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

Table 1 Artificial intelligence terminology in contrast-enhanced ultrasound for liver lesion assessment

Term	Definition	CEUS application
CNN	Deep learning model using convolutional layers to extract image features	Lesion detection, classification, segmentation
Radiomics	Extraction of quantitative handcrafted features from medical images	Supports AI-based lesion characterization
Ultrasomics	Radiomics applied specifically to ultrasound images	CEUS feature analysis for HCC risk stratification
Transformers	AI models using self-attention to learn feature relationships	Modeling CEUS video sequences for enhancement pattern recognition

AI: Artificial intelligence; CEUS: Contrast-enhanced ultrasound; CNN: Convolutional neural network; HCC: Hepatocellular carcinoma.

Table 2 Liver Imaging Reporting and Data System classification system for hepatocellular carcinoma[24]

LI-RADS category	Interpretation
LR-1	Definitely benign
LR-2	Probably benign
LR-3	Intermediate probability of malignancy
LR-4	Probably HCC
LR-5	Definitely HCC
LR-M	Probably/definite malignancy non-HCC specific

HCC: Hepatocellular carcinoma; LI-RADS: Liver Imaging Reporting and Data System; LR-1 to LR-5: Liver Imaging Reporting and Data System categories 1 to 5; LR-M: Liver Imaging Reporting and Data System category M.

Table 3 Performance comparison of clinicians vs automated Liver Imaging Reporting and Data System classification

Ref.	Comparison	Main findings
Urhuț et al[21]	Clinicians vs AI model (CEUS)	For differentiating benign from malignant liver tumors, the AI system showed higher specificity than both experienced readers (blinded and unblinded) but lower sensitivity; less accurate for HCC and metastases yet may assist less-experienced clinicians
Hu et al[15]	Senior radiologists vs DL model (CEUS)	AI outperformed residents (accuracy 82.9%-84.4%, P = 0.038) and matched experts (87.2%-88.2%, P = 0.438), improving resident performance and reducing CEUS interobserver variability in differentiating benign from malignant
Zhou et al[34]	3D-CNN vs CNN + LSTM (CEUS cine-loops)	High overall AUC (approximately 0.91) for CNN + LSTM, outperforming TIC and 3D-CNN by balancing sensitivity and specificity (3D-CNN: 0.96/0.55), narrowing accuracy gap between less-experienced and more-experienced radiologists (0.82 → 0.87); accuracy for benign vs malignant differentiation (n = 210 Lesions): 0.82 for less-experienced radiologists, 0.87 after AI assistance
Oezsoy et al[32]	Weakly supervised DL vs manual LI-RADS scoring	Model matched expert performance using only case-level labels with high accuracy (AUC 0.94)

3D: Three-dimensional; AI: Artificial intelligence; AUC: Area under the curve; CEUS: Contrast-enhanced ultrasonography; CNN: Convolutional neural network; CNN + LSTM: Convolutional neural network with long short-term memory; DL: Deep learning; LI-RADS: Liver Imaging Reporting and Data System; TIC: Time-intensity curve.

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.