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Updated: Oct 4, 2026
Human Liver Microphysiological System for Assessing Drug-Induced Liver Toxicity In Vitro
Published on: January 31, 2022
Multicenter, Multivendor Development and Validation of Automated Liver Prescription
Garrett C Fullerton1,2, Jitka Starekova1, Collin J Buelo3
1Department of Radiology, University of Wisconsin-Madison, Madison, Wisconsin, USA.
Background:
Manual prescription of liver MRI volumes introduces inter-operator variability and prolongs exam duration. Artificial intelligence (AI)-based prescription has been demonstrated, but existing work has been limited to single-vendor settings.
Purpose:
To develop and validate an AI model for automated liver MRI prescription using a large, consecutive, multicenter dataset encompassing multiple vendors, field strengths, localizer sequences, and liver pathologies.
Study Type:
Retrospective.
Population:
A total of 11,012 patients (5928 female) who underwent liver MRI exams across three vendors (GE HealthCare, Philips Healthcare, Siemens Healthineers) at three institutions, split into training/validation/test sets (70/10/20%). Liver disorders included cirrhosis, ascites, portal hypertension, and iron overload.
Field Strength/Sequence:
Three-plane localizers acquired using single-shot fast spin echo (68.3%) and gradient echo (31.7%) at 1.5 T (52.8%) and 3 T (47.2%).
Assessment:
A YOLOv8 object detection model was trained to detect the liver, torso, and arms on localizer images. Automated prescription accuracy was assessed using three-dimensional intersection-over-union (IoU3D) for axial, coronal, and sagittal prescriptions. Subgroup analyses evaluated performance across technical and patient-related factors.
Statistical Tests:
Mann-Whitney U tests with Bonferroni correction, Kruskal-Wallis tests, Wilcoxon signed-rank tests, Spearman correlation. Effect sizes were calculated using rank-biserial correlation (rrb) and epsilon-squared (ε2). p < 0.05 was considered statistically significant.
Results:
Median axial prescription IoU3D was 0.963 (interquartile range: 0.946-0.976), with median IoU3D above 0.940 for coronal and sagittal prescriptions. For axial prescriptions, margins less than 4 mm in each direction were required to achieve complete coverage of manual prescriptions in 90% of cases in the held-out test set. Effects were negligible to small across vendors, field strengths, localizer sequences, and patient factors (rrb ≤ 0.16, ε2 ≤ 0.01).
Data Conclusion:
A single multivendor model achieved accurate automated liver MRI prescription without clinically meaningful performance differences across vendors, field strengths, localizer sequences, or patient factors.
Evidence Level:
2.
Technical Efficacy:
Stage 1.
