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Kumari V, Kumar N, Kumar K S, Kumar A, Skandha SS, Saxena S, Khanna NN, Laird JR, Singh N, Fouda MM, Saba L, Singh R, Suri JS. Deep Learning Paradigm and Its Bias for Coronary Artery Wall Segmentation in Intravascular Ultrasound Scans: A Closer Look. J Cardiovasc Dev Dis 2023; 10:485. [PMID: 38132653 PMCID: PMC10743870 DOI: 10.3390/jcdd10120485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 10/15/2023] [Accepted: 11/07/2023] [Indexed: 12/23/2023] Open
Abstract
BACKGROUND AND MOTIVATION Coronary artery disease (CAD) has the highest mortality rate; therefore, its diagnosis is vital. Intravascular ultrasound (IVUS) is a high-resolution imaging solution that can image coronary arteries, but the diagnosis software via wall segmentation and quantification has been evolving. In this study, a deep learning (DL) paradigm was explored along with its bias. METHODS Using a PRISMA model, 145 best UNet-based and non-UNet-based methods for wall segmentation were selected and analyzed for their characteristics and scientific and clinical validation. This study computed the coronary wall thickness by estimating the inner and outer borders of the coronary artery IVUS cross-sectional scans. Further, the review explored the bias in the DL system for the first time when it comes to wall segmentation in IVUS scans. Three bias methods, namely (i) ranking, (ii) radial, and (iii) regional area, were applied and compared using a Venn diagram. Finally, the study presented explainable AI (XAI) paradigms in the DL framework. FINDINGS AND CONCLUSIONS UNet provides a powerful paradigm for the segmentation of coronary walls in IVUS scans due to its ability to extract automated features at different scales in encoders, reconstruct the segmented image using decoders, and embed the variants in skip connections. Most of the research was hampered by a lack of motivation for XAI and pruned AI (PAI) models. None of the UNet models met the criteria for bias-free design. For clinical assessment and settings, it is necessary to move from a paper-to-practice approach.
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Affiliation(s)
- Vandana Kumari
- School of Computer Science and Engineering, Galgotias University, Greater Noida 201310, India; (V.K.); (S.K.K.)
| | - Naresh Kumar
- Department of Applied Computational Science and Engineering, G L Bajaj Institute of Technology and Management, Greater Noida 201310, India
| | - Sampath Kumar K
- School of Computer Science and Engineering, Galgotias University, Greater Noida 201310, India; (V.K.); (S.K.K.)
| | - Ashish Kumar
- School of CSET, Bennett University, Greater Noida 201310, India;
| | - Sanagala S. Skandha
- Department of CSE, CMR College of Engineering and Technology, Hyderabad 501401, India;
| | - Sanjay Saxena
- Department of Computer Science and Engineering, IIT Bhubaneswar, Bhubaneswar 751003, India;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110076, India;
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA 94574, USA;
| | - Narpinder Singh
- Department of Food Science and Technology, Graphic Era, Deemed to be University, Dehradun 248002, India;
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA;
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09100 Cagliari, Italy;
| | - Rajesh Singh
- Department of Research and Innovation, Uttaranchal Institute of Technology, Uttaranchal University, Dehradun 248007, India;
| | - Jasjit S. Suri
- Stroke Diagnostics and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA
- Department of Computer Science & Engineering, Graphic Era, Deemed to be University, Dehradun 248002, India
- Monitoring and Diagnosis Division, AtheroPoint™, Roseville, CA 95661, USA
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Johri AM, Singh KV, Mantella LE, Saba L, Sharma A, Laird JR, Utkarsh K, Singh IM, Gupta S, Kalra MS, Suri JS. Deep learning artificial intelligence framework for multiclass coronary artery disease prediction using combination of conventional risk factors, carotid ultrasound, and intraplaque neovascularization. Comput Biol Med 2022; 150:106018. [PMID: 36174330 DOI: 10.1016/j.compbiomed.2022.106018] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 08/06/2022] [Accepted: 08/20/2022] [Indexed: 11/23/2022]
Abstract
OBJECTIVE Cardiovascular disease (CVD) is a major healthcare challenge and therefore early risk assessment is vital. Previous assessment techniques use either "conventional CVD risk calculators (CCVRC)" or machine learning (ML) paradigms. These techniques are ad-hoc, unreliable, not fully automated, and have variabilities. We, therefore, introduce AtheroEdge-MCDLAI (AE3.0DL) windows-based platform using multiclass Deep Learning (DL) system. METHODS Data was collected on 500 patients having both carotid ultrasound and corresponding coronary angiography scores (CAS), measured as stenosis in coronary arteries and considered as the gold standard. A total of 39 covariates were used, clubbed into three clusters, namely (i) Office-based: age, gender, body mass index, smoker, hypertension, systolic blood pressure, and diastolic blood pressure; (ii) Laboratory-based: Hyperlipidemia, hemoglobin A1c, and estimated glomerular filtration rate; and (iii) Carotid ultrasound image phenotypes: maximum plaque height, total plaque area, and intra-plaque neovascularization. Baseline characteristics for four classes (target labels) having significant (p < 0.0001) values were calculated using Chi-square and ANOVA. For handling the cohort's imbalance in the risk classes, AE3.0DL used the synthetic minority over-sampling technique (SMOTE). AE3.0DL used Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) DL models and the performance (accuracy and area-under-the-curve) was computed using 10-fold cross-validation (90% training, 10% testing) frameworks. AE3.0DL was validated and benchmarked. RESULTS The AE3.0DL using RNN and LSTM showed an accuracy and AUC (p < 0.0001) pairs as (95.00% and 0.98), and (95.34% and 0.99), respectively, and showed an improvement of 32.93% and 9.94% against CCVRC and ML, respectively. AE3.0DL runs in <1 s. CONCLUSION DL algorithms are a powerful paradigm for coronary artery disease (CAD) risk prediction and CVD risk stratification.
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Affiliation(s)
- Amer M Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, ON, Canada
| | | | - Laura E Mantella
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA
| | | | | | - Suneet Gupta
- Department of Computer Science, Bennett University, Gr. Noida, India
| | - Manudeep S Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, 02114, USA
| | - Jasjit S Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA, USA; Knowledge Engineering Center, Global Biomedical Technologies, Inc., Roseville, CA, USA.
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Jain PK, Dubey A, Saba L, Khanna NN, Laird JR, Nicolaides A, Fouda MM, Suri JS, Sharma N. Attention-Based UNet Deep Learning Model for Plaque Segmentation in Carotid Ultrasound for Stroke Risk Stratification: An Artificial Intelligence Paradigm. J Cardiovasc Dev Dis 2022; 9:326. [PMID: 36286278 PMCID: PMC9604424 DOI: 10.3390/jcdd9100326] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 09/06/2022] [Accepted: 09/14/2022] [Indexed: 11/17/2022] Open
Abstract
Stroke and cardiovascular diseases (CVD) significantly affect the world population. The early detection of such events may prevent the burden of death and costly surgery. Conventional methods are neither automated nor clinically accurate. Artificial Intelligence-based methods of automatically detecting and predicting the severity of CVD and stroke in their early stages are of prime importance. This study proposes an attention-channel-based UNet deep learning (DL) model that identifies the carotid plaques in the internal carotid artery (ICA) and common carotid artery (CCA) images. Our experiments consist of 970 ICA images from the UK, 379 CCA images from diabetic Japanese patients, and 300 CCA images from post-menopausal women from Hong Kong. We combined both CCA images to form an integrated database of 679 images. A rotation transformation technique was applied to 679 CCA images, doubling the database for the experiments. The cross-validation K5 (80% training: 20% testing) protocol was applied for accuracy determination. The results of the Attention-UNet model are benchmarked against UNet, UNet++, and UNet3P models. Visual plaque segmentation showed improvement in the Attention-UNet results compared to the other three models. The correlation coefficient (CC) value for Attention-UNet is 0.96, compared to 0.93, 0.96, and 0.92 for UNet, UNet++, and UNet3P models. Similarly, the AUC value for Attention-UNet is 0.97, compared to 0.964, 0.966, and 0.965 for other models. Conclusively, the Attention-UNet model is beneficial in segmenting very bright and fuzzy plaque images that are hard to diagnose using other methods. Further, we present a multi-ethnic, multi-center, racial bias-free study of stroke risk assessment.
