Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT: A Multicenter Study.
Publication/Presentation Date
11-1-2018
Abstract
OBJECTIVES: The study evaluated the automatic prediction of obstructive disease from myocardial perfusion imaging (MPI) by deep learning as compared with total perfusion deficit (TPD).
BACKGROUND: Deep convolutional neural networks trained with a large multicenter population may provide improved prediction of per-patient and per-vessel coronary artery disease from single-photon emission computed tomography MPI.
METHODS: A total of 1,638 patients (67% men) without known coronary artery disease, undergoing stress
RESULTS: A total of 1,018 (62%) patients and 1,797 of 4,914 (37%) arteries had obstructive disease. Area under the receiver-operating characteristic curve for disease prediction by deep learning was higher than for TPD (per patient: 0.80 vs. 0.78; per vessel: 0.76 vs. 0.73: p < 0.01). With deep learning threshold set to the same specificity as TPD, per-patient sensitivity improved from 79.8% (TPD) to 82.3% (deep learning) (p < 0.05), and per-vessel sensitivity improved from 64.4% (TPD) to 69.8% (deep learning) (p < 0.01).
CONCLUSIONS: Deep learning has the potential to improve automatic interpretation of MPI as compared with current clinical methods.
Volume
11
Issue
11
First Page
1654
Last Page
1663
ISSN
1876-7591
Published In/Presented At
Betancur, Julian et al. “Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT: A Multicenter Study.” JACC. Cardiovascular imaging vol. 11,11 (2018): 1654-1663. doi:10.1016/j.jcmg.2018.01.020
Disciplines
Medicine and Health Sciences
PubMedID
29550305
Department(s)
Department of Medicine
Document Type
Article