Abstract
More than one-third of the adult world population has steatotic liver disease (SLD),
with a few percent of individuals developing cirrhosis after decades of silent liver
fibrosis accumulation. Lack of systematic early detection causes most patients to
be diagnosed late, after decompensation, when treatment has limited effect and survival
is poor. Unfortunately, no isolated screening test in primary care can sufficiently
predict advanced fibrosis from SLD. Recent efforts, therefore, combine several parameters
into screening algorithms, to increase diagnostic accuracy. Besides patient selection,
for example, by specific characteristics, algorithms include nonpatented or patented
blood tests and liver stiffness measurements using elastography-based techniques.
Algorithms can be composed as a set of sequential tests, as recommended by most guidelines
on primary care pathways. Future use of algorithms that are easy to interpret, cheap,
and semiautomatic will improve the management of patients with SLD, to the benefit
of global health care systems.
Keywords
artificial intelligence - fibroScan - NAFLD - alcohol-related liver disease - noninvasive
tests
