Incorporation of quantitative imaging data using artificial intelligence improves risk prediction in veterans with liver disease.

ABSTRACT:

Background and aims

Utilization of electronic health records data to derive predictive indexes such as the electronic Child-Turcotte-Pugh (eCTP) Score can have significant utility in health care delivery. Within the records, CT scans contain phenotypic data which have significant prognostic value. However, data extractions have not traditionally been applied to imaging data. In this study, we used artificial intelligence to automate biomarker extraction from CT scans and examined the value of these features in improving risk prediction in patients with liver disease.

Approach and results

Using a regional liver disease cohort from the Veterans Health System, we retrieved administrative, laboratory, and clinical data for Veterans who had CT scans performed for any clinical ind

SUBMITTER: Su GL 

PROVIDER: S-EPMC11213827 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature