Table 2.
Application of ultrasound-based artificial intelligence in focal liver lesions
|
Ref.
|
Diseases: number of cases
|
Type of ultrasound
|
Algorithm of AI
|
Performance
|
| Xi et al[42] | Benign lesions: 300 | B-mode | CNN | All lesions |
| Accuracy: 84% | ||||
| Uncertain set of lesions | ||||
| Malignant lesions: 296 | Accuracy: 79% | |||
| Yang et al[43] | Benign tumor: 427 | B-mode | CNN | AUC for EV: 0.924 |
| Sensitivity: 86.5% | ||||
| Malignant tumor: 1786 | ||||
| Specificity: 85.5% | ||||
| Virmani et al[44] | HCC: 27 | B-mode | SVM | Accuracy of HCC: 91.6% |
| Sensitivity | ||||
| Metastatic liver tumor: 24 | HCC: 90% | |||
| Metastatic liver tumor: 93.3% | ||||
| Hwang et al[49] | Cyst: 29 | B-mode | ANN | Accuracy: 96% |
| Cyst vs hemangioma | ||||
| Cyst vs malignant | ||||
| Hemangioma: 37 | ||||
| Hemangioma vs malignant | ||||
| Malignant: 33 | ||||
| Schmauch et al[50] | Non-tumorous liver: 258 | B-mode | CNN | AUC |
| Hemangioma: 17 | FLL detection: 0.935 | |||
| Metastasis: 48 | ||||
| HCC: 6 | ||||
| FLL discrimination: 0.916 | ||||
| Cyst: 30 | ||||
| FNH: 8 | ||||
| Tiyarattanachai et al[51] | HCC: 2414 | B-mode | CNN | Detection rate: 87.0% |
| Cyst: 6600 | Sensitivity: 83.9% | |||
| Hemangioma: 5374 | ||||
| Specificity: 97.1% | ||||
| Focal fatty sparing: 5110 | ||||
| Focal fatty infiltration: 934 | ||||
| Gatos et al[47] | Benign FLL: 30 | CEUS | SVM | Accuracy: 90.3% |
| Sensitivity: 93.1% | ||||
| Malignant FLL: 22 | Specificity: 86.9% | |||
| Kondo et al[46] | Benign FLL: 31 | CEUS | SVM | Benign vs malignant |
| Accuracy: 91.8% | ||||
| Sensitivity: 94% | ||||
| Specificity: 87.1% | ||||
| Accuracy | ||||
| Malignant FLL: 67 | ||||
| Benign: 84.4% | ||||
| HCC: 87.7% | ||||
| Metastatic liver tumor: 85.7% | ||||
| Guo et al[48] | Benign FLL: 46 | CEUS | Deep canonical correlation analysis and multiple kernel learning | Accuracy: 90.4% |
| Sensitivity: 93.6% | ||||
| Malignant FLL: 47 | Specificity: 86.8% | |||
| Streba et al[52] | HCC: 41 | CEUS | ANN | Training accuracy: 94.5% |
| Hypervascular liver metastasis: 20 | Testing accuracy: 87.1% | |||
| Hypovascular liver metastasis: 12 | Sensitivity: 93.2% | |||
| Specificity: 89.7% | ||||
| Hemangioma: 16 | ||||
| Focal fatty changes: 23 | ||||
| Căleanu et al[53] | HCC: 30 | CEUS | Deep neural network | Accuracy: 88% |
| Hypervascular liver metastasis: 11 | ||||
| Hypovascular liver metastasis: 11 | ||||
| Hemangioma: 23 | ||||
| FNH: 16 | ||||
| Dong et al[56] | HCC: 322 | B-mode | Radiomics | AUC: 0.81 |
| Hu et al[57] | HCC: 482 | CEUS | Radiomics | AUC: 0.731 |
| Training cohort: 341 | ||||
| Validation cohort: 141 | ||||
| Zhang et al[58] | HCC: 313 | CEUS | Radiomics | AUC |
| Primary cohort: 192 | Primary dataset: 0.849 | |||
| Validation cohort: 121 | Validation dataset: 0.788 | |||
| Liu et al[63] | HCC: 130 | CEUS | Deep learning radiomics | AUC: 0.93 |
| Training cohort: 89 | ||||
| Validation cohort: 41 | ||||
| Ma et al[66] | HCC: 318 | CEUS | Radiomics | AUC: 0.89 |
| Training cohort: 255 | ||||
| Validation cohort: 63 | ||||
| Liu et al[69] | HCC: 419 | CEUS | Deep learning radiomics | C-index |
| RFA: 214 | RFA: 0.726 | |||
| SR: 0.741 | ||||
| SR: 205 |
AI: Artificial intelligence; ANN: Artificial neural network; AUC: Area under the receiver operating characteristic curve; CEUS: Contrast-enhanced ultrasound; CNN: Convolutional neural network; EV: External validation; HCC: Hepatocellular carcinoma; FNH: Focal nodular hyperplasia; FLL: Focal liver lesion; RFA: Radiofrequency ablation; SR: Surgical resection; SVM: Support vector machine.