Machine learning for endoleak detection after endovascular aortic repair.

Journal: Scientific reports
PMID: 33110113

Abstract

Diagnosis of endoleak following endovascular aortic repair (EVAR) relies on manual review of multi-slice CT angiography (CTA) by physicians which is a tedious and time-consuming process that is susceptible to error. We evaluate the use of a deep neural network for the detection of endoleak on CTA for post-EVAR patients using a novel data efficient training approach. 50 CTAs and 20 CTAs with and without endoleak respectively were identified based on gold standard interpretation by a cardiovascular subspecialty radiologist. The Endoleak Augmentor, a custom designed augmentation method, provided robust training for the machine learning (ML) model. Predicted segmentation maps underwent post-processing to determine the presence of endoleak. The model was tested against 3 blinded general radiologists and 1 blinded subspecialist using a held-out subset (10 positive endoleak CTAs, 10 control CTAs). Model accuracy, precision and recall for endoleak diagnosis were 95%, 90% and 100% relative to reference subspecialist interpretation (AUC = 0.99). Accuracy, precision and recall was 70/70/70% for generalist1, 50/50/90% for generalist2, and 90/83/100% for generalist3. The blinded subspecialist had concordant interpretations for all test cases compared with the reference. In conclusion, our ML-based approach has similar performance for endoleak diagnosis relative to subspecialists and superior performance compared with generalists.

Authors

  • Salmonn Talebi
    Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, 208A Stanley Hall #1762, Berkeley, CA, 94720-1762, USA.
  • Mohammad H Madani
    Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA.
  • Ali Madani
    Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, 208A Stanley Hall #1762, Berkeley, CA, 94720-1762, USA.
  • Ashley Chien
    Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, 208A Stanley Hall #1762, Berkeley, CA, 94720-1762, USA.
  • Jody Shen
    Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA.
  • Domenico Mastrodicasa
    Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Ashley River Tower, 25 Courtenay Dr, Charleston, SC 29425-2260 (S.S.M., D.M., M.v.A., C.N.D.C., R.R.B., C.T., A.V.S., A.M.F., B.E.J., L.P.G., U.J.S.); Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany (S.S.M., T.J.V.); Stanford University School of Medicine, Department of Radiology, Stanford, Calif (D.M.); Division of Cardiothoracic Imaging, Nuclear Medicine and Molecular Imaging, Department of Radiology and Imaging Sciences, Emory University, Atlanta, Ga (C.N.D.C.); Division of Cardiology, Department of Medicine, Medical University of South Carolina, Charleston, SC (R.R.B.); Department of Cardiology and Intensive Care Medicine, Heart Center Munich-Bogenhausen, Munich, Germany (C.T.); Department of Cardiology, Munich University Clinic, Ludwig-Maximilians-University, Munich, Germany (C.T.); Siemens Medical Solutions USA, Malvern, Pa (P.S.); and Department of Emergency Medicine, Medical University of South Carolina, Charleston, SC (A.J.M.).
  • Dominik Fleischmann
    Departments of Radiology (L.D.H., G.M., K.H., M.K., M.J.W., A.M.S., D.F.) and Surgery (M.F.), Stanford University School of Medicine, 300 Pasteur Dr, Room S-072, Stanford, CA 94305-5105.
  • Frandics P Chan
    Department of Radiology, School of Medicine, Stanford University, Stanford, CA, USA. frandics@stanford.edu.
  • Mohammad R K Mofrad
    Molecular Cell Biomechanics Laboratory, Departments of Bioengineering and Mechanical Engineering, University of California, Berkeley, California 94720, United States of America.

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