Fully automated quantification of left ventricular volumes and function in cardiac MRI: clinical evaluation of a deep learning-based algorithm.

Journal: The international journal of cardiovascular imaging
Published Date: Jul 16, 2020

Abstract

To investigate the performance of a deep learning-based algorithm for fully automated quantification of left ventricular (LV) volumes and function in cardiac MRI. We retrospectively analysed MR examinations of 50 patients (74% men, median age 57 years). The most common indications were known or suspected ischemic heart disease, cardiomyopathies or myocarditis. Fully automated analysis of LV volumes and function was performed using a deep learning-based algorithm. The analysis was subsequently corrected by a senior cardiovascular radiologist. Manual volumetric analysis was performed by two radiology trainees. Volumetric results were compared using Bland-Altman statistics and intra-class correlation coefficient. The frequency of clinically relevant differences was analysed using re-classification rates. The fully automated volumetric analysis was completed in a median of 8 s. With expert review and corrections, the analysis required a median of 110 s. Median time required for manual analysis was 3.5 min for a cardiovascular imaging fellow and 9 min for a radiology resident (p < 0.0001 for all comparisons). The correlation between fully automated results and expert-corrected results was very strong with intra-class correlation coefficients of 0.998 for end-diastolic volume, 0.997 for end-systolic volume, 0.899 for stroke volume, 0.972 for ejection fraction and 0.991 for myocardial mass (all p < 0.001). Clinically meaningful differences between fully automated and expert corrected results occurred in 18% of cases, comparable to the rate between the two manual readers (20%). Deep learning-based fully automated analysis of LV volumes and function is feasible, time-efficient and highly accurate. Clinically relevant corrections are required in a minority of cases.

Authors

  • Benjamin Böttcher
    Institute of Diagnostic and Interventional Radiology, Paediatric Radiology and Neuroradiology, University Medical Centre Rostock, Ernst-Heydemann-Str. 6, 18057, Rostock, Germany.
  • Ebba Beller
    Department of Radiology, University Hospital, LMU Munich, Munich, Germany.
  • Anke Busse
    Institute of Diagnostic and Interventional Radiology, Paediatric Radiology and Neuroradiology, University Medical Centre Rostock, Ernst-Heydemann-Str. 6, 18057, Rostock, Germany.
  • Daniel Cantré
    Institute of Diagnostic and Interventional Radiology, Paediatric Radiology and Neuroradiology, University Medical Centre Rostock, Ernst-Heydemann-Str. 6, 18057, Rostock, Germany.
  • Seyrani Yücel
    Department of Internal Medicine, Divison of Cardiology, University Medical Center Rostock, Rostock, Germany.
  • Alper Öner
    Department of Internal Medicine, Divison of Cardiology, University Medical Center Rostock, Rostock, Germany.
  • Hüseyin Ince
    Department of Internal Medicine, Divison of Cardiology, University Medical Center Rostock, Rostock, Germany.
  • Marc-André Weber
    Institute of Diagnostic and Interventional Radiology, Paediatric Radiology and Neuroradiology, University Medical Centre Rostock, Ernst-Heydemann-Str. 6, 18057, Rostock, Germany.
  • Felix G Meinel
    Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, United States; Institute for Clinical Radiology, Ludwig-Maximilians-University Hospital, Munich, Germany.

Keywords

External Resources

Popular Topics
Recent Journals