Tadashi Araki1, Nobutaka Ikeda2, Devarshi Shukla3, Pankaj K Jain3, Narendra D Londhe3, Vimal K Shrivastava3, Sumit K Banchhor3, Luca Saba4, Andrew Nicolaides5, Shoaib Shafique6, John R Laird7, Jasjit S Suri8. 1. Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan. 2. Cardiovascular Medicine, National Center for Global Health and Medicine, Tokyo, Japan. 3. Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India. 4. Department of Radiology, University of Cagliari, Italy. 5. Vascular Screening and Diagnostic Centre, London, England, United Kingdom; Vascular Diagnostic Center, University of Cyprus, Nicosia, Cyprus. 6. CorVasc Vascular Laboratory, 8433 Harcourt Rd #100, Indianapolis, IN, USA. 7. UC Davis Vascular Center, University of California, Davis, CA, USA. 8. Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA; Point-Of-Care Devices, Global Biomedical Technologies, Inc., Roseville, CA, USA; Department of Electrical Engineering, University of Idaho (Affl.), Pocatello, ID, USA. Electronic address: jasjit.suri@atheropoint.com.
Abstract
BACKGROUND AND OBJECTIVE: Percutaneous coronary interventional procedures need advance planning prior to stenting or an endarterectomy. Cardiologists use intravascular ultrasound (IVUS) for screening, risk assessment and stratification of coronary artery disease (CAD). We hypothesize that plaque components are vulnerable to rupture due to plaque progression. Currently, there are no standard grayscale IVUS tools for risk assessment of plaque rupture. This paper presents a novel strategy for risk stratification based on plaque morphology embedded with principal component analysis (PCA) for plaque feature dimensionality reduction and dominant feature selection technique. The risk assessment utilizes 56 grayscale coronary features in a machine learning framework while linking information from carotid and coronary plaque burdens due to their common genetic makeup. METHOD: This system consists of a machine learning paradigm which uses a support vector machine (SVM) combined with PCA for optimal and dominant coronary artery morphological feature extraction. Carotid artery proven intima-media thickness (cIMT) biomarker is adapted as a gold standard during the training phase of the machine learning system. For the performance evaluation, K-fold cross validation protocol is adapted with 20 trials per fold. For choosing the dominant features out of the 56 grayscale features, a polling strategy of PCA is adapted where the original value of the features is unaltered. Different protocols are designed for establishing the stability and reliability criteria of the coronary risk assessment system (cRAS). RESULTS: Using the PCA-based machine learning paradigm and cross-validation protocol, a classification accuracy of 98.43% (AUC 0.98) with K=10 folds using an SVM radial basis function (RBF) kernel was achieved. A reliability index of 97.32% and machine learning stability criteria of 5% were met for the cRAS. CONCLUSIONS: This is the first Computer aided design (CADx) system of its kind that is able to demonstrate the ability of coronary risk assessment and stratification while demonstrating a successful design of the machine learning system based on our assumptions.
BACKGROUND AND OBJECTIVE: Percutaneous coronary interventional procedures need advance planning prior to stenting or an endarterectomy. Cardiologists use intravascular ultrasound (IVUS) for screening, risk assessment and stratification of coronary artery disease (CAD). We hypothesize that plaque components are vulnerable to rupture due to plaque progression. Currently, there are no standard grayscale IVUS tools for risk assessment of plaque rupture. This paper presents a novel strategy for risk stratification based on plaque morphology embedded with principal component analysis (PCA) for plaque feature dimensionality reduction and dominant feature selection technique. The risk assessment utilizes 56 grayscale coronary features in a machine learning framework while linking information from carotid and coronary plaque burdens due to their common genetic makeup. METHOD: This system consists of a machine learning paradigm which uses a support vector machine (SVM) combined with PCA for optimal and dominant coronary artery morphological feature extraction. Carotid artery proven intima-media thickness (cIMT) biomarker is adapted as a gold standard during the training phase of the machine learning system. For the performance evaluation, K-fold cross validation protocol is adapted with 20 trials per fold. For choosing the dominant features out of the 56 grayscale features, a polling strategy of PCA is adapted where the original value of the features is unaltered. Different protocols are designed for establishing the stability and reliability criteria of the coronary risk assessment system (cRAS). RESULTS: Using the PCA-based machine learning paradigm and cross-validation protocol, a classification accuracy of 98.43% (AUC 0.98) with K=10 folds using an SVM radial basis function (RBF) kernel was achieved. A reliability index of 97.32% and machine learning stability criteria of 5% were met for the cRAS. CONCLUSIONS: This is the first Computer aided design (CADx) system of its kind that is able to demonstrate the ability of coronary risk assessment and stratification while demonstrating a successful design of the machine learning system based on our assumptions.
