The Experts below are selected from a list of 83169 Experts worldwide ranked by ideXlab platform
Jasjit S Suri - One of the best experts on this subject based on the ideXlab platform.
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a survey on coronary atherosclerotic plaque Tissue Characterization in intravascular optical coherence tomography
Current Atherosclerosis Reports, 2018Co-Authors: Alberto Boi, Luca Saba, Ankush D Jamthikar, Deep Gupta, Aditya M Sharma, Bruno Loi, John R Laird, Narendra N Khanna, Jasjit S SuriAbstract:Atherosclerotic plaque deposition within the coronary vessel wall leads to arterial stenosis and severe catastrophic events over time. Identification of these atherosclerotic plaque components is essential to pre-estimate the risk of cardiovascular disease (CVD) and stratify them as a high or low risk. The Characterization and quantification of coronary plaque components are not only vital but also a challenging task which can be possible using high-resolution imaging techniques. Atherosclerotic plaque components such as thin cap fibroatheroma (TCFA), fibrous cap, macrophage infiltration, large necrotic core, and thrombus are the microstructural plaque components that can be detected with only high-resolution imaging modalities such as intravascular ultrasound (IVUS) and optical coherence tomography (OCT). Light-based OCT provides better visualization of plaque Tissue layers of coronary vessel walls as compared to IVUS. Three dominant paradigms have been identified to characterize atherosclerotic plaque components based on optical attenuation coefficients, machine learning algorithms, and deep learning techniques. This review (condensation of 126 papers after downloading 150 articles) presents a detailed comparison among various methodologies utilized for plaque Tissue Characterization, classification, and arterial measurements in OCT. Furthermore, this review presents the different ways to predict and stratify the risk associated with the CVD based on plaque Characterization and measurements in OCT. Moreover, this review discovers three different paradigms for plaque Characterization and their pros and cons. Among all of the techniques, a combination of machine learning and deep learning techniques is a best possible solution that provides improved OCT-based risk stratification.
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symtosis a liver ultrasound Tissue Characterization and risk stratification in optimized deep learning paradigm
Computer Methods and Programs in Biomedicine, 2018Co-Authors: Mainak Biswas, Luca Saba, Venkatanareshbabu Kuppili, Damodar Reddy Edla, Harman S Suri, Rui Tato Marinhoe, Miguel J Sanches, Jasjit S SuriAbstract:Abstract Background and Objective Fatty Liver Disease (FLD) - a disease caused by deposition of fat in liver cells, is predecessor to terminal diseases such as liver cancer. The machine learning (ML) techniques applied for FLD detection and risk stratification using ultrasound (US) have limitations in computing Tissue Characterization features, thereby limiting the accuracy. Methods Under the class of Symtosis for FLD detection and risk stratification, this study presents a Deep Learning (DL)-based paradigm that computes nearly seven million weights per image when passed through a 22 layered neural network during the cross-validation (training and testing) paradigm. The DL architecture consists of cascaded layers of operations such as: convolution, pooling, rectified linear unit, dropout and a special block called inception model that provides speed and efficiency. All data analysis is performed in optimized Tissue region, obtained by removing background information. We benchmark the DL system against the conventional ML protocols: support vector machine (SVM) and extreme learning machine (ELM). Results The liver US data consists of 63 patients (27 normal/36 abnormal). Using the K10 cross-validation protocol (90% training and 10% testing), the detection and risk stratification accuracies are: 82%, 92% and 100% for SVM, ELM and DL systems, respectively. The corresponding area under the curve is: 0.79, 0.92 and 1.0, respectively. We further validate our DL system using two class biometric facial data that yields an accuracy of 99%. Conclusion DL system shows a superior performance for liver detection and risk stratification compared to conventional machine learning systems: SVM and ELM.
