BACKGROUND Non-obese individuals account for 40% of global steatotic liver disease (SLD) cases, yet lack dedicated targeted screening tools. Current ultrasound-based methods exhibit low detection rates for mild steatosis, delaying intervention. OBJECTIVE This study aimed to develop and validate a non-invasive, multi-class machine learning model using the Ultrasound Attenuation Parameter to predict hepatic steatosis severity grades (none/mild/moderate-severe) in non-obese populations. It sought to address the critical diagnostic gap in early detection and risk stratification for this under-recognized group, thereby enabling timely intervention. METHODS A cohort of 215,145 participants enrolled from 2018 to 2024 was analyzed. UAP thresholds defined steatosis severity: 269 (moderate to severe). Least Absolute Shrinkage and Selection Operator (LASSO) regression identified 14 predictors. Six ML models were trained (70% of the dataset) and validated (30%) using 10-fold cross-validation. Performance metrics included accuracy, Cohen’s kappa, area under the receiver operating characteristic curve (AUROC), F1-score, and SHapley Additive exPlanations (SHAP) analysis for interpretability. RESULTS The ML models were developed and validated using 150,602 participants in the training set and 64,543 in the test set, comprising non-SLD (n=92,944), mild SLD (n=54,121), and moderate-to-severe SLD (n=68,080). The Extreme Gradient Boosting (XGBoost) model demonstrated superior performance compared to other models. On the training set, it achieved a macro-average AUROC of 0.929, a macro-average precision-recall (PR) AUC of 0.878, and an accuracy of 0.788. On the test set, performance remained strong, with a macro-average AUROC of 0.908, a macro-average PR AUC of 0.842, and an accuracy of 0.759, surpassing other models. CONCLUSIONS The XGBoost model enables timely severity assessment, reduces risks of delayed diagnoses, and supports data-driven individualized interventions, demonstrating significant translational potential of this AI-driven approach for SLD management.
Zhu et al. (Sun,) studied this question.