Granular Machine Learning-Based Computed Tomography Contrast Phase Prediction
This DL+RF framework enables automated granular renal contrast phase discrimination and reduces inter-rater variability, representing a meaningful advancement in artificial intelligence-assisted abdominal CT interpretation and supporting improved patient care.
Authors
- Bradley J. Erickson (ORCID: https://orcid.org/0000-0001-7926-6095)
- Timothy L. Kline (ORCID: https://orcid.org/0000-0002-7917-9853)
- Hillary W. Garner (ORCID: https://orcid.org/0000-0002-6699-9692)
- Naoki Takahashi (ORCID: https://orcid.org/0000-0002-7946-6078)
- Michael F. Romero (ORCID: https://orcid.org/0000-0002-1555-7835)
- Bardia Khosravi (ORCID: https://orcid.org/0000-0002-8024-339X)
- Seyed Moein Rassoulinejad-Mousavi (ORCID: https://orcid.org/0000-0001-6569-5905)
- John Charles Lieske (ORCID: https://orcid.org/0000-0002-0202-5944)
- A. Weston
- Ryan T. Moerer
- Aaron E. Carretero Benites
Institutions
- Mayo Clinic (US)
- Jacksonville College (US)
- WinnMed (US)
- Mayo Clinic in Arizona (US)
- Mayo Clinic in Florida (US)
Publication Details
- Journal
- Mayo Clinic Proceedings Digital Health
- Published
- 2025-12-20
- DOI
- https://doi.org/10.1016/j.mcpdig.2025.100332
- Primary Topic
- Advanced X-ray and CT Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00