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understanding symptomatology of atherosclerotic plaque by image based Tissue Characterization
Computer Methods and Programs in Biomedicine, 2013Co-Authors: Rajendra U Acharya, Oliver Faust, Vinitha S Sree, Ang Peng Chuan Alvin, Ganapathy Krishnamurthi, Jose Seabra, Joao Sanches, Jasjit S SuriAbstract:Characterization of carotid atherosclerosis and classification into either symptomatic or asymptomatic is crucial in terms of diagnosis and treatment planning for a range of cardiovascular diseases. This paper presents a computer-aided diagnosis (CAD) system (Atheromatic) that analyzes ultrasound images and classifies them into symptomatic and asymptomatic. The classification result is based on a combination of discrete wavelet transform, higher order spectra (HOS) and textural features. In this study, we compare support vector machine (SVM) classifiers with different kernels. The classifier with a radial basis function (RBF) kernel achieved an average accuracy of 91.7% as well as a sensitivity of 97%, and specificity of 80%. Thus, it is evident that the selected features and the classifier combination can efficiently categorize plaques into symptomatic and asymptomatic classes. Moreover, a novel symptomatic asymptomatic carotid index (SACI), which is an integrated index that is based on the significant features, has been proposed in this work. Each analyzed ultrasound image yields on SACI number. A high SACI value indicates that the image shows symptomatic and low value indicates asymptomatic plaques. We hope this SACI can support vascular surgeons during routine screening for asymptomatic plaques.
John R Laird - One of the best experts on this subject based on the ideXlab platform.
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multimodality carotid plaque Tissue Characterization and classification in the artificial intelligence paradigm a narrative review for stroke application
Annals of Translational Medicine, 2021Co-Authors: Luca Saba, John R Laird, Narendra N Khanna, Skandha S Sanagala, Suneet K Gupta, Vijaya K Koppula, Amer M Johri, Sophie Mavrogeni, Gyan Pareek, Martin MinerAbstract:Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to Characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for Tissue Characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most.
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A Multicenter Study on Carotid Ultrasound Plaque Tissue Characterization and Classification Using Six Deep Artificial Intelligence Models: A Stroke Application
IEEE Transactions on Instrumentation and Measurement, 2021Co-Authors: Luca Saba, John R Laird, Miguel J Sanches, Skandha S Sanagala, Suneet K Gupta, Vijaya K Koppula, Amer M Johri, Vijay Viswanathan, George D. Kitas, Neeraj SharmaAbstract:Atherosclerotic plaque in carotid arteries can ultimately lead to cerebrovascular events if not monitored. The objectives of this study are (a) to design a set of artificial intelligence (AI)-based Tissue Characterization and classification (TCC) systems (Atheromatic 2.0, AtheroPoint, CA, USA) using ultrasound-based carotid artery plaque scans collected from multiple centers and (b) to evaluate the AI performance. We hypothesize that symptomatic plaque is more scattered than asymptomatic plaque. Therefore, the AI system can learn, characterize, and classify them automatically. We developed six kinds of AI systems: four machine learning (ML) systems, one transfer learning (TL) system, and one deep learning (DL) architecture with different layers. Atheromatic 2.0 uses two types of plaque Characterization: (a) an AI-based mean feature strength (MFS) and (b) bispectrum analysis. Three kinds of data were collected: London, Lisbon, and Combined (London + Lisbon). We balanced and then augmented five folds to conduct 3-D optimization for optimal number of AI layers versus folds. Using K10 (90% training, 10% testing), the mean accuracies for DL, TL, and ML over the mean of the three data sets were 93.55%, 94.55%, and 89%, respectively. The corresponding mean AUCs were 0.938, 0.946, and 0.889 (p <; 0.0001), respectively. AI paradigms showed an improvement by 10.41% and 3.32% for London and Lisbon in comparison to Atheromatic 1.0, respectively. On Characterization, for all three data sets, MFS (symptomatic) > MFS (asymptomatic) by 46.56%, 19.40%, and 53.84%, respectively, thus validating our hypothesis. Atheromatic 2.0 showed consistent and stable results and is useful for carotid plaque Tissue classification and Characterization for vascular surgery applications.