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Affiliation(s)
- Pankaj K. Jain
- School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, India
| | - Abhishek Dubey
- School of Biomedical Engineering, Indian Institute of Technology (BHU), Varanasi 221005, India
- Department of Electronics and Communication, Shree Mata Vaishno Devi University, Jammu 182301, India
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), 09100 Cagliari, Italy
| | - Narender N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospital, New Delhi 110076, India
| | - John R. Laird
- Heart and Vascular Institute, Adventist Heath St. Helena, St. Helena, CA 94574, USA
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, Nicosia 2409, Cyprus
| | - Mostafa M. Fouda
- Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
| | - Jasjit S. Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA
| | - Neeraj Sharma
- Department of Electronics and Communication, Shree Mata Vaishno Devi University, Jammu 182301, India
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Anand A, Gurram NR. Automated Deep Learning-based Single-Step Diameter Estimation of Carotid Arteries in B-mode Ultrasound. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2022; 2022:434-437. [PMID: 36086205 DOI: 10.1109/embc48229.2022.9871254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Accurate measurement of blood vessel diameter on ultrasonic images is important in many vascular exams. In one of them, volumetric blood flow measurements, the volume flow rate is calculated by multiplying the time-averaged velocity with the cross-sectional area of the vessel (using diameter measured from B-mode images). Computation of lumen diameter is also vital for planning surgical procedures like carotid artery stenting and endarterectomy. More recently, several automated vessel diameter estimation methods employing deep learning have been proposed. In this paper, we propose a novel single-step automated deep learning-based vessel diameter estimation technique developed on B-mode images. Longitudinal images of the human common carotid artery were acquired by trained vascular sonographers in human subjects using a linear array probe. Ground truth measurements were obtained by a human expert to validate the proposed technique. 504 images (with augmentation) were divided into training, validation, and test sets. Three pre-trained deep learning networks were used for training, and the lumen diameter was predicted in a hold-out test set. The Mean Absolute Deviation (MAD) and Root Mean Square Error (RMSE) ranged from 0.22-0.65 mm and 0.32-0.82 mm, respectively, for the three networks. Furthermore, 5-fold cross-validation resulted in MAD and RMSE of 0.36±0.1 mm and 0.513±0.15 mm, respectively. Clinical Relevance- The results demonstrate that the technology can potentially be embedded in commercial scanners to make the workflow in vascular ultrasound more efficient.
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A hybrid deep learning paradigm for carotid plaque tissue characterization and its validation in multicenter cohorts using a supercomputer framework. Comput Biol Med 2021; 141:105131. [PMID: 34922173 DOI: 10.1016/j.compbiomed.2021.105131] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2021] [Revised: 11/20/2021] [Accepted: 12/09/2021] [Indexed: 12/12/2022]
Abstract
BACKGROUND Early and automated detection of carotid plaques prevents strokes, which are the second leading cause of death worldwide according to the World Health Organization. Artificial intelligence (AI) offers automated solutions for plaque tissue characterization. Recently, solo deep learning (SDL) models have been used, but they do not take advantage of the tandem connectivity offered by AI's hybrid nature. Therefore, this study explores the use of hybrid deep learning (HDL) models in a multicenter framework, making this study the first of its kind. METHODS We hypothesize that HDL techniques perform better than SDL and transfer learning (TL) techniques. We propose two kinds of HDL frameworks: (i) the fusion of two SDLs (Inception with ResNet) or (ii) 10 other kinds of tandem models that fuse SDL with ML. The system Atheromatic™ 2.0HDL (AtheroPoint, CA, USA) was designed on an augmentation framework and three kinds of loss functions (cross-entropy, hinge, and mean-square-error) during training to determine the best optimization paradigm. These 11 combined HDL models were then benchmarked against one SDL model and five types of TL models; thus, this study considers a total of 17 AI models. RESULTS Among the 17 AI models, the best performing HDL system was that comprising CNN and decision tree (DT), as its accuracy and area-under-the-curve were 99.78 ± 1.05% and 0.99 (p<0.0001), respectively. These values are 6.4% and 3.2% better than those recorded for the SDL and TL models, respectively. We validated the performance of the HDL models with diagnostics odds ratio (DOR) and Cohen and Kappa statistics; here, HDL outperformed DL and TL by 23% and 7%, respectively. The online system ran in <2 s. CONCLUSION HDL is a fast, reliable, and effective tool for characterizing the carotid plaque for early stroke risk stratification.
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Biswas M, Saba L, Omerzu T, Johri AM, Khanna NN, Viskovic K, Mavrogeni S, Laird JR, Pareek G, Miner M, Balestrieri A, Sfikakis PP, Protogerou A, Misra DP, Agarwal V, Kitas GD, Kolluri R, Sharma A, Viswanathan V, Ruzsa Z, Nicolaides A, Suri JS. A Review on Joint Carotid Intima-Media Thickness and Plaque Area Measurement in Ultrasound for Cardiovascular/Stroke Risk Monitoring: Artificial Intelligence Framework. J Digit Imaging 2021; 34:581-604. [PMID: 34080104 PMCID: PMC8329154 DOI: 10.1007/s10278-021-00461-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 03/19/2021] [Accepted: 05/04/2021] [Indexed: 02/06/2023] Open
Abstract
Cardiovascular diseases (CVDs) are the top ten leading causes of death worldwide. Atherosclerosis disease in the arteries is the main cause of the CVD, leading to myocardial infarction and stroke. The two primary image-based phenotypes used for monitoring the atherosclerosis burden is carotid intima-media thickness (cIMT) and plaque area (PA). Earlier segmentation and measurement methods were based on ad hoc conventional and semi-automated digital imaging solutions, which are unreliable, tedious, slow, and not robust. This study reviews the modern and automated methods such as artificial intelligence (AI)-based. Machine learning (ML) and deep learning (DL) can provide automated techniques in the detection and measurement of cIMT and PA from carotid vascular images. Both ML and DL techniques are examples of supervised learning, i.e., learn from "ground truth" images and transformation of test images that are not part of the training. This review summarizes (1) the evolution and impact of the fast-changing AI technology on cIMT/PA measurement, (2) the mathematical representations of ML/DL methods, and (3) segmentation approaches for cIMT/PA regions in carotid scans based for (a) region-of-interest detection and (b) lumen-intima and media-adventitia interface detection using ML/DL frameworks. AI-based methods for cIMT/PA segmentation have emerged for CVD/stroke risk monitoring and may expand to the recommended parameters for atherosclerosis assessment by carotid ultrasound.
