Publications by authors named "R STOLL"
Objectives: Urolithiasis, a common and painful urological condition, is influenced by factors such as lifestyle, genetics, and medication. Differentiating between different types of kidney stones is crucial for personalized therapy. The purpose of this study is to investigate the use of photon-counting computed tomography (PCCT) in combination with radiomics and machine learning to develop a method for automated and detailed characterization of kidney stones.
View Article and Find Full Text PDFBackground: For treatment of urolithiasis, the stone composition is of particular interest, as uric acid (UA) stones can be treated by chemolitholysis. In this ex vivo study, we employed an advanced composition analysis approach for urolithiasis utilizing spectral data obtained from a photon-counting detector CT (PCDCT) to differentiate UA and non-UA stones. Our primary objective was to assess the accuracy of this analysis method.
View Article and Find Full Text PDFArticle Synopsis
- - The pilot study aims to create machine learning models that can predict BMI changes for up to 5 years after bariatric surgery, improving preoperative obesity treatment and patient care.
- - Conducted from January 2012 to December 2021 in Switzerland, the study involved analyzing data from over 1,100 patients who underwent obesity surgeries, focusing on those with complete pre and postoperative information.
- - The results show reliable BMI predictions with low root mean square error values, highlighting the study's effectiveness in forecasting weight outcomes and the development of a web-based calculator for healthcare professionals.
Viruses often use ion channel proteins to initialise host infections. Defects in ion channel proteins are also linked to several metabolic disorders in humans. In that instance, modulation of ion channel activities becomes central to development of antiviral therapies and drug design.
View Article and Find Full Text PDFPurpose: This ex vivo study aimed to compare a newly developed dual-source photon-counting CT (PCCT) with a 3rd generation dual-source dual-energy CT (DECT) for the detection and measurement (stone lengths and volumetrics) of urinary stones.
Methods: 143 urinary stones with a known geometry were physically measured and defined as reference values. Next, urinary stones were placed in an anthropomorphic abdomen-model and were scanned with DECT and PCCT.