Categorizing liver stiffness in children and adults through deep learning and multiparametric MRI with segmented liver and spleen data

Motivation: To address the limited accessibility of Magnetic Resonance Elastography (MRE) for liver stiffness assessment. Goal(s): To develop AI-based pipeline for categorizing subjects into no/mild (<3 kPa) and moderate/severe (≥3 kPa) liver stiffening using multiparametric MRI images. Approach: Our model contains two main components: segmentation and classification. We employed a Swin-UNETR model to segment liver and spleen tissues from multiparametric MRI images. Then, we developed a Swin Transformer-based model for liver stiffness stratification. We used multi-site ten-fold cross-validation to evaluate our models’ performance. Results: Our best model achieved an Area Under the Receiver Operating Characteristic (AUROC) curve of 0.84 for liver stiffness categorization. Impact: Offering an accessible and accurate method for liver stiffness categorization, our research may enhance patient care, reduce healthcare costs, and expand the availability of this vital diagnostic tool, benefiting clinicians, researchers, and, ultimately, patients with liver disease, worldwide.