Ming Wai Yeung1,2, Jan Walter Benjamins1, Remco J J Knol3, Friso M van der Zant3, Folkert W Asselbergs2, Pim van der Harst1,2, Luis Eduardo Juarez-Orozco4,5. 1. Department of Cardiology, University Medical Center Groningen, University of Groningen, 9700 RB, Groningen, The Netherlands. 2. Department of Cardiology, Division of Heart & Lungs, University Medical Center Utrecht, University of Utrecht, Heidelberglaan 1, Box 30.001, 9700RB, Utrecht, The Netherlands. 3. Department of Nuclear Medicine, Cardiac Imaging Division Alkmaar, Northwest Clinics, Alkmaar, The Netherlands. 4. Department of Cardiology, University Medical Center Groningen, University of Groningen, 9700 RB, Groningen, The Netherlands. l.e.juarez.orozco@gmail.com. 5. Department of Cardiology, Division of Heart & Lungs, University Medical Center Utrecht, University of Utrecht, Heidelberglaan 1, Box 30.001, 9700RB, Utrecht, The Netherlands. l.e.juarez.orozco@gmail.com.
Abstract
BACKGROUND: Advanced cardiac imaging with positron emission tomography (PET) is a powerful tool for the evaluation of known or suspected cardiovascular disease. Deep learning (DL) offers the possibility to abstract highly complex patterns to optimize classification and prediction tasks. METHODS AND RESULTS: We utilized DL models with a multi-task learning approach to identify an impaired myocardial flow reserve (MFR <2.0 ml/g/min) as well as to classify cardiovascular risk traits (factors), namely sex, diabetes, arterial hypertension, dyslipidemia and smoking at the individual-patient level from PET myocardial perfusion polar maps using transfer learning. Performance was assessed on a hold-out test set through the area under receiver operating curve (AUC). DL achieved the highest AUC of 0.94 [0.87-0.98] in classifying an impaired MFR in reserve perfusion polar maps. Fine-tuned DL for the classification of cardiovascular risk factors yielded the highest performance in the identification of sex from stress polar maps (AUC = 0.81 [0.73, 0.88]). Identification of smoking achieved an AUC = 0.71 [0.58, 0.85] from the analysis of rest polar maps. The identification of dyslipidemia and arterial hypertension showed poor performance and was not statistically significant. CONCLUSION: Multi-task DL for the evaluation of quantitative PET myocardial perfusion polar maps is able to identify an impaired MFR as well as cardiovascular risk traits such as sex, smoking and possibly diabetes at the individual-patient level.
BACKGROUND: Advanced cardiac imaging with positron emission tomography (PET) is a powerful tool for the evaluation of known or suspected cardiovascular disease. Deep learning (DL) offers the possibility to abstract highly complex patterns to optimize classification and prediction tasks. METHODS AND RESULTS: We utilized DL models with a multi-task learning approach to identify an impaired myocardial flow reserve (MFR <2.0 ml/g/min) as well as to classify cardiovascular risk traits (factors), namely sex, diabetes, arterial hypertension, dyslipidemia and smoking at the individual-patient level from PET myocardial perfusion polar maps using transfer learning. Performance was assessed on a hold-out test set through the area under receiver operating curve (AUC). DL achieved the highest AUC of 0.94 [0.87-0.98] in classifying an impaired MFR in reserve perfusion polar maps. Fine-tuned DL for the classification of cardiovascular risk factors yielded the highest performance in the identification of sex from stress polar maps (AUC = 0.81 [0.73, 0.88]). Identification of smoking achieved an AUC = 0.71 [0.58, 0.85] from the analysis of rest polar maps. The identification of dyslipidemia and arterial hypertension showed poor performance and was not statistically significant. CONCLUSION: Multi-task DL for the evaluation of quantitative PET myocardial perfusion polar maps is able to identify an impaired MFR as well as cardiovascular risk traits such as sex, smoking and possibly diabetes at the individual-patient level.
Authors: Victoria Delgado; Bogdan A Popescu; Sven Plein; Stephan Achenbach; Juhani Knuuti; Jeroen J Bax Journal: Eur Heart J Date: 2019-02-07 Impact factor: 29.983
Authors: Ryan Poplin; Avinash V Varadarajan; Katy Blumer; Yun Liu; Michael V McConnell; Greg S Corrado; Lily Peng; Dale R Webster Journal: Nat Biomed Eng Date: 2018-02-19 Impact factor: 25.671