Sanagala S Skandha1, Suneet K Gupta2, Luca Saba3, Vijaya K Koppula4, Amer M Johri5, Narendra N Khanna6, Sophie Mavrogeni7, John R Laird8, Gyan Pareek9, Martin Miner10, Petros P Sfikakis11, Athanasios Protogerou12, Durga P Misra13, Vikas Agarwal13, Aditya M Sharma14, Vijay Viswanathan15, Vijay S Rathore16, Monika Turk17, Raghu Kolluri18, Klaudija Viskovic19, Elisa Cuadrado-Godia20, George D Kitas21, Andrew Nicolaides22, Jasjit S Suri23. 1. CSE Department, CMR College of Engineering & Technology, Hyderabad, India; CSE Department, Bennett University, Greater Noida, UP, India. 2. CSE Department, Bennett University, Greater Noida, UP, India. 3. Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy. 4. CSE Department, CMR College of Engineering & Technology, Hyderabad, India. 5. Department of Medicine, Division of Cardiology, Queen's University, Kingston, Ontario, Canada. 6. Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India. 7. Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece. 8. Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA. 9. Minimally Invasive Urology Institute, Brown University, Providence, RI, USA. 10. Men's Health Center, Miriam Hospital Providence, RI, USA. 11. Rheumatology Unit, National Kapodistrian University of Athens, Greece. 12. Department of Cardiovascular Prevention, National and Kapodistrian Univ. of Athens, Greece. 13. Dept. of Clinical Immunology and Rheumatology, SGPGIMS, Lucknow, India. 14. Division of Cardiovascular Medicine, University of Virginia, VA, USA. 15. MV Hospital for Diabetes & Professor M Viswanathan Diabetes Research Centre, Chennai, India. 16. Nephrology Department, Kaiser Permanente, Sacramento, CA, USA. 17. The Hanse-Wissenschaftskolleg Institute for Advanced Study, Delmenhorst, Germany. 18. OhioHealth Heart and Vascular, Ohio, USA. 19. University Hospital for Infectious Diseases, Zagreb, Croatia. 20. IMIM - Hospital Del Mar, Passeig Marítim, Barcelona, Spain. 21. R & D Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK. 22. Vascular Screening and Diagnostic Centre, University of Nicosia, Nicosia, Cyprus. 23. Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA. Electronic address: jasjit.suri@atheropoint.com.
Abstract
BACKGROUND AND PURPOSE: Atherosclerotic plaque tissue rupture is one of the leading causes of strokes. Early carotid plaque monitoring can help reduce cardiovascular morbidity and mortality. Manual ultrasound plaque classification and characterization methods are time-consuming and can be imprecise due to significant variations in tissue characteristics. We report a novel artificial intelligence (AI)-based plaque tissue classification and characterization system. METHODS: We hypothesize that symptomatic plaque is hypoechoic due to its large lipid core and minimal collagen, as well as its heterogeneous makeup. Meanwhile, asymptomatic plaque is hyperechoic due to its small lipid core, abundant collagen, and the fact that it is often calcified. We designed a computer-aided diagnosis (CADx) system consisting of three kinds of deep learning (DL) classification paradigms: Deep Convolutional Neural Network (DCNN), Visual Geometric Group-16 (VGG16), and transfer learning, (tCNN). DCNN was 3-D optimized by varying the number of CNN layers and data augmentation frameworks. The DL systems were benchmarked against four types of machine learning (ML) classification systems, and the CADx system was characterized using two novel strategies consisting of DL mean feature strength (MFS) and a bispectrum model using higher-order spectra. RESULTS: After balancing symptomatic and asymptomatic plaque classes, a five-fold augmentation process was applied, yielding 1000 carotid scans in each class. Then, using a K10 protocol (trained to test the ratio of 90%-10%), tCNN and DCNN yielded accuracy (area under the curve (AUC)) pairs of 83.33%, 0.833 (p < 0.0001) and 95.66%, 0.956 (p < 0.0001), respectively. DCNN was superior to ML by 7.01%. As part of the characterization process, the MFS of the symptomatic plaque was found to be higher compared to the asymptomatic plaque by 17.5% (p < 0.0001). A similar pattern was seen in the bispectrum, which was higher for symptomatic plaque by 5.4% (p < 0.0001). It took <2 s to perform the online CADx process on a supercomputer. CONCLUSIONS: The performance order of the three AI systems was DCNN > tCNN > ML. Bispectrum-based on higher-order spectra proved a powerful paradigm for plaque tissue characterization. Overall, the AI-based systems offer a powerful solution for plaque tissue classification and characterization.
