Attention-based graph neural network framework for non-invasive CAP score prediction in fatty liver disease via body...

Hepatic steatosis, affecting one-third of the global population, is a key challenge in gastroenterology with limited screening focus. It characterizes metabolic dysfunction-associated steatotic liver disease, which is increasingly prevalent and linked to metabolic issues, yet lacks accessible non-invasive early detection tools. This study evaluates an AI model's ability to predict controlled attenuation parameter (CAP) scores, providing qualitative estimates of mild and moderate or greater liver steatosis degrees. The study included 705 participants from a nutrition clinic, with data collected on 27 features such as physical exams, body measurements, and InBody270 results. CAP score was obtained from transient elastography findings. We developed a novel graph neural network (GNN) architecture that conceptualizes the human body as an interconnected graph structure to capture complex physiological relationships between different anatomical regions. The proposed GNN model significantly outperformed traditional machine learning approaches, achieving RMSE of 23.7 dB/m, MAE of 18.9 dB/m, and R² of 0.87. Attention-guided feature importance analysis identified waist circumference, trunk fat mass, and neck circumference as the most influential predictors of CAP scores. The graph-based model outperforms traditional machine learning in predicting CAP scores, leveraging body relationships for reliable, non-invasive hepatic steatosis screening across all severities.