Copyright
©The Author(s) 2022.
Artif Intell Gastroenterol. Jun 28, 2022; 3(3): 80-87
Published online Jun 28, 2022. doi: 10.35712/aig.v3.i3.80
Published online Jun 28, 2022. doi: 10.35712/aig.v3.i3.80
Blood biomarker panels for steatosis | |||
Panel | Patient | Anthropometry | Blood biomarkers |
FLI | - | BMI, Waist circumference | GGT and TG |
HSI | Presence of DM | BMI | AST:ASL |
Steatotest | Sex | BMI | ALT, GGT, TG, A2M, ApoA1, haptoglobin, bilirubin,cholesterol, and glucose |
LAP | Sex | Waist circumference | TG |
ION | Sex | Waist to hip ratio | ALT, TG |
NAFLD LFS | Presence of DM and MS | - | AST:ALT, Insulin |
Blood biomarker panels for fibrosis | |||
Panel | Patient | Anthropometry | Blood biomarkers |
APRI | - | - | Platelet count, AST |
FIB-4 | Age | - | Platelet count, AST, ALT |
FibroTest | Age, sex | BMI | GGT, A2M, ApoA1, haptoglobin, and total bilirubin |
Fibrometer | Age | Body weight | Platelet count, AST, ALT, glucose, ferritin |
ELF | - | - | Hyaluronic acid, PIIINP and TIMP-1 |
Hepascore | Age, sex | - | GGT, Hyaluronic acid, PIIINP and TIMP-1 |
BARD | Presence of DM | BMI | AST:ALT |
NFS | Age, sex, Presence of DM | - | Platelet count, AST:ALT, Albumin |
- Citation: Carteri RB, Grellert M, Borba DL, Marroni CA, Fernandes SA. Machine learning approaches using blood biomarkers in non-alcoholic fatty liver diseases. Artif Intell Gastroenterol 2022; 3(3): 80-87
- URL: https://www.wjgnet.com/2644-3236/full/v3/i3/80.htm
- DOI: https://dx.doi.org/10.35712/aig.v3.i3.80