Publications by authors named "H O Raaschou"
Background: Good outcomes in stroke care require swift diagnostics, for which magnetic resonance imaging (MRI) as first-line brain imaging is superior to computed tomography scans. Reduced length of stay (LOS) in hospital and emergency departments (ED) may optimize resource use. Fast-track stroke MRI was implemented as the primary imaging technique for suspected stroke, in the ED at Copenhagen University Hospital-Herlev and Gentofte in 2020.
View Article and Find Full Text PDFObjective: To create a scalable and feasible retrospective consecutive knee osteoarthritis (OA) radiographic database with limited human labor using commercial and custom-built artificial intelligence (AI) tools.
Methods: We applied four AI tools, two commercially available and two custom-built tools, to analyze 6 years of clinical consecutive knee radiographs from patients aged 35-79 at the University of Copenhagen Hospital, Bispebjerg-Frederiksberg Hospital, Denmark. The tools provided Kellgren-Lawrence (KL) grades, joint space widths, patella osteophyte detection, radiographic view detection, knee joint implant detection, and radiographic marker detection.
Article Synopsis
- This study aimed to evaluate the performance of a commercial AI tool in detecting acute brain ischemia on MRI compared to a seasoned neuroradiologist.
- Researchers analyzed MRIs from 1030 patients suspected of a stroke, ultimately focusing on 995 for their assessment.
- Results showed the AI tool had high sensitivity (89%) and specificity (90%) for detecting ischemic lesions, although performance varied with lesion size and image quality.
The first patient was misclassified in the diagnostic conclusion according to a local clinical expert opinion in a new clinical implementation of a knee osteoarthritis artificial intelligence (AI) algorithm at Bispebjerg-Frederiksberg University Hospital, Copenhagen, Denmark. In preparation for the evaluation of the AI algorithm, the implementation team collaborated with internal and external partners to plan workflows, and the algorithm was externally validated. After the misclassification, the team was left wondering: what is an acceptable error rate for a low-risk AI diagnostic algorithm? A survey among employees at the Department of Radiology showed significantly lower acceptable error rates for AI (6.
View Article and Find Full Text PDFRational And Objectives: This study investigated how an AI tool impacted radiologists reading time for non-contrast chest CT exams.
Materials And Methods: An AI tool was implemented into the PACS reading workflow of non-contrast chest CT exams between April and May 2020. The reading time was recorded for one CONSULTANT RADIOLOGIST and one RADIOLOGY RESIDENT by an external observer.