AI in the Prediction of Hepatic Fibrosis Progression Using Non-Coding RNAs

Clinical Snapshot

Key Findings

• Effect Size: Not quantified - narrative synthesis only

• Primary Outcome: Potential of AI-enhanced ncRNA analysis for hepatic fibrosis prediction

• Nnt Or Sensitivity: Diagnostic accuracy metrics not systematically reported

• Confidence Interval: Not provided

Clinical Application

Currently limited - requires specialized laboratory capabilities and validation studies Would require TGA approval for diagnostic applications; not currently covered by PBS; needs validation in Australian populations before RACGP guideline consideration Patients with chronic liver disease at risk of fibrosis progression

Abstract

Hepatic fibrosis is a dynamic and progressive condition that can lead to cirrhosis and hepatocellular carcinoma (HCC) if left untreated. Appropriate assessment of the disease progression of fibrosis is critical for early intervention and individualized treatment regimens. Traditional biopsy techniques are invasive and prone to sampling errors, highlighting the need for less invasive predictive techniques. Non-coding RNAs (ncRNAs), including microRNAs (miRNAs), long ncRNAs (lncRNAs), and circular RNAs (circRNAs), have emerged as key regulators of hepatic fibrogenesis and as a possible biomarker for disease staging and prognosis. The emergence of artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has revolutionized the comprehensive large-scale analysis of transcriptomic data, enhancing the identification of ncRNA biomarkers and predictive modeling. The AI-based algorithms have been found to be more precise in anticipating fibrosis progression by means of integrating multi-omics data, ncRNA interaction networks, and by improving non-invasive diagnostic tools. This review involves the analysis of AI and ncRNA research in hepatic fibrosis, highlighting recent discoveries, possible challenges, and future opportunities. We address the necessity of standardization of data and clinical validation, as well as discuss the role of AI in identifying biomarkers of ncRNA, predicting the stage of fibrosis and risk stratification. ncRNA analysis with AI has a tremendous potential of transforming the diagnostics and prognostics of hepatic fibrosis, enabling precision hepatology.

References

1. 1.Roy, D., Hussain, M. S., Khan, Y., Patel, D. N., Smerat, A., Bainsal, N., Ashique, S., Kumar, A., Khan, I. A., Dilorom, O., Allaberganova, M., & Ashirova, A. (2026). AI in the prediction of hepatic fibrosis progression using non-coding RNAs. Clinica Chimica Acta, 120973. https://doi.org/10.1016/j.cca.2026.120973