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

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.