Machine Learning-Driven Risk Prediction Models for Posthepatectomy Liver Failure: A Narrative Review.

Area of Science:

• Artificial Intelligence
• Machine Learning in Medicine
• Surgical Risk Stratification

Background:

• Posthepatectomy liver failure (PHLF) is a significant cause of morbidity and mortality after major liver resections.
• Machine learning (ML) offers advanced tools for risk stratification in surgical patient populations.

Purpose of the Study:

• To systematically review and critically analyze the literature on ML-driven risk prediction models for PHLF.
• To evaluate the performance and limitations of ML models in identifying patients at risk of PHLF.

Main Methods:

• Systematic literature search of PubMed/MEDLINE, Scopus, and Web of Science databases.
• Inclusion and analysis of fifteen studies that developed and validated ML models for PHLF prediction.

Main Results:

• ML models effectively predict PHLF using perioperative clinical, laboratory, and imaging data.
• ML algorithms demonstrate high accuracy (AUC, sensitivity), often exceeding traditional risk scores.
• Limitations include small sample sizes, heterogeneity, and lack of external validation in existing studies.

Conclusions:

• ML-driven tools show potential for early and accurate PHLF risk detection.
• Integration of ML with clinical judgment can enhance personalized surgical planning and optimize outcomes.
• Further research is needed to address limitations and improve the clinical utility of ML for PHLF prediction.