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rheumatoid arthritis atherosclerosis imaging and cardiovascular risk assessment using machine and deep learning based Tissue Characterization
Current Atherosclerosis Reports, 2019Co-Authors: Narendra N Khanna, Luca Saba, Ankush D Jamthikar, Deep Gupta, Matteo Piga, Carlo Carcassi, Argiris Giannopoulos, A N Nicolaides, John R LairdAbstract:Rheumatoid arthritis (RA) is a chronic, autoimmune disease which may result in a higher risk of cardiovascular (CV) events and stroke. Tissue Characterization and risk stratification of patients with rheumatoid arthritis are a challenging problem. Risk stratification of RA patients using traditional risk factor–based calculators either underestimates or overestimates the CV risk. Advancements in medical imaging have facilitated early and accurate CV risk stratification compared to conventional cardiovascular risk calculators. In recent years, a link between carotid atherosclerosis and rheumatoid arthritis has been widely discussed by multiple studies. Imaging the carotid artery using 2-D ultrasound is a noninvasive, economic, and efficient imaging approach that provides an atherosclerotic plaque Tissue–specific image. Such images can help to morphologically characterize the plaque type and accurately measure vital phenotypes such as media wall thickness and wall variability. Intelligence-based paradigms such as machine learning– and deep learning–based techniques not only automate the risk Characterization process but also provide an accurate CV risk stratification for better management of RA patients. This review provides a brief understanding of the pathogenesis of RA and its association with carotid atherosclerosis imaged using the B-mode ultrasound technique. Lacunas in traditional risk scores and the role of machine learning–based Tissue Characterization algorithms are discussed and could facilitate cardiovascular risk assessment in RA patients. The key takeaway points from this review are the following: (i) inflammation is a common link between RA and atherosclerotic plaque buildup, (ii) carotid ultrasound is a better choice to characterize the atherosclerotic plaque Tissues in RA patients, and (iii) intelligence-based paradigms are useful for accurate Tissue Characterization and risk stratification of RA patients.
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a survey on coronary atherosclerotic plaque Tissue Characterization in intravascular optical coherence tomography
Current Atherosclerosis Reports, 2018Co-Authors: Alberto Boi, Luca Saba, Ankush D Jamthikar, Deep Gupta, Aditya M Sharma, Bruno Loi, John R Laird, Narendra N Khanna, Jasjit S SuriAbstract:Atherosclerotic plaque deposition within the coronary vessel wall leads to arterial stenosis and severe catastrophic events over time. Identification of these atherosclerotic plaque components is essential to pre-estimate the risk of cardiovascular disease (CVD) and stratify them as a high or low risk. The Characterization and quantification of coronary plaque components are not only vital but also a challenging task which can be possible using high-resolution imaging techniques. Atherosclerotic plaque components such as thin cap fibroatheroma (TCFA), fibrous cap, macrophage infiltration, large necrotic core, and thrombus are the microstructural plaque components that can be detected with only high-resolution imaging modalities such as intravascular ultrasound (IVUS) and optical coherence tomography (OCT). Light-based OCT provides better visualization of plaque Tissue layers of coronary vessel walls as compared to IVUS. Three dominant paradigms have been identified to characterize atherosclerotic plaque components based on optical attenuation coefficients, machine learning algorithms, and deep learning techniques. This review (condensation of 126 papers after downloading 150 articles) presents a detailed comparison among various methodologies utilized for plaque Tissue Characterization, classification, and arterial measurements in OCT. Furthermore, this review presents the different ways to predict and stratify the risk associated with the CVD based on plaque Characterization and measurements in OCT. Moreover, this review discovers three different paradigms for plaque Characterization and their pros and cons. Among all of the techniques, a combination of machine learning and deep learning techniques is a best possible solution that provides improved OCT-based risk stratification.
Xueding Wang - One of the best experts on this subject based on the ideXlab platform.
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frequency domain analysis of photoacoustic imaging data from prostate adenocarcinoma tumors in a murine model
Ultrasound in Medicine and Biology, 2011Co-Authors: Ronald E Kumon, Cheri X Deng, Xueding WangAbstract:Photoacoustic imaging is an emerging technique for anatomical and functional sub-surface imaging but previous studies have predominantly focused on time-domain analysis. In this study, frequency-domain analysis of the radio-frequency signals from photoacoustic imaging was performed to generate quantitative parameters for Tissue Characterization. To account for the response of the imaging system, the photoacoustic spectra were calibrated by dividing the photoacoustic spectra (radio-frequency ultrasound spectra resulting from laser excitation) from Tissue by the photoacoustic spectrum of a point absorber excited under the same conditions. The resulting quasi-linear photoacoustic spectra were fit by linear regression and midband fit, slope and intercept were computed from the best-fit line. These photoacoustic spectral parameters were compared between the region-of-interests (ROIs) representing prostate adenocarcinoma tumors and adjacent normal flank Tissue in a murine model. The mean midband fit and intercept in the ROIs showed significant differences between cancerous and noncancerous regions. These initial results suggest that such frequency-domain analysis can provide a quantitative method for tumor Tissue Characterization using photoacoustic imaging in vivo.
