Artificial intelligence in contrast enhanced ultrasound: A new era for liver lesion assessment

Table 4 Key contributions of artificial intelligence-based contrast-enhanced ultrasound in clinical practice

Objective	Clinical impact
Reduction in interpretation time	AI-assisted models provide results in approximately 10 seconds, faster than manual reading (23-29 seconds)[33]
Improved diagnostic accuracy	Deep learning models achieve AUCs of 0.96-0.97 for benign vs malignant lesions[31,33]
Fully automated workflows	End-to-end segmentation and classification eliminate manual intervention[34]
Integration into ultrasound systems	Real-time AI implementation feasible within existing CEUS devices[28]
Reduction of annotation workload	Weakly supervised learning reduces dependence on manually labeled training data[32]
Enhanced LI-RADS standardization	AI models align closely with LI-RADS criteria, improving consistency and objectivity[15,24]

AI: Artificial intelligence; AUC: Area under the curve; CEUS: Contrast-enhanced ultrasound; LI-RADS: Liver Imaging Reporting and Data System.