Artificial intelligence in expanding hepatic resection boundaries: Integrating portal flow modulation and regenerative strategies

Table 1 Artificial intelligence and machine learning applications in liver surgery

Application area	AI/ML model type	Data source	Clinical impact	Performance metric
Liver segmentation/volumetry	CNN (U-Net, variants)	CT, MRI	Automated FLR measurement, resection planning	Dice coefficient > 0.95
FLR function prediction	Radiomics, ML classifiers	MRI (Gd-EOB-DTPA), CT	Predicts PHLF and functional margins	AUC 0.82-0.94
Outcome prediction (PHLF, complications)	Gradient boosting, Light GBM	EHR, imaging	Individualized risk, clinical DSS	AUC 0.82-0.94
Tumor segmentation/classification	Deep CNN	CT, MRI	Automated detection, margin planning	Accuracy > 93%
Intraoperative decision support	Explainable ML, AR	Video, segmentation	Real-time guidance, workflow efficiency	Not routinely quantified

CNN: Convolutional neural network; FLR: Future liver remnant; EOB-DTPA: Gadoxetic acid (gadoxetate disodium; ethoxybenzyl-diethylenetriamine pentaacetic acid); PHLF: Post-hepatectomy liver failure; DSS: Decision support system; AUC: Area under the curve; CT: Computed tomography; AI: Artificial intelligence; ML: Machine learning; MRI: Magnetic resonance imaging; AR: Augmented reality.