Diagnosis of significant liver fibrosis in patients with chronic hepatitis B using a deep learning-based data integration network.

Abstract

Chronic hepatitis B virus (CHB) infection remains a major global health burden and the non-invasive and accurate diagnosis of significant liver fibrosis (≥ F2) in CHB patients is clinically very important. This study aimed to assess the potential of the joint use of ultrasound images of liver parenchyma, liver stiffness values, and patients' clinical parameters in a deep learning model to improve the diagnosis of ≥ F2 in CHB patients. Of 527 CHB patients who underwent US examination, liver elastography and biopsy, 284 eligible patients were included. We developed a deep learning-based data integration network (DI-Net) to fuse the information of ultrasound images of liver parenchyma, liver stiffness values and patients' clinical parameters for diagnosing ≥ F2 in CHB patients. The performance of DI-Net was cross-validated in a main cohort (n = 155) of the included patients and externally validated in an independent cohort (n = 129), with comparisons against single-source data-based models and other non-invasive methods in terms of the area under the receiver-operating-characteristic curve (AUC). DI-Net achieved an AUC of 0.943 (95% confidence interval [CI] 0.893-0.973) in the cross-validation, and an AUC of 0.901 (95% CI 0.834-0.945) in the external validation, which were significantly greater than those of the comparative methods (AUC ranges: 0.774-0.877 and 0.741-0.848 for cross- and external validations, respectively, ps < 0.01). The joint use of ultrasound images of liver parenchyma, liver stiffness values, and patients' clinical parameters in a deep learning model could significantly improve the diagnosis of ≥ F2 in CHB patients.