3-D optimized classification and characterization artificial intelligence paradigm for cardiovascular/stroke risk stratification using carotid ultrasound-based delineated plaque: Atheromatic™ 2.0
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 varia...
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| Published in | Computers in biology and medicine Vol. 125; p. 103958 |
|---|---|
| Main Authors | , , , , , , , , , , , , , , , , , , , , , , , |
| Format | Journal Article |
| Language | English |
| Published |
United States
Elsevier Ltd
01.10.2020
Elsevier Limited |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0010-4825 1879-0534 1879-0534 |
| DOI | 10.1016/j.compbiomed.2020.103958 |
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| Abstract | 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.
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.
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.
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.
•First-time classification and characterization of ultrasound-based carotid plaques using 3-D optimization of deep convolution neural networks with varying augmentation and layers of the deep CNN: Atheromatic™ 2.0 (AtheroPoint™, Roseville, CA, USA).•Comparison of seven Artificial Intelligence (AI) models, its generalization and benchmarking against Atheromatic™ 1.0 (AtheroPoint™, Roseville, CA, USA).•Performance evaluation using statistical techniques namely DOR, power analysis, Atheromatic™ SI, and Kappa analysis.•Comparison between local computer vs. supercomputer frameworks. |
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| AbstractList | AbstractBackground and PurposeAtherosclerotic 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. MethodsWe 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. ResultsAfter 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. ConclusionsThe 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. 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. 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. 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. 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. •First-time classification and characterization of ultrasound-based carotid plaques using 3-D optimization of deep convolution neural networks with varying augmentation and layers of the deep CNN: Atheromatic™ 2.0 (AtheroPoint™, Roseville, CA, USA).•Comparison of seven Artificial Intelligence (AI) models, its generalization and benchmarking against Atheromatic™ 1.0 (AtheroPoint™, Roseville, CA, USA).•Performance evaluation using statistical techniques namely DOR, power analysis, Atheromatic™ SI, and Kappa analysis.•Comparison between local computer vs. supercomputer frameworks. Background and PurposeAtherosclerotic 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.MethodsWe 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.ResultsAfter 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.ConclusionsThe 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. 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. 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. 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. 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. 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.BACKGROUND AND PURPOSEAtherosclerotic 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.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.METHODSWe 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.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.RESULTSAfter 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.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.CONCLUSIONSThe 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. |
| ArticleNumber | 103958 |
| Author | Saba, Luca Viskovic, Klaudija Sharma, Aditya M. Miner, Martin Viswanathan, Vijay Nicolaides, Andrew Mavrogeni, Sophie Gupta, Suneet K. Cuadrado-Godia, Elisa Suri, Jasjit S. Sfikakis, Petros P. Turk, Monika Skandha, Sanagala S. Protogerou, Athanasios Misra, Durga P. Agarwal, Vikas Kolluri, Raghu Rathore, Vijay S. Johri, Amer M. Pareek, Gyan Koppula, Vijaya K. Laird, John R. Kitas, George D. Khanna, Narendra N. |
| Author_xml | – sequence: 1 givenname: Sanagala S. surname: Skandha fullname: Skandha, Sanagala S. organization: CSE Department, CMR College of Engineering & Technology, Hyderabad, India – sequence: 2 givenname: Suneet K. surname: Gupta fullname: Gupta, Suneet K. organization: CSE Department, Bennett University, Greater Noida, UP, India – sequence: 3 givenname: Luca surname: Saba fullname: Saba, Luca organization: Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy – sequence: 4 givenname: Vijaya K. surname: Koppula fullname: Koppula, Vijaya K. organization: CSE Department, CMR College of Engineering & Technology, Hyderabad, India – sequence: 5 givenname: Amer M. surname: Johri fullname: Johri, Amer M. organization: Department of Medicine, Division of Cardiology, Queen's University, Kingston, Ontario, Canada – sequence: 6 givenname: Narendra