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frequency domain analysis of photoacoustic imaging data from prostate adenocarcinoma tumors in a murine model
Ultrasound in Medicine and Biology, 2011Co-Authors: Ronald E Kumon, Cheri X Deng, Xueding WangAbstract:Abstract Photoacoustic imaging is an emerging technique for anatomical and functional sub-surface imaging but previous studies have predominantly focused on time-domain analysis. In this study, frequency-domain analysis of the radio-frequency signals from photoacoustic imaging was performed to generate quantitative parameters for Tissue Characterization. To account for the response of the imaging system, the photoacoustic spectra were calibrated by dividing the photoacoustic spectra (radio-frequency ultrasound spectra resulting from laser excitation) from Tissue by the photoacoustic spectrum of a point absorber excited under the same conditions. The resulting quasi-linear photoacoustic spectra were fit by linear regression and midband fit, slope and intercept were computed from the best-fit line. These photoacoustic spectral parameters were compared between the region-of-interests (ROIs) representing prostate adenocarcinoma tumors and adjacent normal flank Tissue in a murine model. The mean midband fit and intercept in the ROIs showed significant differences between cancerous and noncancerous regions. These initial results suggest that such frequency-domain analysis can provide a quantitative method for tumor Tissue Characterization using photoacoustic imaging in vivo . (E-mail: cxdeng@umich.edu and xdwang@umich.edu )
Matthew M Cheung - One of the best experts on this subject based on the ideXlab platform.
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mr diffusion kurtosis imaging for neural Tissue Characterization
NMR in Biomedicine, 2010Co-Authors: E X Wu, Matthew M CheungAbstract:In conventional diffusion tensor imaging (DTI), water diffusion distribution is described as a 2nd-order three-dimensional (3D) diffusivity tensor. It assumes that diffusion occurs in a free and unrestricted environment with a Gaussian distribution of diffusion displacement, and consequently that diffusion weighted (DW) signal decays with diffusion factor (b-value) monoexponentially. In biological Tissue, complex cellular microstructures make water diffusion a highly hindered or restricted process. Non-monoexponential decays are experimentally observed in both white matter and gray matter. As a result, DTI quantitation is b-value dependent and DTI fails to fully utilize the diffusion measurements that are inherent to Tissue microstructure. Diffusion kurtosis imaging (DKI) characterizes restricted diffusion and can be readily implemented on most clinical scanners. It provides a higher-order description of water diffusion process by a 2nd-order 3D diffusivity tensor as in conventional DTI together with a 4th-order 3D kurtosis tensor. Because kurtosis is a measure of the deviation of the diffusion displacement profile from a Gaussian distribution, DKI analyses quantify the degree of diffusion restriction or Tissue complexity without any biophysical assumption. In this work, the theory of diffusion kurtosis and DKI including the directional kurtosis analysis is revisited. Several recent rodent DKI studies from our group are summarized, and DKI and DTI compared for their efficacy in detecting neural Tissue alterations. They demonstrate that DKI offers a more comprehensive approach than DTI in describing the complex water diffusion process in vivo. By estimating both diffusivity and kurtosis, it may provide improved sensitivity and specificity in MR diffusion Characterization of neural Tissues. Copyright © 2010 John Wiley & Sons, Ltd.
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mr diffusion kurtosis imaging for neural Tissue Characterization
NMR in Biomedicine, 2010Co-Authors: Matthew M CheungAbstract:In conventional diffusion tensor imaging (DTI), water diffusion distribution is described as a 2nd-order three-dimensional (3D) diffusivity tensor. It assumes that diffusion occurs in a free and unrestricted environment with a Gaussian distribution of diffusion displacement, and consequently that diffusion weighted (DW) signal decays with diffusion factor (b-value) monoexponentially. In biological Tissue, complex cellular microstructures make water diffusion a highly hindered or restricted process. Non-monoexponential decays are experimentally observed in both white matter and gray matter. As a result, DTI quantitation is b-value dependent and DTI fails to fully utilize the diffusion measurements that are inherent to Tissue microstructure. Diffusion kurtosis imaging (DKI) characterizes restricted diffusion and can be readily implemented on most clinical scanners. It provides a higher-order description of water diffusion process by a 2nd-order 3D diffusivity tensor as in conventional DTI together with a 4th-order 3D kurtosis tensor. Because kurtosis is a measure of the deviation of the diffusion displacement profile from a Gaussian distribution, DKI analyses quantify the degree of diffusion restriction or Tissue complexity without any biophysical assumption. In this work, the theory of diffusion kurtosis and DKI including the directional kurtosis analysis is revisited. Several recent rodent DKI studies from our group are summarized, and DKI and DTI compared for their efficacy in detecting neural Tissue alterations. They demonstrate that DKI offers a more comprehensive approach than DTI in describing the complex water diffusion process in vivo. By estimating both diffusivity and kurtosis, it may provide improved sensitivity and specificity in MR diffusion Characterization of neural Tissues.
