Machine Learning Models predict liver steatosis

Introduction Screening for liver fibrosis continues to rely on laboratory panels and non-invasive tests such as FIB-4-score and transient elastography. In this study, we evaluated the potential of machine learning (ML) methods to predict liver steatosis on abdominal ultrasound and liver fibrosis in individuals participating in a screening program for colorectal cancer.

Methods We performed ultrasound on 5834 patients admitted between 2006 and 2020, and transient elastography on a subset of 1240 patients. Steatosis on ultrasound was diagnosed if liver areas showed a significantly increased echogenicity compared to the renal parenchyma. Liver fibrosis was defined as a liver stiffness measurement≥8kPa in transient elastography. Extreme gradient boosting (XGBoost) algorithms were prospectively evaluated for prediction.

Results The mean age was 58±9 years with 3036 males (52%), and 77% suffered from metabolic syndrome. Modelling laboratory parameters, clinical parameters, and data on eight food types/dietary patterns, good accuracy in predicting liver steatosis on ultrasound (AUC-ROC 0.87) moderate accuracy in predicting liver fibrosis with XGBoost (AUC-ROC of 0.71) could be achieved. Limiting variables to non-self-reported (non-subjective) variables did not significantly alter performance. Gender-specific analyses showed significantly higher performance in male (AUC-ROC 0.70) compared to females (AUC-ROC 0.60) in predicting liver fibrosis.

Conclusion ML based on point-prevalence laboratory and clinical information predicts liver steatosis with high and liver fibrosis with moderate accuracy. It is conceivable that a model that includes parameters at different time points might perform better. The observed gender differences suggest the need to develop sex-specific models.

Publication History

Article published online:
26 August 2022