Early and accurate diagnosis of steatotic liver by artificial intelligence (AI)-supported ultrasonography

Objectives: Steatotic liver disease is the most frequent chronic liver disease worldwide. Ultrasonography (US) is commonly employed for the assessment and diagnosis. Few information is available on the possible use of artificial intelligence (AI) to ameliorate the diagnostic accuracy of ultrasonography. Materials and methods: An AI-based algorithm was developed using a dataset of US images. We prospectively enrolled 134 patients for algorithm validation. Patients underwent abdominal US and Proton Density Fat Fraction MRI scans (MRI-PDFF), assumed as reference technique. The hepatorenal index was manually calculated (HRIM) by 4 operators. An automatic hepatorenal index (HRIA) was obtained by the algorithm. The accuracy of HRIA to discriminate steatosis grades was evaluated by ROC analysis using MRI-PDFF cut-offs. Results: Overweight was 40 % of subjects (BMI 26.4 kg/cm(2)). The median HRIA was 1.11 (IQR 0.32) and the average of 4 manually calculated HRIM was 1.08 (IQR 0.26), with a 15 % inter-operator variability. Both HRIA (R = 0.79, P < 0.0001) and HRIM (R = 0.69, P < 0.0001) significantly correlated with liver fat percentage (MRIPDFF). According to MRI-PDFF, 32 % of enrolled subjects had steatosis. Discrimination capacity by AUC between patient with steatosis and patient without steatosis was better for HRIA than HRIM (AUC: 0.87 vs. 0.82, respectively). ROC analysis showed an AUC = 0.98 for HRIA with 1.64 cut-off in distinguishing between mild and moderate/severe groups. Conclusions: The use of AI improves accuracy and speed of ultrasonography in the diagnosis of liver steatosis. Further studies should evaluate the routine use of this technique in the management of liver steatosis at high cardio-metabolic risk.