AI and Machine Learning Computational Modeling that Takes into Consideration Gut Microbiota for a Personalized Decision Support Preoperative Planning for an Optimum Liver Regeneration After Partial Hepatectomy.

Abstract

Introduction: Gut microbiota (GM) is implicated in the remnant liver regeneration (LR) after partial hepatectomy (PH) and affects outcomes. Our study shifts the algorithmic computational modeling from the classical knowledge of (LR) to that of (GM) implication, integrating Artificial Intelligence/Machine Learning (AI/ML) for risk/benefit analysis to optimize outcomes.

Methods: The best model predicting postoperative liver volume (LR) has been developed upon the classic biological knowledge. This phenomenological model predicts, whether liver size would recover or remain irreversibly reduced and it is not perfect.

Results: Focusing on the impact of (GM) on (LR) after PH and the current articles upon (GM) and its impact on the change of the medical dogma integrated (GM), (AI/ML) to provide new predictive and therapeutics capabilities after PH.

Conclusion/discussion: Personalized and precise preoperative preparation for PH can optimize anatomic PH, pre-operative planning and outcomes upon AI/ML risk/benefit analysis integrating the impact and measurements of (GM).