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

Journal: Hepatology (Baltimore, Md.)
PMID: 38156985

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.

Authors

  • Grace L Su
    Michigan Medicine, Department of Internal Medicine, Division of Gastroenterology and Hepatology, Ann Arbor, Michigan, United States of America.
  • Peng Zhang
    Key Laboratory of Macromolecular Science of Shaanxi Province, School of Chemistry & Chemical Engineering, Shaanxi Normal University, Xi'an, Shaanxi 710062, China.
  • Patrick X Belancourt
    Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, Michigan, USA.
  • Bradley Youles
    Center for Clinical Management Research, VA Ann Arbor Healthcare System, 2215 Fuller Rd, Ann Arbor, MI, 48105, USA.
  • Binu Enchakalody
    Department of Surgery, University of Michigan Medical School, Ann Arbor, Michigan, USA.
  • Ponni Perumalswami
    Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, Michigan, USA.
  • Akbar Waljee
    Center for Clinical Management Research, VA Ann Arbor Healthcare System, Ann Arbor, Michigan, USA.
  • Sameer Saini
    Division of Gastroenterology, University of Michigan, Ann Arbor, Michigan, USA.

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