BACKGROUND AND PURPOSE:Atherosclerotic plaque tissue rupture is one of the leading causes of strokes. Early carotid plaque monitoring can help reduce cardiovascular morbidity and mortality. Manual ultrasound plaque classification and characterization methods are time-consuming and can be imprecise due to significant variations in tissue characteristics. We report a novel artificial intelligence (AI)-based plaque tissue classification and characterization system. METHODS: We hypothesize that symptomatic plaque is hypoechoic due to its large lipid core and minimal collagen, as well as its heterogeneous makeup. Meanwhile, asymptomatic plaque is hyperechoic due to its small lipid core, abundant collagen, and the fact that it is often calcified. We designed a computer-aided diagnosis (CADx) system consisting of three kinds of deep learning (DL) classification paradigms: Deep Convolutional Neural Network (DCNN), Visual Geometric Group-16 (VGG16), and transfer learning, (tCNN). DCNN was 3-D optimized by varying the number of CNN layers and data augmentation frameworks. The DL systems were benchmarked against four types of machine learning (ML) classification systems, and the CADx system was characterized using two novel strategies consisting of DL mean feature strength (MFS) and a bispectrum model using higher-order spectra. RESULTS: After balancing symptomatic and asymptomatic plaque classes, a five-fold augmentation process was applied, yielding 1000 carotid scans in each class. Then, using a K10 protocol (trained to test the ratio of 90%-10%), tCNN and DCNN yielded accuracy (area under the curve (AUC)) pairs of 83.33%, 0.833 (p < 0.0001) and 95.66%, 0.956 (p < 0.0001), respectively. DCNN was superior to ML by 7.01%. As part of the characterization process, the MFS of the symptomatic plaque was found to be higher compared to the asymptomatic plaque by 17.5% (p < 0.0001). A similar pattern was seen in the bispectrum, which was higher for symptomatic plaque by 5.4% (p < 0.0001). It took <2 s to perform the online CADx process on a supercomputer. CONCLUSIONS: The performance order of the three AI systems was DCNN > tCNN > ML. Bispectrum-based on higher-order spectra proved a powerful paradigm for plaque tissue characterization. Overall, the AI-based systems offer a powerful solution for plaque tissue classification and characterization.
Authors: Mohit Agarwal; Luca Saba; Suneet K Gupta; Amer M Johri; Narendra N Khanna; Sophie Mavrogeni; John R Laird; Gyan Pareek; Martin Miner; Petros P Sfikakis; Athanasios Protogerou; Aditya M Sharma; Vijay Viswanathan; George D Kitas; Andrew Nicolaides; Jasjit S Suri Journal: Med Biol Eng Comput Date: 2021-02-05 Impact factor: 2.602
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: L Saba; C Loewe; T Weikert; M C Williams; N Galea; R P J Budde; R Vliegenthart; B K Velthuis; M Francone; J Bremerich; L Natale; K Nikolaou; J N Dacher; C Peebles; F Caobelli; A Redheuil; M Dewey; K F Kreitner; R Salgado Journal: Eur Radiol Date: 2022-10-04 Impact factor: 7.034
Authors: Jasjit S Suri; Sushant Agarwal; Gian Luca Chabert; Alessandro Carriero; Alessio Paschè; Pietro S C Danna; Luca Saba; Armin Mehmedović; Gavino Faa; Inder M Singh; Monika Turk; Paramjit S Chadha; Amer M Johri; Narendra N Khanna; Sophie Mavrogeni; John R Laird; Gyan Pareek; Martin Miner; David W Sobel; Antonella Balestrieri; Petros P Sfikakis; George Tsoulfas; Athanasios D Protogerou; Durga Prasanna Misra; Vikas Agarwal; George D Kitas; Jagjit S Teji; Mustafa Al-Maini; Surinder K Dhanjil; Andrew Nicolaides; Aditya Sharma; Vijay Rathore; Mostafa Fatemi; Azra Alizad; Pudukode R Krishnan; Ferenc Nagy; Zoltan Ruzsa; Mostafa M Fouda; Subbaram Naidu; Klaudija Viskovic; Mannudeep K Kalra Journal: Diagnostics (Basel) Date: 2022-06-16
Authors: Ankush D Jamthikar; Deep Gupta; Anudeep Puvvula; Amer M Johri; Narendra N Khanna; Luca Saba; Sophie Mavrogeni; John R Laird; Gyan Pareek; Martin Miner; Petros P Sfikakis; Athanasios Protogerou; George D Kitas; Raghu Kolluri; Aditya M Sharma; Vijay Viswanathan; Vijay S Rathore; Jasjit S Suri Journal: Rheumatol Int Date: 2020-08-28 Impact factor: 2.631