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Affiliation(s)
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Tomaž Omerzu
- Department of Neurology, University Medical Centre Maribor, Maribor, Slovenia
| | - Amer M Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, ON, Canada
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
| | | | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, Rhode Island, USA
| | - Martin Miner
- Men's Health Center, Miriam Hospital Providence, Rhode Island, USA
| | - Antonella Balestrieri
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Petros P Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, Athens, Greece
| | | | | | - Vikas Agarwal
- Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow, UP, India
| | - George D Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester, UK
| | | | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA
| | - Vijay Viswanathan
- MV Hospital for Diabetes and Professor M Viswanathan Diabetes Research Centre, Chennai, India
| | - Zoltan Ruzsa
- Invasive Cardiology Division, University of Szeged, Budapest, Hungary
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, University of Nicosia Medical School, Nicosia, Cyprus
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA.
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Multiclass machine learning vs. conventional calculators for stroke/CVD risk assessment using carotid plaque predictors with coronary angiography scores as gold standard: a 500 participants study. Int J Cardiovasc Imaging 2020; 37:1171-1187. [DOI: 10.1007/s10554-020-02099-7] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Accepted: 11/03/2020] [Indexed: 02/07/2023]
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Viswanathan V, Jamthikar AD, Gupta D, Puvvula A, Khanna NN, Saba L, Viskovic K, Mavrogeni S, Turk M, Laird JR, Pareek G, Miner M, Ajuluchukwu J, Sfikakis PP, Protogerou A, Kitas GD, Nicolaides A, Sharma A, Suri JS. Integration of estimated glomerular filtration rate biomarker in image-based cardiovascular disease/stroke risk calculator: a south Asian-Indian diabetes cohort with moderate chronic kidney disease. INT ANGIOL 2020; 39:290-306. [PMID: 32214072 DOI: 10.23736/s0392-9590.20.04338-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
BACKGROUND Recently, a 10-year image-based integrated calculator (called AtheroEdge Composite Risk Score-AECRS1.0) was developed which combines conventional cardiovascular risk factors (CCVRF) with image phenotypes derived from carotid ultrasound (CUS). Such calculators did not include chronic kidney disease (CKD)-based biomarker called estimated glomerular filtration rate (eGFR). The novelty of this study is to design and develop an advanced integrated version called-AECRS2.0 that combines eGFR with image phenotypes to compute the composite risk score. Furthermore, AECRS2.0 was benchmarked against QRISK3 which considers eGFR for risk assessment. METHODS The method consists of three major steps: 1) five, current CUS image phenotypes (CUSIP) measurements using AtheroEdge system (AtheroPoint, CA, USA) consisting of: average carotid intima-media thickness (cIMTave), maximum cIMT (cIMTmax), minimum cIMT (cIMTmin), variability in cIMT (cIMTV), and total plaque area (TPA); 2) five, 10-year CUSIP measurements by combining these current five CUSIP with 11 CCVRF (age, ethnicity, gender, body mass index, systolic blood pressure, smoking, carotid artery type, hemoglobin, low-density lipoprotein cholesterol, total cholesterol, and eGFR); 3) AECRS2.0 risk score computation and its comparison to QRISK3 using area-under-the-curve (AUC). RESULTS South Asian-Indian 339 patients were retrospectively analyzed by acquiring their left/right common carotid arteries (678 CUS, mean age: 54.25±9.84 years; 75.22% males; 93.51% diabetic with HbA1c ≥6.5%; and mean eGFR 73.84±20.91 mL/min/1.73m<sup>2</sup>). The proposed AECRS2.0 reported higher AUC (AUC=0.89, P<0.001) compared to QRISK3 (AUC=0.51, P<0.001) by ~74% in CKD patients. CONCLUSIONS An integrated calculator AECRS2.0 can be used to assess the 10-year CVD/stroke risk in patients suffering from CKD. AECRS2.0 was much superior to QRISK3.
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Affiliation(s)
- Vijay Viswanathan
- MV Hospital for Diabetes and Professor M Viswanathan Diabetes Research Centre, Chennai, India
| | - Ankush D Jamthikar
- Department of Electronics and Communications, Visvesvaraya National Institute of Technology, Nagpur, India
| | - Deep Gupta
- Department of Electronics and Communications, Visvesvaraya National Institute of Technology, Nagpur, India
| | | | - Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
| | - Luca Saba
- Department of Radiology, University of Cagliari, Cagliari, Italy
| | - Klaudija Viskovic
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, Zagreb, Croatia
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece
| | - Monika Turk
- Department of Neurology, University Medical Center Maribor, Maribor, Slovenia
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI, USA
| | - Martin Miner
- Men's Health Center, Miriam Hospital, Providence, RI, USA
| | - Jna Ajuluchukwu
- Department of Medicine, LUTH (Lagos University Teaching Hospital), Lagos, Nigeria
| | - Petros P Sfikakis
- Unit of Rheumatology, National Kapodistrian University, Athens, Greece
| | - Athanasios Protogerou
- Department of Cardiovascular Prevention and, Research Unit Clinic, Laboratory of Pathophysiology, National and Kapodistrian University, Athens, Greece
| | - George D Kitas
- R & D Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Center, University of Nicosia Medical School, Nicosia, Cyprus
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA, USA
| | - Jasjit S Suri
- Division of Stroke Monitoring and Diagnostics, AtheroPoint™, Roseville, CA, USA -
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Puvvula A, Jamthikar AD, Gupta D, Khanna NN, Porcu M, Saba L, Viskovic K, Ajuluchukwu JNA, Gupta A, Mavrogeni S, Turk M, Laird JR, Pareek G, Miner M, Sfikakis PP, Protogerou A, Kitas GD, Nicolaides A, Viswanathan V, Suri JS. Morphological Carotid Plaque Area Is Associated With Glomerular Filtration Rate: A Study of South Asian Indian Patients With Diabetes and Chronic Kidney Disease. Angiology 2020; 71:520-535. [PMID: 32180436 DOI: 10.1177/0003319720910660] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
We evaluated the association between automatically measured carotid total plaque area (TPA) and the estimated glomerular filtration rate (eGFR), a biomarker of chronic kidney disease (CKD). Automated average carotid intima-media thickness (cIMTave) and TPA measurements in carotid ultrasound (CUS) were performed using AtheroEdge (AtheroPoint). Pearson correlation coefficient (CC) was then computed between the TPA and eGFR for (1) males versus females, (2) diabetic versus nondiabetic patients, and (3) between the left and right carotid artery. Overall, 339 South Asian Indian patients with either type 2 diabetes mellitus (T2DM) or CKD, or hypertension (stage 1 or stage 2) were retrospectively analyzed by acquiring cIMTave and TPA measurements of their left and right common carotid arteries (CCA; total CUS: 678, mean age: 54.2 ± 9.8 years; 75.2% males; 93.5% with T2DM). The CC between TPA and eGFR for different scenarios were (1) for males and females -0.25 (P < .001) and -0.35 (P < .001), respectively; (2) for T2DM and non-T2DM -0.26 (P < .001) and -0.49 (P = .02), respectively, and (3) for left and right CCA -0.25 (P < .001) and -0.23 (P < .001), respectively. Automated TPA is an equally reliable biomarker compared with cIMTave for patients with CKD (with or without T2DM) with subclinical atherosclerosis.