Luca Saba - One of the best experts on this subject based on the ideXlab platform.
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multimodality carotid plaque Tissue Characterization and classification in the artificial intelligence paradigm a narrative review for stroke application
Annals of Translational Medicine, 2021Co-Authors: Luca Saba, John R Laird, Narendra N Khanna, Skandha S Sanagala, Suneet K Gupta, Vijaya K Koppula, Amer M Johri, Sophie Mavrogeni, Gyan Pareek, Martin MinerAbstract:Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to Characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for Tissue Characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most.
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A Multicenter Study on Carotid Ultrasound Plaque Tissue Characterization and Classification Using Six Deep Artificial Intelligence Models: A Stroke Application
IEEE Transactions on Instrumentation and Measurement, 2021Co-Authors: Luca Saba, John R Laird, Miguel J Sanches, Skandha S Sanagala, Suneet K Gupta, Vijaya K Koppula, Amer M Johri, Vijay Viswanathan, George D. Kitas, Neeraj SharmaAbstract:Atherosclerotic plaque in carotid arteries can ultimately lead to cerebrovascular events if not monitored. The objectives of this study are (a) to design a set of artificial intelligence (AI)-based Tissue Characterization and classification (TCC) systems (Atheromatic 2.0, AtheroPoint, CA, USA) using ultrasound-based carotid artery plaque scans collected from multiple centers and (b) to evaluate the AI performance. We hypothesize that symptomatic plaque is more scattered than asymptomatic plaque. Therefore, the AI system can learn, characterize, and classify them automatically. We developed six kinds of AI systems: four machine learning (ML) systems, one transfer learning (TL) system, and one deep learning (DL) architecture with different layers. Atheromatic 2.0 uses two types of plaque Characterization: (a) an AI-based mean feature strength (MFS) and (b) bispectrum analysis. Three kinds of data were collected: London, Lisbon, and Combined (London + Lisbon). We balanced and then augmented five folds to conduct 3-D optimization for optimal number of AI layers versus folds. Using K10 (90% training, 10% testing), the mean accuracies for DL, TL, and ML over the mean of the three data sets were 93.55%, 94.55%, and 89%, respectively. The corresponding mean AUCs were 0.938, 0.946, and 0.889 (p <; 0.0001), respectively. AI paradigms showed an improvement by 10.41% and 3.32% for London and Lisbon in comparison to Atheromatic 1.0, respectively. On Characterization, for all three data sets, MFS (symptomatic) > MFS (asymptomatic) by 46.56%, 19.40%, and 53.84%, respectively, thus validating our hypothesis. Atheromatic 2.0 showed consistent and stable results and is useful for carotid plaque Tissue classification and Characterization for vascular surgery applications.
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rheumatoid arthritis atherosclerosis imaging and cardiovascular risk assessment using machine and deep learning based Tissue Characterization
Current Atherosclerosis Reports, 2019Co-Authors: Narendra N Khanna, Luca Saba, Ankush D Jamthikar, Deep Gupta, Matteo Piga, Carlo Carcassi, Argiris Giannopoulos, A N Nicolaides, John R LairdAbstract:Rheumatoid arthritis (RA) is a chronic, autoimmune disease which may result in a higher risk of cardiovascular (CV) events and stroke. Tissue Characterization and risk stratification of patients with rheumatoid arthritis are a challenging problem. Risk stratification of RA patients using traditional risk factor–based calculators either underestimates or overestimates the CV risk. Advancements in medical imaging have facilitated early and accurate CV risk stratification compared to conventional cardiovascular risk calculators. In recent years, a link between carotid atherosclerosis and rheumatoid arthritis has been widely discussed by multiple studies. Imaging the carotid artery using 2-D ultrasound is a noninvasive, economic, and efficient imaging approach that provides an atherosclerotic plaque Tissue–specific image. Such images can help to morphologically characterize the plaque type and accurately measure vital phenotypes such as media wall thickness and wall variability. Intelligence-based paradigms such as machine learning– and deep learning–based techniques not only automate the risk Characterization process but also provide an accurate CV risk stratification for better management of RA patients. This review provides a brief understanding of the pathogenesis of RA and its association with carotid atherosclerosis imaged using the B-mode ultrasound technique. Lacunas in traditional risk scores and the role of machine learning–based Tissue Characterization algorithms are discussed and could facilitate cardiovascular risk assessment in RA patients. The key takeaway points from this review are the following: (i) inflammation is a common link between RA and atherosclerotic plaque buildup, (ii) carotid ultrasound is a better choice to characterize the atherosclerotic plaque Tissues in RA patients, and (iii) intelligence-based paradigms are useful for accurate Tissue Characterization and risk stratification of RA patients.
