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©The Author(s) 2022.
World J Gastroenterol. Oct 14, 2022; 28(38): 5530-5546
Published online Oct 14, 2022. doi: 10.3748/wjg.v28.i38.5530
Published online Oct 14, 2022. doi: 10.3748/wjg.v28.i38.5530
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 |
- Citation: Liu JQ, Ren JY, Xu XL, Xiong LY, Peng YX, Pan XF, Dietrich CF, Cui XW. Ultrasound-based artificial intelligence in gastroenterology and hepatology. World J Gastroenterol 2022; 28(38): 5530-5546
- URL: https://www.wjgnet.com/1007-9327/full/v28/i38/5530.htm
- DOI: https://dx.doi.org/10.3748/wjg.v28.i38.5530