nr |
titel |
auteur |
tijdschrift |
jaar |
jaarg. |
afl. |
pagina('s) |
type |
1 |
Accurate classification of liver nuclei based on AI models for diagnostic support of hepatocellular carcinoma
|
Grignaffini, F. |
|
|
54 |
S3 |
p. S166 |
artikel |
2 |
A quantitative MRCP-derived score for medium-term outcome prediction in primary sclerosing cholangitis
|
Cristoferi, L. |
|
|
54 |
S3 |
p. S169 |
artikel |
3 |
Artificial intelligence and big data processing for the development of predictive models to support Covid-19 hospital patients
|
Miele, L. |
|
|
54 |
S3 |
p. S167-S168 |
artikel |
4 |
Can AI discriminate NAFLD vs NASH?
|
Giuffrè, M. |
|
|
54 |
S3 |
p. S168 |
artikel |
5 |
Cancer cells and tumor vasculature dynamics analyzed by bio-mathematical modeling in HCC patients with different response to TKIs, TACE and TARE
|
Damone, F. |
|
|
54 |
S3 |
p. S166-S167 |
artikel |
6 |
CEUS LI-RADS and quantification software: evaluation of intra-operator, inter-operator, and software-operator agreement in classifying liver nodules into LI-RADS classes
|
Giamperoli, A. |
|
|
54 |
S3 |
p. S167 |
artikel |
7 |
Development and validation of an artificial intelligence PIVKA-II-based model for the prediction of hepatocellular carcinoma development in patients with HCV-related cirrhosis successfully treated with direct-acting antivirals
|
Caviglia, G.P. |
|
|
54 |
S3 |
p. S165 |
artikel |
8 |
Editorial Board
|
|
|
|
54 |
S3 |
p. i-ii |
artikel |
9 |
LLM-PBC: Logic Learning Machine-based explainable rules accurately stratify the genetic risk of Primary Biliary Cholangitis
|
Gerussi, A. |
|
|
54 |
S3 |
p. S169-S170 |
artikel |
10 |
Machine learning combined with Deep learning approach to forecast the onset of major cardiovascular events in NAFLD asymptomatic patients
|
Cirella, A. |
|
|
54 |
S3 |
p. S168 |
artikel |
11 |
New algorithm to identify patients at higher risk to develop hepatocellular carcinoma, based on machine learning approach
|
Martini, A. |
|
|
54 |
S3 |
p. S166 |
artikel |