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Affiliation(s)
- Anudeep Puvvula
- Annu's Hospitals for Skin and Diabetes, Nellore, Andhra Pradesh, India
| | - Ankush D Jamthikar
- Department of Electronics and Communications Engineering, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, India
| | - Deep Gupta
- Department of Electronics and Communications Engineering, Visvesvaraya National Institute of Technology, Nagpur, Maharashtra, India
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha Apollo Hospitals, New Delhi, Delhi, India
| | - Michele Porcu
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari, Italy
| | - Klaudija Viskovic
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, Zagreb, Croatia
| | | | - Ajay Gupta
- Department of Radiology, Weill Cornell Medicine, New York City, NY, USA
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece
| | - Monika Turk
- Department of Neurology, University Medical Centre Maribor, Slovenia
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St Helena, St Helena, CA, USA
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI, USA
| | - Martin Miner
- Men's Health Center, Miriam Hospital, Providence, RI, USA
| | - Petros P Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, Greece
| | - Athanasios Protogerou
- Department of Cardiovascular Prevention and Research Unit Clinic and Laboratory of Pathophysiology, National and Kapodistrian University of Athens, Greece
| | - George D Kitas
- R & D Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, United Kingdom
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre and University of Nicosia Medical School, Cyprus
| | - Vijay Viswanathan
- M. V. Hospital for Diabetes and Professor M. Viswanathan Diabetes Research Centre, Chennai, Tamil Nadu, India
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint, Roseville, CA, USA
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Saba L, Jamthikar A, Gupta D, Khanna NN, Viskovic K, Suri HS, Gupta A, Mavrogeni S, Turk M, Laird JR, Pareek G, Miner M, Sfikakis PP, Protogerou A, Kitas GD, Viswanathan V, Nicolaides A, Bhatt DL, Suri JS. Global perspective on carotid intima-media thickness and plaque: should the current measurement guidelines be revisited? INT ANGIOL 2019; 38:451-465. [PMID: 31782286 DOI: 10.23736/s0392-9590.19.04267-6] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Carotid intima-media thickness (cIMT) and carotid plaque (CP) currently act as risk predictors for CVD/Stroke risk assessment. Over 2000 articles have been published that cover either use cIMT/CP or alterations of cIMT/CP and additional image-based phenotypes to associate cIMT related markers with CVD/Stroke risk. These articles have shown variable results, which likely reflect a lack of standardization in the tools for measurement, risk stratification, and risk assessment. Guidelines for cIMT/CP measurement are influenced by major factors like the atherosclerosis disease itself, conventional risk factors, 10-year measurement tools, types of CVD/Stroke risk calculators, incomplete validation of measurement tools, and the fast pace of computer technology advancements. This review discusses the following major points: 1) the American Society of Echocardiography and Mannheim guidelines for cIMT/CP measurements; 2) forces that influence the guidelines; and 3) calculators for risk stratification and assessment under the influence of advanced intelligence methods. The review also presents the knowledge-based learning strategies such as machine and deep learning which may play a future role in CVD/stroke risk assessment. We conclude that both machine learning and non-machine learning strategies will flourish for current and 10-year CVD/Stroke risk prediction as long as they integrate image-based phenotypes with conventional risk factors.
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Affiliation(s)
- Luca Saba
- Department of Radiology, University of Cagliari, Cagliari, Italy
| | - Ankush Jamthikar
- Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur, India
| | - Deep Gupta
- Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur, India
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
| | - Klaudija Viskovic
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, Zagreb, Croatia
| | | | - Ajay Gupta
- Department of Radiology, Weill Cornell Medicine, New York, NY, USA
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece
| | - Monika Turk
- Department of Neurology, University Medical Center Maribor, Maribor, Slovenia
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI, USA
| | - Martin Miner
- Men's Health Center, Miriam Hospital, Providence, RI, USA
| | - Petros P Sfikakis
- Unit of Rheumatology, National Kapodistrian University of Athens, Athens, Greece
| | - Athanasios Protogerou
- Department of Cardiovascular Prevention and Research, Clinic and Laboratory of Pathophysiology, National and Kapodistrian, University of Athens, Athens, Greece
| | - George D Kitas
- R and D Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK
| | - Vijay Viswanathan
- MV Hospital for Diabete, Professor M Viswanathan Diabetes Research Center, Chennai, India
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Center, University of Nicosia Medical School, Nicosia, Cyprus
| | - Deepak L Bhatt
- Brigham and Women's Hospital Heart, Vascular Center, Harvard Medical School, Boston, MA, USA
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA -
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11
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Cuadrado-Godia E, Srivastava SK, Saba L, Araki T, Suri HS, Giannopolulos A, Omerzu T, Laird J, Khanna NN, Mavrogeni S, Kitas GD, Nicolaides A, Suri JS. Geometric Total Plaque Area Is an Equally Powerful Phenotype Compared With Carotid Intima-Media Thickness for Stroke Risk Assessment: A Deep Learning Approach. ACTA ACUST UNITED AC 2018. [DOI: 10.1177/1544316718806421] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Currently, carotid intima-media thickness (cIMT) and geometric total plaque area (gTPA) are computed manually and thus are tedious and prone to interobserver and intraobserver variabilities. This study presents an intelligence-based automated deep learning (DL)–based technique for carotid wall interface detection, cIMT, and lumen diameter (LD) measurements, followed by a 3D cylindrical approach for TPA measurement. The observers were used for manual tracings of which were then used for the design of two DL-based systems. The DL boundaries for inner lumen wall and outer interadventitial borders were used for computing the cIMT and LD. Using cylindrical approach, we computed the gTPA. Furthermore, we compute the 10-year image-based cIMT and gTPA, using the progression rates. A total of 396 B-mode ultrasound right and left common carotid artery images were taken from 203 patients. The mean cIMT and gTPA using DL1 and DL2 is 0.91 mm, 20.52 mm2 and 0.88 mm, 19.44 mm2, respectively. The coefficient of correlation between gTPA and cIMT using DL1 and DL2 is 0.92 ( P < .001) and 0.94 ( P < .001), respectively. The area under the curve (AUC) for gTPA showed an improvement over cIMT by 14.36% and 12.57% for DL1 and DL2, respectively. The corresponding 10-year risk improvements were 9.09% and 6.26%. Our statistical significance tests successfully passed t test, Mann-Whitney, Wilcoxon, Kolmogorov-Smirnov, and Friedman. The study shows gTPA as an equally powerful carotid risk biomarker like cIMT. Given the cIMT and LD, cylindrical fitting is a fast method for gTPA measurements.
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Affiliation(s)
| | | | - Luca Saba
- Azienda Ospedaliero Universitaria, Cagliari, Italy
| | | | | | | | | | | | | | | | - George D. Kitas
- The University of Manchester, UK
- The Dudley Group NHS Foundation Trust, UK
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12
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Kumar PK, Araki T, Rajan J, Laird JR, Nicolaides A, Suri JS. State-of-the-art review on automated lumen and adventitial border delineation and its measurements in carotid ultrasound. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 163:155-168. [PMID: 30119850 DOI: 10.1016/j.cmpb.2018.05.015] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/12/2017] [Revised: 04/29/2018] [Accepted: 05/09/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE Accurate, reliable, efficient, and precise measurements of the lumen geometry of the common carotid artery (CCA) are important for (a) managing the progression/regression of atherosclerotic build-up and (b) the risk of stroke. The image-based degree of stenosis in the carotid artery and the plaque burden can be predicted using the automated carotid lumen diameter (LD)/inter-adventitial diameter (IAD) measurements from B-mode ultrasound images. The objective of this review is to present the state-of-the-art methods and systems for the measurement of LD/IAD in CCA based on automated or semi-automated strategies. Further, the performance of these systems is compared based on various metrics for its measurements. METHODS The automated algorithms proposed for the segmentation of carotid lumen are broadly classified into two different categories as: region-based and boundary-based. These techniques are discussed in detail specifying their pros and cons. Further, we discuss the challenges encountered in the segmentation process along with its quantitative assessment. Lastly, we present stenosis quantification and risk stratification strategies. RESULTS Even though, we have found more boundary-based approaches compared to region-based approaches in the literature, however, the region-based strategy yield more satisfactory performance. Novel risk stratification strategies are presented. On a patient database containing 203 patients, 9 patients are identified as high risk patients, whereas 27 patients are identified as medium risk patients. CONCLUSIONS We have presented different techniques for the lumen segmentation of the common carotid artery from B-mode ultrasound images and measurement of lumen diameter and inter-adventitial diameter. We believe that the issue regarding boundary-based techniques can be compensated by taking regional statistics embedded with boundary-based information.
