Abstract #0598

AI-based Automated Liver Image Prescription: Evaluation across Patients and Pathologies and Prospective Implementation and Validation

Ruiqi Geng^1,2, Collin J. Buelo^1,2, Mahalakshmi Sundaresan³, Jitka Starekova¹, Nikolaos Panagiotopoulos^1,4, Thekla Helene Oechtering^1,4, Edward M. Lawrence¹, Marcin Ignaciuk¹, Scott B. Reeder^1,2,5,6,7, and Diego Hernando^1,2,3,7

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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