Testing remote feedback using a virtual semi-automated educational tool for the detection of pancreatic tumour-vessel contact on staging CT
Virtual expert feedback improved resident sensitivity in identifying tumour-vessel contact without compromising specificity or accuracy. These findings support the use of semi-automated educational tools to enhance radiology training efficiency and effectiveness.
Authors
- Daniel McCarthy
- Cameron Dawson
- Robert Policelli
- Salma Dammak (ORCID: https://orcid.org/0000-0003-2927-338X)
- Courtney Abbott
- Darryl Ramsewak
- Vibhuti Kalia
- Henry Madubuobi
- Zahra Kassam (ORCID: https://orcid.org/0000-0002-2098-1550)
- Indranil Balki (ORCID: https://orcid.org/0000-0002-5230-0393)
- David Wang (ORCID: https://orcid.org/0000-0003-2277-4608)
- Stefan Knezevic
- Aaron D. Ward
- Harry Marshall
- Imran Ladak
- Abdulrahman Nadrah
Institutions
- Western University (CA)
- Radboud University Nijmegen (NL)
- London Health Sciences Centre (CA)
- King Faisal Specialist Hospital & Research Centre (SA)
- Radboud University Medical Center (NL)
- St Joseph's Health Care (CA)
- Vanderbilt University Medical Center (US)
Publication Details
- Journal
- Current Problems in Diagnostic Radiology
- Published
- 2025-10-01
- DOI
- https://doi.org/10.1067/j.cpradiol.2025.10.007
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00