Machine learning is being pressed into play to extract rich information from imaging and clinical data to aid the non-invasive and accurate diagnosis of multiple liver conditions.

Machine learning may help identify biomarkers and develop predictive models that could assist in the diagnosis of metabolic dysfunction-associated steatotic liver disease and hepatic fibrosis ...

Using five deep learning training models, an accuracy of 92% was achieved by the best-performing ensemble on retrospective MRE images of patients with varied liver stiffnesses.

Fatty liver disease, caused by the accumulation of fat in the liver, is estimated to affect one in four people worldwide. If left untreated, it can lead to serious complications, such as cirrhosis and ...

Finding relationships between bio-signals and health outcomes is complicated for many reasons, including sorting out irrelevant data.

An AI algorithm demonstrated strong performance for detecting chronic liver disease from routinely acquired echocardiography studies containing subcostal images of the liver.

Using five deep learning training models, an accuracy of 92% was achieved by the best-performing ensemble on retrospective MRE images of patients with varied liver stiffnesses.