Copyright
©The Author(s) 2019.
World J Gastroenterol. Feb 14, 2019; 25(6): 672-682
Published online Feb 14, 2019. doi: 10.3748/wjg.v25.i6.672
Published online Feb 14, 2019. doi: 10.3748/wjg.v25.i6.672
n | Task | Type | Accuracy | Ref. |
1 | Detecting liver new tumors | Deep learning based on CT | 86% | [36] |
2 | Predicting the primary origin of liver metastasis | Deep learning based on CT | 56% | [40] |
3 | Detecting cirrhosis with liver capsules | Deep learning based on ultrasound | 96.8% | [41] |
4 | Detecting fatty liver disease and making risk stratification | Deep learning based on ultrasound | 100% | [42] |
5 | Detecting and distinguishing different focal liver lesions. | Deep learning based on ultrasound | 97.2% | [43] |
6 | Detecting metastatic liver malignancy | Deep learning based on PET/CT | 90.5% | [44] |
- Citation: Zhou LQ, Wang JY, Yu SY, Wu GG, Wei Q, Deng YB, Wu XL, Cui XW, Dietrich CF. Artificial intelligence in medical imaging of the liver. World J Gastroenterol 2019; 25(6): 672-682
- URL: https://www.wjgnet.com/1007-9327/full/v25/i6/672.htm
- DOI: https://dx.doi.org/10.3748/wjg.v25.i6.672