Opportunistic assessment of ischemic heart disease risk using abdominopelvic computed tomography and medical record data: a multimodal explainable artificial intelligence approach.

Journal: Scientific reports
PMID: 38030716

Abstract

Current risk scores using clinical risk factors for predicting ischemic heart disease (IHD) events-the leading cause of global mortality-have known limitations and may be improved by imaging biomarkers. While body composition (BC) imaging biomarkers derived from abdominopelvic computed tomography (CT) correlate with IHD risk, they are impractical to measure manually. Here, in a retrospective cohort of 8139 contrast-enhanced abdominopelvic CT examinations undergoing up to 5 years of follow-up, we developed multimodal opportunistic risk assessment models for IHD by automatically extracting BC features from abdominal CT images and integrating these with features from each patient's electronic medical record (EMR). Our predictive methods match and, in some cases, outperform clinical risk scores currently used in IHD risk assessment. We provide clinical interpretability of our model using a new method of determining tissue-level contributions from CT along with weightings of EMR features contributing to IHD risk. We conclude that such a multimodal approach, which automatically integrates BC biomarkers and EMR data, can enhance IHD risk assessment and aid primary prevention efforts for IHD. To further promote research, we release the Opportunistic L3 Ischemic heart disease (OL3I) dataset, the first public multimodal dataset for opportunistic CT prediction of IHD.

Authors

  • Juan M Zambrano Chaves
    Department of Biomedical Data Science, Stanford University, 1265 Welch Road, MSOB West Wing, Third Floor, Stanford, CA, 94305, USA.
  • Andrew L Wentland
    University of Wisconsin-Madison, 1111 Highland Ave, Madison, WI, 53705, USA.
  • Arjun D Desai
    Department of Radiology, Stanford University, Stanford, California, USA.
  • Imon Banerjee
    Mayo Clinic, Department of Radiology, Scottsdale, AZ, USA.
  • Gurkiran Kaur
    Department of Radiology, Mayo Clinic, 13400 East Shea Blvd, Scottsdale, AZ, 85259, USA.
  • Ramon Correa
    School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
  • Robert D Boutin
    Department of Radiology, University of California, Davis, School of Medicine, Sacramento, California.
  • David J Maron
    Department of Medicine (Cardiovascular Medicine), Stanford Prevention Research Center, Stanford University School of Medicine, Stanford, California, USA.
  • Fatima Rodriguez
    From the Division of Cardiovascular Medicine, Cardiovascular Institute (F.R., R.A.H.), Department of Medicine (F.R., R.A.H.).
  • Alexander T Sandhu
    Division of Cardiovascular Medicine, Department of Medicine, School of Medicine, Stanford University, 300 Pasteur Drive, Stanford, CA, 94305, USA.
  • Daniel Rubin
    Department of Radiology, Stanford University, Stanford, CA, USA.
  • Akshay S Chaudhari
    Department of Radiology, Stanford University, Stanford, California.
  • Bhavik N Patel
    Department of Radiology, Stanford University, Stanford, California, United States of America.

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