Predicting Safe Liver Resection Volume for Major Hepatectomy Using Artificial Intelligence

(1) Background: Advancements in the field of liver surgery have led to a critical need for precise estimations of preoperative liver function to prevent post-hepatectomy liver failure (PHLF), a significant cause of morbidity and mortality. This study introduces a novel application of artificial intelligence (AI) in determining safe resection volumes according to a patient's liver function in major hepatectomies. (2) Methods: We incorporated a deep learning approach, incorporating a unique liver-specific loss function, to analyze patient characteristics, laboratory data, and liver volumetry from computed tomography scans of 52 patients. Our approach was evaluated against existing machine and deep learning techniques. (3) Results: Our approach achieved 68.8% accuracy in predicting safe resection volumes, demonstrating superior performance over traditional models. Furthermore, it significantly reduced the mean absolute error in under-predicted volumes to 23.72, indicating a more precise estimation of safe resection limits. These findings highlight the potential of integrating AI into surgical planning for liver resections. (4) Conclusion: By providing more accurate predictions of safe resection volumes, our method aims to minimize the risk of PHLF, thereby improving clinical outcomes for patients undergoing hepatectomy.