Deep-learning algorithm improves liver fibrosis diagnosis

An algorithm combining the Fibrosis-4 Index (FIB-4) and an ultrasound deep-learning model could improve diagnostic accuracy and referral management for all-cause advanced liver fibrosis, a study published April 23 in Radiology found. Researchers led by Li-Da Chen, PhD, from the First Affiliated Hospital of Sun Yat-sen University in Guangzhou, China found that their combined sequential algorithm improved specificity by over 20% for predicting pathologically advanced liver fibrosis compared with the Fibrosis-4 Index (FIB-4) alone. Additionally, the algorithm reduced unnecessary referrals by 42% without requiring access to liver stiffness measurement. “AI-based image analysis provides a promising approach for noninvasive assessment of liver fibrosis on ultrasound images, offering an accessible tool for primary healthcare setting,” Chen and colleagues wrote. Noninvasive tests can be used to screen patients with chronic liver disease for advanced liver fibrosis. However, the researchers noted that the use of single tests may not be enough to provide an accurate diagnosis. Chen and colleagues constructed sequential clinical algorithms that include an ultrasound deep-learning model. From there, they compared the ability of the algorithms in predicting advanced liver fibrosis with that of other noninvasive tests. The retrospective study included adult patients with a history of chronic liver disease or unexplained abnormal liver function test results who underwent B-mode liv...
Source: AuntMinnie.com Headlines - Category: Radiology Authors: Tags: Ultrasound Artificial Intelligence Source Type: news