A recent study published in the Journal of Hepatology demonstrated that a machine learning-based pan-elastography model can accurately predict clinically significant portal hypertension (CSPH) in patients with compensated advanced chronic liver disease (cACLD). CSPH is a critical stage in chronic liver disease, as it signals a heightened risk of life-threatening complications. Patients diagnosed with CSPH are often prescribed non-selective beta-blockers (NSBBs), which have been shown to reduce the risk of disease progression. The model was designed to work across multiple elastography technologies, including vibration-controlled transient elastography (VCTE), two-dimensional shear-wave elastography (2D-SWE), and point shear-wave elastography (p-SWE). Validation of a pan-ELastography Machine-learning (ELM) score to predict clinically significant portal hypertension in compensated advanced chronic liver disease.