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Affiliation(s)
- P Krishna Kumar
- Department of Computer Science and Engineering, National Institute of Technology Calicut, Kerala, India
| | - Tadashi Araki
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Jeny Rajan
- Department of Computer Science and Engineering, National Institute of Technology Karnataka, Surathkal, India
| | - John R Laird
- Heart and Vascular Institute, Adventist Health, St. Helena, CA, USA
| | | | - Jasjit S Suri
- Stroke Monitoring Division, AtheroPoint, Roseville, CA, USA; Department of Electrical Engineering, University of Idaho (Affl.), ID, USA.
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13
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Cuadrado-Godia E, Maniruzzaman M, Araki T, Puvvula A, Jahanur Rahman M, Saba L, Suri HS, Gupta A, Banchhor SK, Teji JS, Omerzu T, Khanna NN, Laird JR, Nicolaides A, Mavrogeni S, Kitas GD, Suri JS. Morphologic TPA (mTPA) and composite risk score for moderate carotid atherosclerotic plaque is strongly associated with HbA1c in diabetes cohort. Comput Biol Med 2018; 101:128-145. [PMID: 30138774 DOI: 10.1016/j.compbiomed.2018.08.008] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 08/05/2018] [Accepted: 08/05/2018] [Indexed: 12/11/2022]
Abstract
BACKGROUND This study examines the association between six types of carotid artery disease image-based phenotypes and HbA1c in diabetes patients. Six phenotypes (intima-media thickness measurements (cIMT (ave.), cIMT (max.), cIMT (min.)), bidirectional wall variability (cIMTV), morphology-based total plaque area (mTPA), and composite risk score (CRS)) were measured in an automated setting using AtheroEdge™ (AtheroPoint, CA, USA). METHOD Consecutive 199 patients (157 M, age: 68.96 ± 10.98 years), L/R common carotid artery (CCA; 398 US scans) who underwent a carotid ultrasound (L/R) were retrospectively analyzed using AtheroEdge™ system. Two operators (novice and experienced) manually calibrated all the US scans using AtheroEdge™. Logistic regression (LR) and Odds ratio (OR) was computed and phenotypes were ranked. RESULTS The baseline results showed 150 low-risk patients (HbA1c < 6.50 mg/dl) and 49 high-risk patients (HbA1c ≥ 6.50 mg/dl). The fasting blood sugar (FBS) was highly associated with HbA1c (P < 0.001). Except for cIMTV, all phenotypes showed an OR > 1.0 (P < 0.001) for left common carotid artery (LCCA), right carotid artery (RCCA), and mean of left and right common carotid artery (MCCA). After adjusting the FBS, the OR for mTPA showed a higher risk for LCCA, RCCA, and MCCA. The coefficient of correlation (CC) between phenotypes and HbA1c were strong and inter-CC between cIMT and mTPA/CRS was above 0.9 (P < 0.001). The statistical tests showed that phenotypes were significantly associated with diabetes (P-value<0.0001). CONCLUSIONS All phenotypes using AtheroEdge™, except cIMTV, showed a strong association with HbA1c. mTPA and CRS were equally strong phenotypes as cIMT. The CRS phenotype showed the strongest relationship to HbA1c.
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Affiliation(s)
| | - Md Maniruzzaman
- Department of Statistics, University of Rajshahi and the JiVit A Project of John Hopkins University, Gaibandha, Bangladesh
| | - Tadashi Araki
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Anudeep Puvvula
- Annu's Hospitals for Skin and Diabetes, Nellore, Andra Pradesh, India
| | - Md Jahanur Rahman
- Department of Statistics, University of Rajshahi, Rajshahi, Bangladesh
| | - Luca Saba
- Department of Radiology, A.O.U., Italy
| | | | - Ajay Gupta
- Brain and Mind Research Institute and Department of Radiology, Weill Cornell Medical College, NY, USA
| | | | - Jagjit S Teji
- Department of Pediatrics, Ann and Robert H. Lurie Children's Hospital of Chicago, Northwestern University, Feinberg School of Medicine Mercy Hospital, Chicago, IL, USA
| | - Tomaž Omerzu
- Department of Neurology, University Medical Centre Maribor, Slovenia
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha Apollo Hospitals, New Delhi, India
| | - John R Laird
- Heart and Vascular Institute, Adventist Health, St. Helena, CA, USA
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, London, UK; Vascular Diagnostic Centre, University of Cyprus, Nicosia, Cyprus
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece
| | - George D Kitas
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester, UK; Department of Rheumatology, Group NHS Foundation Trust, Dudley, UK
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA.
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14
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Deep learning strategy for accurate carotid intima-media thickness measurement: An ultrasound study on Japanese diabetic cohort. Comput Biol Med 2018; 98:100-117. [DOI: 10.1016/j.compbiomed.2018.05.014] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Revised: 05/10/2018] [Accepted: 05/10/2018] [Indexed: 01/06/2023]
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15
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Banchhor SK, Londhe ND, Araki T, Saba L, Radeva P, Laird JR, Suri JS. Wall-based measurement features provides an improved IVUS coronary artery risk assessment when fused with plaque texture-based features during machine learning paradigm. Comput Biol Med 2017; 91:198-212. [PMID: 29100114 DOI: 10.1016/j.compbiomed.2017.10.019] [Citation(s) in RCA: 31] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2017] [Revised: 10/19/2017] [Accepted: 10/19/2017] [Indexed: 12/31/2022]
Abstract
BACKGROUND Planning of percutaneous interventional procedures involves a pre-screening and risk stratification of the coronary artery disease. Current screening tools use stand-alone plaque texture-based features and therefore lack the ability to stratify the risk. METHOD This IRB approved study presents a novel strategy for coronary artery disease risk stratification using an amalgamation of IVUS plaque texture-based and wall-based measurement features. Due to common genetic plaque makeup, carotid plaque burden was chosen as a gold standard for risk labels during training-phase of machine learning (ML) paradigm. Cross-validation protocol was adopted to compute the accuracy of the ML framework. A set of 59 plaque texture-based features was padded with six wall-based measurement features to show the improvement in stratification accuracy. The ML system was executed using principle component analysis-based framework for dimensionality reduction and uses support vector machine classifier for training and testing-phases. RESULTS The ML system produced a stratification accuracy of 91.28%, demonstrating an improvement of 5.69% when wall-based measurement features were combined with plaque texture-based features. The fused system showed an improvement in mean sensitivity, specificity, positive predictive value, and area under the curve by: 6.39%, 4.59%, 3.31% and 5.48%, respectively when compared to the stand-alone system. While meeting the stability criteria of 5%, the ML system also showed a high average feature retaining power and mean reliability of 89.32% and 98.24%, respectively. CONCLUSIONS The ML system showed an improvement in risk stratification accuracy when the wall-based measurement features were fused with the plaque texture-based features.
