indicating their repressive potential T. congolense, which has rely post-transcriptional regulation due its unique genomic organization. Our data show that uORF’s translation does not only...
Purpose Due to the increasing number of COVID-19 infections since spring 2020 patient care workflow underwent changes in Germany. To minimize face-to-face exposure and reduce infection risk, non-time-critical elective medical procedures were postponed. Since ultrasound examinations include often can be substituted by other imaging modalities not requiring direct contact, has declined significantly. The aim this study is quantify baseline years before, during, early post-pandemic period...
Zielsetzung Virtual noncalcium (VNCa) imaging from dual-energy computed tomography (DECT) has been suggested to estimate cellularity and metabolic activity of lytic bone lesions (LBLs) in MM. We aimed explore the feasibility RT response monitoring with DECT-derived VNCa attenuation measurements