Publications by authors named "J Sosna"
Purpose: Radiological follow-up of oncology patients requires the detection of metastatic lung lesions and the quantitative analysis of their changes in longitudinal imaging studies. Our aim was to evaluate SimU-Net, a novel deep learning method for the automatic analysis of metastatic lung lesions and their temporal changes in pairs of chest CT scans.
Materials And Methods: SimU-Net is a simultaneous multichannel 3D U-Net model trained on pairs of registered prior and current scans of a patient.
Background: Ensuring appropriate computed tomography (CT) utilization optimizes patient care while minimizing radiation exposure. Decision support tools show promise for standardizing appropriateness.
Objectives: In the current study, we aimed to assess CT appropriateness rates using the European Society of Radiology (ESR) iGuide criteria across seven European countries.
Insights from a web-based questionnaire: examining diagnostic procedures prior to magnetic resonance imaging.
Isr J Health Policy Res
September 2024
The appropriate use of diagnostic imaging, particularly MRI, is a critical concern in modern healthcare. This paper examines the current state of MRI utilization in Israel, drawing on a recent study by Kaim et al. that surveyed 557 Israeli adults who underwent MRI in the public health system.
View Article and Find Full Text PDFArticle Synopsis
- Doctors need to keep an eye on cancer patients by checking for changes in their body scans, which can be tricky and takes a lot of time.
- A new method has been developed to help doctors find and analyze these changes more easily, using a cool graph-based system.
- When tested, this method showed it could find almost all missed or incorrectly identified issues in the scans, helping doctors make better decisions about patient care.
Article Synopsis
- This study looks at how to better find mistakes when doctors look at cancer scans over time, so they don't miss or wrongly identify tumors.
- It tested two ways of checking the scans: one done by experienced doctors and another one using a computer program that helps find problems automatically.
- The results showed that both methods found a lot of missed and misidentified tumors, with the computer method being especially good at it, helping doctors evaluate cancer better.