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Affiliation(s)
| | | | - Tadashi Araki
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Luca Saba
- Department of Radiology, University of Cagliari, Italy
| | - Petia Radeva
- Department of Mathematics and Computer Science, University of Barcelona, Barcelona, Spain
| | | | - Jasjit S Suri
- Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA.
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16
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Saba L, Jain PK, Suri HS, Ikeda N, Araki T, Singh BK, Nicolaides A, Shafique S, Gupta A, Laird JR, Suri JS. Plaque Tissue Morphology-Based Stroke Risk Stratification Using Carotid Ultrasound: A Polling-Based PCA Learning Paradigm. J Med Syst 2017; 41:98. [PMID: 28501967 DOI: 10.1007/s10916-017-0745-0] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2017] [Accepted: 04/27/2017] [Indexed: 01/18/2023]
Abstract
Severe atherosclerosis disease in carotid arteries causes stenosis which in turn leads to stroke. Machine learning systems have been previously developed for plaque wall risk assessment using morphology-based characterization. The fundamental assumption in such systems is the extraction of the grayscale features of the plaque region. Even though these systems have the ability to perform risk stratification, they lack the ability to achieve higher performance due their inability to select and retain dominant features. This paper introduces a polling-based principal component analysis (PCA) strategy embedded in the machine learning framework to select and retain dominant features, resulting in superior performance. This leads to more stability and reliability. The automated system uses offline image data along with the ground truth labels to generate the parameters, which are then used to transform the online grayscale features to predict the risk of stroke. A set of sixteen grayscale plaque features is computed. Utilizing the cross-validation protocol (K = 10), and the PCA cutoff of 0.995, the machine learning system is able to achieve an accuracy of 98.55 and 98.83%corresponding to the carotidfar wall and near wall plaques, respectively. The corresponding reliability of the system was 94.56 and 95.63%, respectively. The automated system was validated against the manual risk assessment system and the precision of merit for same cross-validation settings and PCA cutoffs are 98.28 and 93.92%for the far and the near wall, respectively.PCA-embedded morphology-based plaque characterization shows a powerful strategy for risk assessment and can be adapted in clinical settings.
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Affiliation(s)
- Luca Saba
- Department of Radiology, University of Cagliari, Cagliari, Italy
| | - Pankaj K Jain
- Point-of-Care Devices, Global Biomedical Technologies, Inc., Roseville, CA, USA
| | - Harman S Suri
- Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
| | - Nobutaka Ikeda
- Cardiovascular Medicine, National Center for Global Health and Medicine, Tokyo, Japan
| | - Tadashi Araki
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Bikesh K Singh
- Department of Biomedical Engineering, NIT Raipur, Raipur, Chhattisgarh, India
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, London, England, UK.,Vascular Diagnostic Centre, University of Cyprus, Nicosia, Cyprus
| | - Shoaib Shafique
- CorVasc Vascular Laboratory, 8433 Harcourt Rd #100, Indianapolis, IN, USA
| | - Ajay Gupta
- Brain and Mind Research Institute and Department of Radiology, Weill Cornell Medical College, New York, NY, USA
| | - John R Laird
- UC Davis Vascular Centre, University of California, Davis, CA, USA
| | - Jasjit S Suri
- Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA. .,Department of Electrical Engineering, University of Idaho (Affl.), Pocatello, ID, USA.
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17
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Shenouda N, Proudfoot NA, Currie KD, Timmons BW, MacDonald MJ. Automated ultrasound edge-tracking software comparable to established semi-automated reference software for carotid intima-media thickness analysis. Clin Physiol Funct Imaging 2017; 38:396-401. [PMID: 28444941 DOI: 10.1111/cpf.12428] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2016] [Accepted: 02/28/2017] [Indexed: 11/26/2022]
Abstract
Many commercial ultrasound systems are now including automated analysis packages for the determination of carotid intima-media thickness (cIMT); however, details regarding their algorithms and methodology are not published. Few studies have compared their accuracy and reliability with previously established automated software, and those that have were in asymptomatic adults. Therefore, this study compared cIMT measures from a fully automated ultrasound edge-tracking software (EchoPAC PC, Version 110.0.2; GE Medical Systems, Horten, Norway) to an established semi-automated reference software (Artery Measurement System (AMS) II, Version 1.141; Gothenburg, Sweden) in 30 healthy preschool children (ages 3-5 years) and 27 adults with coronary artery disease (CAD; ages 48-81 years). For both groups, Bland-Altman plots revealed good agreement with a negligible mean cIMT difference of -0·03 mm. Software differences were statistically, but not clinically, significant for preschool images (P = 0·001) and were not significant for CAD images (P = 0·09). Intra- and interoperator repeatability was high and comparable between software for preschool images (ICC, 0·90-0·96; CV, 1·3-2·5%), but slightly higher with the automated ultrasound than the semi-automated reference software for CAD images (ICC, 0·98-0·99; CV, 1·4-2·0% versus ICC, 0·84-0·89; CV, 5·6-6·8%). These findings suggest that the automated ultrasound software produces valid cIMT values in healthy preschool children and adults with CAD. Automated ultrasound software may be useful for ensuring consistency among multisite research initiatives or large cohort studies involving repeated cIMT measures, particularly in adults with documented CAD.
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Affiliation(s)
- Ninette Shenouda
- Department of Kinesiology, McMaster University, Hamilton, ON, Canada
| | - Nicole A Proudfoot
- Department of Kinesiology, McMaster University, Hamilton, ON, Canada.,Child Health & Exercise Medicine Program, McMaster University, Hamilton, ON, Canada
| | - Katharine D Currie
- Faculty of Kinesiology and Physical Education, University of Toronto, Toronto, ON, Canada
| | - Brian W Timmons
- Department of Kinesiology, McMaster University, Hamilton, ON, Canada.,Child Health & Exercise Medicine Program, McMaster University, Hamilton, ON, Canada
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18
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Ikeda N, Dey N, Sharma A, Gupta A, Bose S, Acharjee S, Shafique S, Cuadrado-Godia E, Araki T, Saba L, Laird JR, Nicolaides A, Suri JS. Automated segmental-IMT measurement in thin/thick plaque with bulb presence in carotid ultrasound from multiple scanners: Stroke risk assessment. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2017; 141:73-81. [PMID: 28241970 DOI: 10.1016/j.cmpb.2017.01.009] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2016] [Revised: 12/20/2016] [Accepted: 01/17/2017] [Indexed: 06/06/2023]
Abstract
BACKGROUND AND OBJECTIVES Standardization of the carotid IMT requires a reference marker in ultrasound scans. It has been shown previously that manual reference marker and manually created carotid segments are used for measuring IMT in these segments. Manual methods are tedious, time consuming, subjective, and prone to errors. Bulb edge can be considered as a reference marker for measurements of the cIMT. However, bulb edge can be difficult to locate in ultrasound scans due to: (a) low signal to noise ratio in the bulb region as compared to common carotid artery region; (b) uncertainty of bulb location in craniocaudal direction; and (c) variability in carotid bulb shape and size. This paper presents an automated system (a class of AtheroEdge™ system from AtheroPoint™, Roseville, CA, USA) for locating the bulb edge as a reference marker and further develop segmental-IMT (sIMT) which measures IMT in 10mm segments (namely: s1, s2 and s3) proximal to the bulb edge. METHODS The patented methodology uses an integrated approach which combines carotid geometry and pixel-classification paradigms. The system first finds the bulb edge and then measures the sIMT proximal to the bulb edge. The system also estimates IMT in bulb region (bIMT). The 649 image database consists of varying plaque (light, moderate to heavy), image resolutions, shapes, sizes and ethnicity. RESULTS Our results show that the IMT contributions in different carotid segments are as follows: bulb-IMT 34%, s1-IMT 29.46%, s2-IMT 11.48%, and s3-IMT 12.75%, respectively. We compare our automated results against reader's tracings demonstrating the following performance: mean lumen-intima error: 0.01235 ± 0.01224mm, mean media-adventitia error: 0.020933 ± 0.01539mm and mean IMT error: 0.01063 ± 0.0031mm. Our system's Precision of Merit is: 98.23%, coefficient of correlation between automated and Reader's IMT is: 0.998 (p-value < 0.0001). These numbers are improved compared to previous publications by Suri's group which is automated multi-resolution conventional cIMT. CONCLUSIONS Our fully automated bulb detection system reports 92.67% precision against ideal bulb edge locations as marked by the reader in the bulb transition zone.
