Abstract #0598
AI-based Automated Liver Image Prescription: Evaluation across Patients and Pathologies and Prospective Implementation and Validation
Ruiqi Geng1,2, Collin J. Buelo1,2, Mahalakshmi Sundaresan3, Jitka Starekova1, Nikolaos Panagiotopoulos1,4, Thekla Helene Oechtering1,4, Edward M. Lawrence1, Marcin Ignaciuk1, Scott B. Reeder1,2,5,6,7, and Diego Hernando1,2,3,7
1Radiology, University of Wisconsin-Madison, Madison, WI, United States, 2Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, 3Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI, United States, 4Radiology and Nuclear Medicine, Universität zu Lübeck, Lübeck, Germany, 5Medicine, University of Wisconsin-Madison, Madison, WI, United States, 6Emergency Medicine, University of Wisconsin-Madison, Madison, WI, United States, 7Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, United States
Synopsis
An automated AI-based method for liver image prescription from a localizer was recently proposed. In this work, this AI method was further evaluated in a larger retrospective patient cohort (1,039 patients for training/testing), across pathologies, field strengths, and against radiologists’ inter-reader reproducibility performance. AI-based 3D axial prescription achieved a S/I shift of <2.3 cm compared to manual prescription for 99.5% of test dataset. The AI method performed well across all sub-cohorts and better in 3D axial prescription than radiologists’ inter-reader reproducibility performance. The AI method was successfully implemented on a clinical MR system and showed robust performance across localizer sequences.
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