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a survey on coronary atherosclerotic plaque Tissue Characterization in intravascular optical coherence tomography
Current Atherosclerosis Reports, 2018Co-Authors: Alberto Boi, Luca Saba, Ankush D Jamthikar, Deep Gupta, Aditya M Sharma, Bruno Loi, John R Laird, Narendra N Khanna, Jasjit S SuriAbstract:Atherosclerotic plaque deposition within the coronary vessel wall leads to arterial stenosis and severe catastrophic events over time. Identification of these atherosclerotic plaque components is essential to pre-estimate the risk of cardiovascular disease (CVD) and stratify them as a high or low risk. The Characterization and quantification of coronary plaque components are not only vital but also a challenging task which can be possible using high-resolution imaging techniques. Atherosclerotic plaque components such as thin cap fibroatheroma (TCFA), fibrous cap, macrophage infiltration, large necrotic core, and thrombus are the microstructural plaque components that can be detected with only high-resolution imaging modalities such as intravascular ultrasound (IVUS) and optical coherence tomography (OCT). Light-based OCT provides better visualization of plaque Tissue layers of coronary vessel walls as compared to IVUS. Three dominant paradigms have been identified to characterize atherosclerotic plaque components based on optical attenuation coefficients, machine learning algorithms, and deep learning techniques. This review (condensation of 126 papers after downloading 150 articles) presents a detailed comparison among various methodologies utilized for plaque Tissue Characterization, classification, and arterial measurements in OCT. Furthermore, this review presents the different ways to predict and stratify the risk associated with the CVD based on plaque Characterization and measurements in OCT. Moreover, this review discovers three different paradigms for plaque Characterization and their pros and cons. Among all of the techniques, a combination of machine learning and deep learning techniques is a best possible solution that provides improved OCT-based risk stratification.
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symtosis a liver ultrasound Tissue Characterization and risk stratification in optimized deep learning paradigm
Computer Methods and Programs in Biomedicine, 2018Co-Authors: Mainak Biswas, Luca Saba, Venkatanareshbabu Kuppili, Damodar Reddy Edla, Harman S Suri, Rui Tato Marinhoe, Miguel J Sanches, Jasjit S SuriAbstract:Abstract Background and Objective Fatty Liver Disease (FLD) - a disease caused by deposition of fat in liver cells, is predecessor to terminal diseases such as liver cancer. The machine learning (ML) techniques applied for FLD detection and risk stratification using ultrasound (US) have limitations in computing Tissue Characterization features, thereby limiting the accuracy. Methods Under the class of Symtosis for FLD detection and risk stratification, this study presents a Deep Learning (DL)-based paradigm that computes nearly seven million weights per image when passed through a 22 layered neural network during the cross-validation (training and testing) paradigm. The DL architecture consists of cascaded layers of operations such as: convolution, pooling, rectified linear unit, dropout and a special block called inception model that provides speed and efficiency. All data analysis is performed in optimized Tissue region, obtained by removing background information. We benchmark the DL system against the conventional ML protocols: support vector machine (SVM) and extreme learning machine (ELM). Results The liver US data consists of 63 patients (27 normal/36 abnormal). Using the K10 cross-validation protocol (90% training and 10% testing), the detection and risk stratification accuracies are: 82%, 92% and 100% for SVM, ELM and DL systems, respectively. The corresponding area under the curve is: 0.79, 0.92 and 1.0, respectively. We further validate our DL system using two class biometric facial data that yields an accuracy of 99%. Conclusion DL system shows a superior performance for liver detection and risk stratification compared to conventional machine learning systems: SVM and ELM.