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Affiliation(s)
- Nobutaka Ikeda
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, 2-17-6 Ohashi Meguro-ku, Tokyo, Japan
| | - Nilanjan Dey
- Point of Care Devices, Global Biomedical Technologies, Inc., Roseville, CA, USA
| | - Aditya Sharma
- Cardiovascular Medicine, University of Virginia, VA, USA
| | - Ajay Gupta
- Department of Radiology, Brain and Mind Research Institute, Weill Cornell Medical College, NY, USA
| | - Soumyo Bose
- Point of Care Devices, Global Biomedical Technologies, Inc., Roseville, CA, USA
| | - Suvojit Acharjee
- Point of Care Devices, Global Biomedical Technologies, Inc., Roseville, CA, USA
| | - Shoaib Shafique
- CorVasc Vascular Laboratory, 8433 Harcourt Rd #100, Indianapolis, IN, USA
| | | | - Tadashi Araki
- Division of Cardiovascular Medicine, Centre for Global Health and Medicine (NCGM), 1-21-1 Toyama Shinjuku-ku, Tokyo, Japan
| | - Luca Saba
- Azienda Ospedaliero Universitaria (A.O.U.) di Cagliari - Polo di Monserrato, Università di Cagliari, s.s. 554 Monserrato, Cagliari 09045, Italy
| | - John R Laird
- UC Davis Vascular Center, University of California, Davis, CA, USA
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, London, and Department of Biological Sciences, University of Cyprus, Nicosia, Cyprus
| | - Jasjit S Suri
- Point of Care Devices, Global Biomedical Technologies, Inc., Roseville, CA, USA; Diagnostic and Monitoring Division, AtheroPoint™ LLC, Roseville, CA, USA; Electrical Engineering Department (Aff.), Idaho State University, ID, USA.
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19
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Saba L, Banchhor SK, Suri HS, Londhe ND, Araki T, Ikeda N, Viskovic K, Shafique S, Laird JR, Gupta A, Nicolaides A, Suri JS. Accurate cloud-based smart IMT measurement, its validation and stroke risk stratification in carotid ultrasound: A web-based point-of-care tool for multicenter clinical trial. Comput Biol Med 2016; 75:217-34. [PMID: 27318571 DOI: 10.1016/j.compbiomed.2016.06.010] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2016] [Revised: 06/05/2016] [Accepted: 06/07/2016] [Indexed: 11/29/2022]
Abstract
This study presents AtheroCloud™ - a novel cloud-based smart carotid intima-media thickness (cIMT) measurement tool using B-mode ultrasound for stroke/cardiovascular risk assessment and its stratification. This is an anytime-anywhere clinical tool for routine screening and multi-center clinical trials. In this pilot study, the physician can upload ultrasound scans in one of the following formats (DICOM, JPEG, BMP, PNG, GIF or TIFF) directly into the proprietary cloud of AtheroPoint from the local server of the physician's office. They can then run the intelligent and automated AtheroCloud™ cIMT measurements in point-of-care settings in less than five seconds per image, while saving the vascular reports in the cloud. We statistically benchmark AtheroCloud™ cIMT readings against sonographer (a registered vascular technologist) readings and manual measurements derived from the tracings of the radiologist. One hundred patients (75 M/25 F, mean age: 68±11 years), IRB approved, Toho University, Japan, consisted of Left/Right common carotid artery (CCA) artery (200 ultrasound scans), (Toshiba, Tokyo, Japan) were collected using a 7.5MHz transducer. The measured cIMTs for L/R carotid were as follows (in mm): (i) AtheroCloud™ (0.87±0.20, 0.77±0.20); (ii) sonographer (0.97±0.26, 0.89±0.29) and (iii) manual (0.90±0.20, 0.79±0.20), respectively. The coefficient of correlation (CC) between sonographer and manual for L/R cIMT was 0.74 (P<0.0001) and 0.65 (P<0.0001), while, between AtheroCloud™ and manual was 0.96 (P<0.0001) and 0.97 (P<0.0001), respectively. We observed that 91.15% of the population in AtheroCloud™ had a mean cIMT error less than 0.11mm compared to sonographer's 68.31%. The area under curve for receiving operating characteristics was 0.99 for AtheroCloud™ against 0.81 for sonographer. Our Framingham Risk Score stratified the population into three bins as follows: 39% in low-risk, 70.66% in medium-risk and 10.66% in high-risk bins. Statistical tests were performed to demonstrate consistency, reliability and accuracy of the results. The proposed AtheroCloud™ system is completely reliable, automated, fast (3-5 seconds depending upon the image size having an internet speed of 180Mbps), accurate, and an intelligent, web-based clinical tool for multi-center clinical trials and routine telemedicine clinical care.
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Affiliation(s)
- Luca Saba
- Department of Radiology, University of Cagliari, Italy
| | - Sumit K Banchhor
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India
| | - Harman S Suri
- Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
| | - Narendra D Londhe
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India
| | - Tadashi Araki
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Nobutaka Ikeda
- Cardiovascular Medicine, National Center for Global Health and Medicine, Tokyo, Japan
| | | | - Shoaib Shafique
- CorVasc Vascular Laboratory, 8433 Harcourt Rd #100, Indianapolis, IN, USA
| | - John R Laird
- UC Davis Vascular Centre, University of California, Davis, CA, USA
| | - Ajay Gupta
- Brain and Mind Research Institute, Weill Cornell Medical College, NY, USA
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, London, England; Vascular Diagnostic Centre, University of Cyprus, Nicosia, Cyprus
| | - Jasjit S Suri
- 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.), ID, USA.
