Pulmonary Embolism Survival Prediction Using Multimodal Learning Based on Computed Tomography Angiography and Clinical Data.

Journal: Journal of thoracic imaging
Published Date:

Abstract

PURPOSE: Pulmonary embolism (PE) is a significant cause of mortality in the United States. The objective of this study is to implement deep learning (DL) models using computed tomography pulmonary angiography (CTPA), clinical data, and PE Severity Index (PESI) scores to predict PE survival.

Authors

  • Zhusi Zhong
    Radiology AI Lab, Brown University, Providence, Rhode Island, USA.
  • Helen Zhang
    The Warren Alpert Medical School of Brown University, Providence, RI, 02903, USA.
  • Fayez H Fayad
    Department of Diagnostic Radiology, Rhode Island Hospital.
  • Andrew C Lancaster
    Division of Pulmonary and Critical Care Medicine, Johns Hopkins University School of Medicine, Baltimore, MD.
  • John Sollee
    Department of Diagnostic Radiology, Rhode Island Hospital, 593 Eddy St., Providence, RI 02903, USA; Warren Alpert Medical School of Brown University, 222 Richmond St., Providence, RI 02903, USA.
  • Shreyas Kulkarni
    Department of Diagnostic Radiology, Rhode Island Hospital.
  • Cheng Ting Lin
    The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, USA. clin97@jhmi.edu.
  • Jie Li
    Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence Application Technology Research Institute, Shenzhen Polytechnic University, Shenzhen, China.
  • Xinbo Gao
  • Scott Collins
    Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI, USA.
  • Colin F Greineder
    Department of Emergency Medicine, University of Michigan, Ann Arbor, MI.
  • Sun H Ahn
    Department of Diagnostic Radiology, Rhode Island Hospital.
  • Harrison X Bai
    Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania.
  • Zhicheng Jiao
  • Michael K Atalay
    From the Department of Radiology, Xiangya Hospital, Central South University, Changsha 410008, China (H.X.B., Z.X., D.C.W., W.H.L.); Department of Diagnostic Imaging, Rhode Island Hospital, Providence, RI (H.X.B., B.H., K.H., I.P., M.K.A.); Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pa (R.W.); Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology. Massachusetts General Hospital, Boston, Mass (K.C.); Warren Alpert Medical School at Brown University, Providence, RI (H.X.B., K.H., T.M.L.T., J.W.C., I.P.); Department of Radiology, Yongzhou Central Hospital, Yongzhou, China (L.B.S.); Department of Radiology, Changde Second People's Hospital, Changde, China (J.M.); Department of Radiology, Affiliated Nan Hua Hospital, University of South China, Hengyang, China (X.L.J.); Department of Radiology, Loudi Central Hospital, Loudi, China (Q.H.Z.); Department of Radiology, Chenzhou Second People's Hospital, Chenzhou, China (P.F.H.); Department of Radiology, Zhuzhou Central Hospital, Zhuzhou, China (Y.H.L.); Department of Radiology, Yiyang City Center Hospital, Yiyang, China (F.X.F.); Department of Radiology, Brigham and Women's Hospital, Boston, Mass (R.Y.H.); Department of Radiology, Hospital of the University of Pennsylvania, Philadelphia, Pa (R.S.); and Department of Radiology, The First Hospital of Changsha, Changsha, China (Q.Z.Y.).