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

Area of Science:

• Biochemistry
• Genomics
• Computational Biology

Background:

• Hepatic fibrosis can progress to cirrhosis and hepatocellular carcinoma (HCC).
• Accurate fibrosis staging is crucial for timely intervention and personalized treatment.
• Current biopsy methods are invasive and have sampling errors, necessitating less invasive predictive techniques.

Purpose of the Study:

• To review the role of artificial intelligence (AI) and non-coding RNAs (ncRNAs) in hepatic fibrosis.
• To explore recent advancements, challenges, and future directions in AI-driven ncRNA biomarker discovery for fibrosis.
• To highlight the potential of AI in improving non-invasive diagnostic and prognostic tools for hepatic fibrosis.

Main Methods:

• Analysis of current research on AI and ncRNA in hepatic fibrosis.
• Review of AI/machine learning/deep learning applications in transcriptomic data analysis.
• Integration of multi-omics data and ncRNA interaction networks for predictive modeling.

Main Results:

• Non-coding RNAs (miRNAs, lncRNAs, circRNAs) are key regulators and potential biomarkers in hepatic fibrosis.
• AI algorithms demonstrate enhanced precision in predicting fibrosis progression by integrating diverse data types.
• AI improves non-invasive diagnostic instruments and risk stratification for hepatic fibrosis.

Conclusions:

• AI-powered ncRNA analysis offers significant potential for transforming hepatic fibrosis diagnostics and prognostics.
• Standardization of data and clinical validation are essential for realizing AI's full potential in precision hepatology.
• AI facilitates the identification of ncRNA biomarkers for precise fibrosis staging and risk stratification.