Authors: Luca Saba; Skandha S Sanagala; Suneet K Gupta; Vijaya K Koppula; Amer M Johri; Aditya M Sharma; Raghu Kolluri; Deepak L Bhatt; Andrew Nicolaides; Jasjit S Suri Journal: Int J Cardiovasc Imaging Date: 2021-01-09 Impact factor: 2.357
Authors: Luca Saba; Pankaj K Jain; Harman S Suri; Nobutaka Ikeda; Tadashi Araki; Bikesh K Singh; Andrew Nicolaides; Shoaib Shafique; Ajay Gupta; John R Laird; Jasjit S Suri Journal: J Med Syst Date: 2017-05-13 Impact factor: 4.460
Authors: Alberto Boi; Ankush D Jamthikar; Luca Saba; Deep Gupta; Aditya Sharma; Bruno Loi; John R Laird; Narendra N Khanna; Jasjit S Suri Journal: Curr Atheroscler Rep Date: 2018-05-21 Impact factor: 5.113
Authors: Ankush Jamthikar; Deep Gupta; Elisa Cuadrado-Godia; Anudeep Puvvula; Narendra N Khanna; Luca Saba; Klaudija Viskovic; Sophie Mavrogeni; Monika Turk; John R Laird; Gyan Pareek; Martin Miner; Petros P Sfikakis; Athanasios Protogerou; George D Kitas; Chithra Shankar; Andrew Nicolaides; Vijay Viswanathan; Aditya Sharma; Jasjit S Suri Journal: Cardiovasc Diagn Ther Date: 2020-08
Authors: George Konstantonis; Krishna V Singh; Petros P Sfikakis; Ankush D Jamthikar; George D Kitas; Suneet K Gupta; Luca Saba; Kleio Verrou; Narendra N Khanna; Zoltan Ruzsa; Aditya M Sharma; John R Laird; Amer M Johri; Manudeep Kalra; Athanasios Protogerou; Jasjit S Suri Journal: Rheumatol Int Date: 2022-01-11 Impact factor: 2.631
Authors: Luca Saba; Skandha S Sanagala; Suneet K Gupta; Vijaya K Koppula; Amer M Johri; Narendra N Khanna; Sophie Mavrogeni; John R Laird; Gyan Pareek; Martin Miner; Petros P Sfikakis; Athanasios Protogerou; Durga P Misra; Vikas Agarwal; Aditya M Sharma; Vijay Viswanathan; Vijay S Rathore; Monika Turk; Raghu Kolluri; Klaudija Viskovic; Elisa Cuadrado-Godia; George D Kitas; Neeraj Sharma; Andrew Nicolaides; Jasjit S Suri Journal: Ann Transl Med Date: 2021-07
Authors: Ankush Jamthikar; Deep Gupta; Narendra N Khanna; Luca Saba; Tadashi Araki; Klaudija Viskovic; Harman S Suri; Ajay Gupta; Sophie Mavrogeni; Monika Turk; John R Laird; Gyan Pareek; Martin Miner; Petros P Sfikakis; Athanasios Protogerou; George D Kitas; Vijay Viswanathan; Andrew Nicolaides; Deepak L Bhatt; Jasjit S Suri Journal: Cardiovasc Diagn Ther Date: 2019-10