| no |
title |
author |
magazine |
year |
volume |
issue |
page(s) |
type |
| 1 |
Accurate classification of liver nuclei based on AI models for diagnostic support of hepatocellular carcinoma
|
Grignaffini, F.
|
|
|
54 |
S3 |
p. S166
|
article
|
| 2 |
A quantitative MRCP-derived score for medium-term outcome prediction in primary sclerosing cholangitis
|
Cristoferi, L.
|
|
|
54 |
S3 |
p. S169
|
article
|
| 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
|
article
|
| 4 |
Can AI discriminate NAFLD vs NASH?
|
Giuffrè, M.
|
|
|
54 |
S3 |
p. S168
|
article
|
| 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
|
article
|
| 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
|
article
|
| 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
|
article
|
| 8 |
Editorial Board
|
|
|
|
54 |
S3 |
p. i-ii
|
article
|
| 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
|
article
|
| 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
|
article
|
| 11 |
New algorithm to identify patients at higher risk to develop hepatocellular carcinoma, based on machine learning approach
|
Martini, A.
|
|
|
54 |
S3 |
p. S166
|
article
|
Journal description