Artificial intelligence-driven risk estimation, early detection, diagnostic characterisation and prognosis in hepatitis b-related hepatocellular carcinoma

Figure 1 Proposed integration of artificial intelligence into the hepatitis B-related hepatocellular carcinoma care pathway. A chronic hepatitis B population undergoes baseline risk stratification using conventional scores or clinical-variable machine learning. Longitudinal laboratory, virological and elastographic data permit dynamic risk estimation, which determines an individualised surveillance strategy in place of a uniform biannual schedule. Detection is performed by artificial intelligence-assisted ultrasonography, cross-sectional imaging, or serological and liquid-biopsy testing. A tumour so identified proceeds to diagnostic characterisation and prognostic assessment, and the output informs multidisciplinary clinical decision-making. The lower band lists the methodological conditions that apply to every stage. The figure represents a framework for investigation and not a description of validated practice. HBV: Hepatitis B virus; ML: Machine learning; US: Ultrasonography; CT: Computed tomography; MRI: Magnetic resonance imaging; HCC: Hepatocellular carcinoma; AI: Artificial intelligence.