N. surname: Khanna fullname: Khanna, Narendra N. organization: Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India – sequence: 7 givenname: Sophie surname: Mavrogeni fullname: Mavrogeni, Sophie organization: Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece – sequence: 8 givenname: John R. surname: Laird fullname: Laird, John R. organization: Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA – sequence: 9 givenname: Gyan surname: Pareek fullname: Pareek, Gyan organization: Minimally Invasive Urology Institute, Brown University, Providence, RI, USA – sequence: 10 givenname: Martin surname: Miner fullname: Miner, Martin organization: Men's Health Center, Miriam Hospital Providence, RI, USA – sequence: 11 givenname: Petros P. surname: Sfikakis fullname: Sfikakis, Petros P. organization: Rheumatology Unit, National Kapodistrian University of Athens, Greece – sequence: 12 givenname: Athanasios surname: Protogerou fullname: Protogerou, Athanasios organization: Department of Cardiovascular Prevention, National and Kapodistrian Univ. of Athens, Greece – sequence: 13 givenname: Durga P. surname: Misra fullname: Misra, Durga P. organization: Dept. of Clinical Immunology and Rheumatology, SGPGIMS, Lucknow, India – sequence: 14 givenname: Vikas surname: Agarwal fullname: Agarwal, Vikas organization: Dept. of Clinical Immunology and Rheumatology, SGPGIMS, Lucknow, India – sequence: 15 givenname: Aditya M. surname: Sharma fullname: Sharma, Aditya M. organization: Division of Cardiovascular Medicine, University of Virginia, VA, USA – sequence: 16 givenname: Vijay surname: Viswanathan fullname: Viswanathan, Vijay organization: MV Hospital for Diabetes & Professor M Viswanathan Diabetes Research Centre, Chennai, India – sequence: 17 givenname: Vijay S. surname: Rathore fullname: Rathore, Vijay S. organization: Nephrology Department, Kaiser Permanente, Sacramento, CA, USA – sequence: 18 givenname: Monika surname: Turk fullname: Turk, Monika organization: The Hanse-Wissenschaftskolleg Institute for Advanced Study, Delmenhorst, Germany – sequence: 19 givenname: Raghu surname: Kolluri fullname: Kolluri, Raghu organization: OhioHealth Heart and Vascular, Ohio, USA – sequence: 20 givenname: Klaudija surname: Viskovic fullname: Viskovic, Klaudija organization: University Hospital for Infectious Diseases, Zagreb, Croatia – sequence: 21 givenname: Elisa surname: Cuadrado-Godia fullname: Cuadrado-Godia, Elisa organization: IMIM - Hospital Del Mar, Passeig Marítim, Barcelona, Spain – sequence: 22 givenname: George D. surname: Kitas fullname: Kitas, George D. organization: R & D Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK – sequence: 23 givenname: Andrew surname: Nicolaides fullname: Nicolaides, Andrew organization: Vascular Screening and Diagnostic Centre, University of Nicosia, Nicosia, Cyprus – sequence: 24 givenname: Jasjit S. surname: Suri fullname: Suri, Jasjit S. email: jasjit.suri@atheropoint.com organization: Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32927257$$D View this record in MEDLINE/PubMed |
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| Copyright | 2020 Elsevier Ltd Elsevier Ltd Copyright © 2020 Elsevier Ltd. All rights reserved. 2020. Elsevier Ltd |
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| DOI | 10.1016/j.compbiomed.2020.103958 |
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| Keywords | Supercomputer deep learning Carotid plaque symptomatic Accuracy Atherosclerosis Machine learning Asymptomatic And speed Performance Artificial intelligence ultrasound |
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| Snippet | Atherosclerotic plaque tissue rupture is one of the leading causes of strokes. Early carotid plaque monitoring can help reduce cardiovascular morbidity and... AbstractBackground and PurposeAtherosclerotic plaque tissue rupture is one of the leading causes of strokes. Early carotid plaque monitoring can help reduce... Background and PurposeAtherosclerotic plaque tissue rupture is one of the leading causes of strokes. Early carotid plaque monitoring can help reduce... |
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| SubjectTerms | Accuracy And speed Arteriosclerosis Artificial intelligence Artificial neural networks Asymptomatic Atherosclerosis Automation CAD Cardiovascular disease Cardiovascular diseases Carotid plaque Classification Classification systems Collagen Computer aided design Coronary vessels Data augmentation Deep learning Internal Medicine Learning algorithms Lipids Machine learning Magnetic resonance imaging Medical imaging Morbidity Mortality Neural networks Other Statistical methods Supercomputer Support vector machines symptomatic Transfer learning Ultrasonic imaging Ultrasound Wavelet transforms |
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| Title | 3-D optimized classification and characterization artificial intelligence paradigm for cardiovascular/stroke risk stratification using carotid ultrasound-based delineated plaque: Atheromatic™ 2.0 |
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