Ankush Jamthikar1, Deep Gupta1, Narendra N Khanna2, Luca Saba3, Tadashi Araki4, Klaudija Viskovic5, Harman S Suri6, Ajay Gupta7, Sophie Mavrogeni8, Monika Turk9, John R Laird10, Gyan Pareek11, Martin Miner12, Petros P Sfikakis13, Athanasios Protogerou14, George D Kitas15, Vijay Viswanathan16, Andrew Nicolaides17, Deepak L Bhatt18, Jasjit S Suri19. 1. Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, India. 2. Department of Cardiology, Indraprastha Apollo Hospitals, New Delhi, India. 3. Department of Radiology, University of Cagliari, Cagliari, Italy. 4. Division of Cardiovascular Medicine, Toho University, Tokyo, Japan. 5. Department of Radiology and Ultrasound, University Hospital for Infectious Diseases Croatia, Zagreb, Croatia. 6. Department of Neuroscience, Brown University, Providence, RI, USA. 7. Department of Radiology, Weill Cornell Medicine, New York, NY, USA. 8. Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece. 9. Department of Neurology, University Medical Centre Maribor, Maribor, Slovenia. 10. Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA, USA. 11. Minimally Invasive Urology Institute, Brown University, Providence, RI, USA. 12. Men's Health Center, Miriam Hospital Providence, Providence, RI, USA. 13. Rheumatology Unit, National and Kapodistrian University of Athens, Athens, Greece. 14. Department of Cardiovascular Prevention & Research Unit Clinic & Laboratory of Pathophysiology, National and Kapodistrian University of Athens, Athens, Greece. 15. R & D Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK. 16. M.V. Hospital for Diabetes and Professor M. Viswanathan Diabetes Research Centre, Chennai, India. 17. Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, Nicosia, Cyprus. 18. Brigham and Women's Hospital Heart & Vascular Center, Harvard Medical School, Boston, MA, USA. 19. Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA.
Abstract
BACKGROUND: Most cardiovascular (CV)/stroke risk calculators using the integration of carotid ultrasound image-based phenotypes (CUSIP) with conventional risk factors (CRF) have shown improved risk stratification compared with either method. However such approaches have not yet leveraged the potential of machine learning (ML). Most intelligent ML strategies use follow-ups for the endpoints but are costly and time-intensive. We introduce an integrated ML system using stenosis as an endpoint for training and determine whether such a system can lead to superior performance compared with the conventional ML system. METHODS: The ML-based algorithm consists of an offline and online system. The offline system extracts 47 features which comprised of 13 CRF and 34 CUSIP. Principal component analysis (PCA) was used to select the most significant features. These offline features were then trained using the event-equivalent gold standard (consisting of percentage stenosis) using a random forest (RF) classifier framework to generate training coefficients. The online system then transforms the PCA-based test features using offline trained coefficients to predict the risk labels on test subjects. The above ML system determines the area under the curve (AUC) using a 10-fold cross-validation paradigm. The above system so-called "AtheroRisk-Integrated" was compared against "AtheroRisk-Conventional", where only 13 CRF were considered in a feature set. RESULTS: Left and right common carotid arteries of 202 Japanese patients (Toho University, Japan) were retrospectively examined to obtain 395 ultrasound scans. AtheroRisk-Integrated system [AUC =0.80, P<0.0001, 95% confidence interval (CI): 0.77 to 0.84] showed an improvement of ~18% against AtheroRisk-Conventional ML (AUC =0.68, P<0.0001, 95% CI: 0.64 to 0.72). CONCLUSIONS: ML-based integrated model with the event-equivalent gold standard as percentage stenosis is powerful and offers low cost and high performance CV/stroke risk assessment. 2019 Cardiovascular Diagnosis and Therapy. All rights reserved.
BACKGROUND: Most cardiovascular (CV)/stroke risk calculators using the integration of carotid ultrasound image-based phenotypes (CUSIP) with conventional risk factors (CRF) have shown improved risk stratification compared with either method. However such approaches have not yet leveraged the potential of machine learning (ML). Most intelligent ML strategies use follow-ups for the endpoints but are costly and time-intensive. We introduce an integrated ML system using stenosis as an endpoint for training and determine whether such a system can lead to superior performance compared with the conventional ML system. METHODS: The ML-based algorithm consists of an offline and online system. The offline system extracts 47 features which comprised of 13 CRF and 34 CUSIP. Principal component analysis (PCA) was used to select the most significant features. These offline features were then trained using the event-equivalent gold standard (consisting of percentage stenosis) using a random forest (RF) classifier framework to generate training coefficients. The online system then transforms the PCA-based test features using offline trained coefficients to predict the risk labels on test subjects. The above ML system determines the area under the curve (AUC) using a 10-fold cross-validation paradigm. The above system so-called "AtheroRisk-Integrated" was compared against "AtheroRisk-Conventional", where only 13 CRF were considered in a feature set. RESULTS: Left and right common carotid arteries of 202 Japanese patients (Toho University, Japan) were retrospectively examined to obtain 395 ultrasound scans. AtheroRisk-Integrated system [AUC =0.80, P<0.0001, 95% confidence interval (CI): 0.77 to 0.84] showed an improvement of ~18% against AtheroRisk-Conventional ML (AUC =0.68, P<0.0001, 95% CI: 0.64 to 0.72). CONCLUSIONS: ML-based integrated model with the event-equivalent gold standard as percentage stenosis is powerful and offers low cost and high performance CV/stroke risk assessment. 2019 Cardiovascular Diagnosis and Therapy. All rights reserved.
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