Publications by authors named "Eline Langius-Wiffen"
Purpose: To evaluate the diagnostic performance and generalizability of the winning DL algorithm of the RSNA 2020 PE detection challenge to a local population using CTPA data from two hospitals.
Materials And Methods: Consecutive CTPA images from patients referred for suspected PE were retrospectively analysed. The winning RSNA 2020 DL algorithm was retrained on the RSNA-STR Pulmonary Embolism CT (RSPECT) dataset.
Article Synopsis
- The study aimed to evaluate the diagnostic accuracy of a deep learning AI for detecting cervical spine fractures on CT scans, comparing it to the performance of attending radiologists and identifying fractures requiring stabilizing therapy.
- A total of 2,368 scans were analyzed, revealing that AI had a sensitivity of 71.5% and detected many fractures missed by radiologists, while radiologists had a higher sensitivity of 88.2% but missed fewer fractures in need of stabilizing therapy.
- The findings suggest that while the AI missed more fractures overall, it was able to identify some that radiologists missed, including several critical injuries needing intervention.
Objectives: Virtual monochromatic images (VMI) are increasingly used in clinical practice as they improve contrast-to-noise ratio. However, due to their different appearances, the performance of artificial intelligence (AI) trained on conventional CT images may worsen. The goal of this study was to assess the performance of an established AI algorithm trained on conventional polychromatic computed tomography (CT) images (CPI) to detect pulmonary embolism (PE) on VMI.
View Article and Find Full Text PDFObjectives: The purpose of this study was to evaluate the incremental value of artificial intelligence (AI) compared to the diagnostic accuracy of radiologists alone in detecting incidental acute pulmonary embolism (PE) on routine portal venous contrast-enhanced chest computed tomography (CT).
Methods: CTs of 3089 consecutive patients referred to the radiology department for a routine contrast-enhanced chest CT between 27-5-2020 and 31-12-2020, were retrospectively analysed by a CE-certified and FDA-approved AI algorithm. The diagnostic performance of the AI was compared to the initial report.
Purpose: To generate and extend the evidence on the clinical validity of an artificial intelligence (AI) algorithm to detect acute pulmonary embolism (PE) on CT pulmonary angiography (CTPA) of patients suspected of PE and to evaluate the possibility of reducing the risk of missed findings in clinical practice with AI-assisted reporting.
Methods: Consecutive CTPA scan data of 3316 patients referred because of suspected PE between 24-2-2018 and 31-12-2020 were retrospectively analysed by a CE-certified and FDA-approved AI algorithm. The output of the AI was compared with the attending radiologists' report.