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Saba L, Than JCM, Noor NM, Rijal OM, Kassim RM, Yunus A, Ng CR, Suri JS. Inter-observer Variability Analysis of Automatic Lung Delineation in Normal and Disease Patients. J Med Syst 2016; 40:142. [PMID: 27114353 DOI: 10.1007/s10916-016-0504-7] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2016] [Accepted: 04/18/2016] [Indexed: 11/26/2022]
Abstract
Human interaction has become almost mandatory for an automated medical system wishing to be accepted by clinical regulatory agencies such as Food and Drug Administration. Since this interaction causes variability in the gathered data, the inter-observer and intra-observer variability must be analyzed in order to validate the accuracy of the system. This study focuses on the variability from different observers that interact with an automated lung delineation system that relies on human interaction in the form of delineation of the lung borders. The database consists of High Resolution Computed Tomography (HRCT): 15 normal and 81 diseased patients' images taken retrospectively at five levels per patient. Three observers manually delineated the lungs borders independently and using software called ImgTracer™ (AtheroPoint™, Roseville, CA, USA) to delineate the lung boundaries in all five levels of 3-D lung volume. The three observers consisted of Observer-1: lesser experienced novice tracer who is a resident in radiology under the guidance of radiologist, whereas Observer-2 and Observer-3 are lung image scientists trained by lung radiologist and biomedical imaging scientist and experts. The inter-observer variability can be shown by comparing each observer's tracings to the automated delineation and also by comparing each manual tracing of the observers with one another. The normality of the tracings was tested using D'Agostino-Pearson test and all observers tracings showed a normal P-value higher than 0.05. The analysis of variance (ANOVA) test between three observers and automated showed a P-value higher than 0.89 and 0.81 for the right lung (RL) and left lung (LL), respectively. The performance of the automated system was evaluated using Dice Similarity Coefficient (DSC), Jaccard Index (JI) and Hausdorff (HD) Distance measures. Although, Observer-1 has lesser experience compared to Obsever-2 and Obsever-3, the Observer Deterioration Factor (ODF) shows that Observer-1 has less than 10% difference compared to the other two, which is under acceptable range as per our analysis. To compare between observers, this study used regression plots, Bland-Altman plots, two tailed T-test, Mann-Whiney, Chi-Squared tests which showed the following P-values for RL and LL: (i) Observer-1 and Observer-3 were: 0.55, 0.48, 0.29 for RL and 0.55, 0.59, 0.29 for LL; (ii) Observer-1 and Observer-2 were: 0.57, 0.50, 0.29 for RL and 0.54, 0.59, 0.29 for LL; (iii) Observer-2 and Observer-3 were: 0.98, 0.99, 0.29 for RL and 0.99, 0.99, 0.29 for LL. Further, CC and R-squared coefficients were computed between observers which came out to be 0.9 for RL and LL. All three observers however manage to show the feature that diseased lungs are smaller than normal lungs in terms of area.
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Affiliation(s)
- Luca Saba
- Azienda Ospedaliero Universitaria (A.O.U.) di Cagliari - Polo di Monserrato, Università di Cagliari, s.s. 554 Monserrato, Cagliari, 09045, Italy
| | - Joel C M Than
- UTM Razak School of Engineering and Advanced Technology, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
| | - Norliza M Noor
- Department of Engineering, UTM Razak School of Engineering and Advanced Technology, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
| | - Omar M Rijal
- Institute of Mathematical Sciences, Faculty of Science, University of Malaya, Kuala Lumpur, Malaysia
| | - Rosminah M Kassim
- Department of Diagnostic Imaging, Kuala Lumpur Hospital, Kuala Lumpur, Malaysia
| | - Ashari Yunus
- Institute of Respiratory Medicine, Kuala Lumpur, Malaysia
| | - Chue R Ng
- UTM Razak School of Engineering and Advanced Technology, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
| | - Jasjit S Suri
- Global Biomedical Technologies, Inc., Roseville, CA, USA.
- AtheroPoint™ LLC, Roseville, CA, USA.
- Department of Electrical Engineering (Affl.), Idaho State University, Pocatello, ID, USA.
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Araki T, Ikeda N, Shukla D, Londhe ND, Shrivastava VK, Banchhor SK, Saba L, Nicolaides A, Shafique S, Laird JR, Suri JS. A new method for IVUS-based coronary artery disease risk stratification: A link between coronary & carotid ultrasound plaque burdens. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 124:161-179. [PMID: 26707374 DOI: 10.1016/j.cmpb.2015.10.022] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/22/2015] [Revised: 10/12/2015] [Accepted: 10/21/2015] [Indexed: 06/05/2023]
Abstract
Interventional cardiologists have a deep interest in risk stratification prior to stenting and percutaneous coronary intervention (PCI) procedures. Intravascular ultrasound (IVUS) is most commonly adapted for screening, but current tools lack the ability for risk stratification based on grayscale plaque morphology. Our hypothesis is based on the genetic makeup of the atherosclerosis disease, that there is evidence of a link between coronary atherosclerosis disease and carotid plaque built up. This novel idea is explored in this study for coronary risk assessment and its classification of patients between high risk and low risk. This paper presents a strategy for coronary risk assessment by combining the IVUS grayscale plaque morphology and carotid B-mode ultrasound carotid intima-media thickness (cIMT) - a marker of subclinical atherosclerosis. Support vector machine (SVM) learning paradigm is adapted for risk stratification, where both the learning and testing phases use tissue characteristics derived from six feature combinational spaces, which are then used by the SVM classifier with five different kernels sets. These six feature combinational spaces are designed using 56 novel feature sets. K-fold cross validation protocol with 10 trials per fold is used for optimization of best SVM-kernel and best feature combination set. IRB approved coronary IVUS and carotid B-mode ultrasound were jointly collected on 15 patients (2 days apart) via: (a) 40MHz catheter utilizing iMap (Boston Scientific, Marlborough, MA, USA) with 2865 frames per patient (42,975 frames) and (b) linear probe B-mode carotid ultrasound (Toshiba scanner, Japan). Using the above protocol, the system shows the classification accuracy of 94.95% and AUC of 0.95 using optimized feature combination. This is the first system of its kind for risk stratification as a screening tool to prevent excessive cost burden and better patients' cardiovascular disease management, while validating our two hypotheses.
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Affiliation(s)
- Tadashi Araki
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Nobutaka Ikeda
- Cardiovascular Medicine, National Center for Global Health and Medicine, Tokyo, Japan
| | - Devarshi Shukla
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India; Department of Electrical Engineering, University of Idaho (Affl.), ID, USA
| | - Narendra D Londhe
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India; Department of Electrical Engineering, University of Idaho (Affl.), ID, USA
| | | | - Sumit K Banchhor
- Department of Electrical Engineering, NIT Raipur, Chhattisgarh, India; Department of Electrical Engineering, University of Idaho (Affl.), ID, USA
| | - Luca Saba
- Department of Radiology, University of Cagliari, Italy
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, London, England, United Kingdom; Vascular Diagnostic Center, University of Cyprus, Nicosia, Cyprus
| | | | - John R Laird
- UC Davis Vascular Center, University of California, Davis, CA, USA
| | - Jasjit S Suri
- Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA; Department of Electrical Engineering, University of Idaho (Affl.), ID, USA.
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Ring M, Eriksson MJ, Jogestrand T, Caidahl K. Ultrasound measurements of carotid intima-media thickness by two semi-automated analysis systems. Clin Physiol Funct Imaging 2015; 36:389-95. [DOI: 10.1111/cpf.12241] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2014] [Accepted: 01/27/2015] [Indexed: 11/29/2022]
Affiliation(s)
- M. Ring
- Department of Molecular Medicine and Surgery; Karolinska Institutet; Stockholm Sweden
| | - M. J. Eriksson
- Department of Molecular Medicine and Surgery; Karolinska Institutet; Stockholm Sweden
| | - T. Jogestrand
- Department of Laboratory Medicine; Karolinska Institutet; Stockholm Sweden
| | - K. Caidahl
- Department of Molecular Medicine and Surgery; Karolinska Institutet; Stockholm Sweden
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