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Huang KL, Chang TY, Wu YM, Chang YJ, Wu HC, Liu CH, Lee TH, Ho MY. Mediating roles of leukoaraiosis and infarcts in the effects of unilateral carotid artery stenosis on cognition. Front Aging Neurosci 2022; 14:972480. [PMID: 36248002 PMCID: PMC9559387 DOI: 10.3389/fnagi.2022.972480] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Accepted: 08/26/2022] [Indexed: 11/13/2022] Open
Abstract
Background and objectivesLeukoaraiosis and infarcts are common in patients with carotid artery stenosis (CAS), and CAS severity, leukoaraiosis and infarcts all have been implicated in cognitive impairments. CAS severity was not only hypothesized to directly impede specific cognitive domains, but also transmit its effects indirectly to cognitive function through ipsilateral infarcts as well as periventricular leukoaraiosis (PVL) and deep white matter leukoaraiosis (DWML). We aimed to delineate the contributions of leukoaraiosis, infarcts and CAS to different specific cognitive domains.Materials and methodsOne hundred and sixty one participants with unilateral CAS (>50%) on the left (n = 85) or right (n = 76) side and 65 volunteers without significant CAS (<50%) were recruited. The PVL, DWML, and infarct severity were visually rated on MRI. A comprehensive cognitive battery was administered and standardized based on age norms. Correlation and mediation analyses were adopted to examine the direct and indirect influence of CAS, leukoaraiosis, and infarct on specific cognitive domains with adjustment for education, hypertension, diabetes mellitus, and hyperlipidemia.ResultsCarotid artery stenosis severity was associated with ipsilateral leukoaraiosis and infarct. Left CAS had direct effects on most cognitive domains, except for visual memory and constructional ability, and transmitted its indirect effects on all cognitive domains through ipsilateral PVL, and on constructional ability and psychomotor through infarcts. Right CAS only had negative direct effects on visual memory, psychomotor, design fluency and color processing speed, and transmitted its indirect effects on visual memory, word and color processing speed through ipsilateral infarcts. The trends of direct and indirect cognitive effects remained similar after covariate adjustment.ConclusionLeft and right CAS would predominantly lead to verbal and non-verbal cognitive impairment respectively, and such effects could be mediated through CAS-related leukoaraiosis and infarct. Given that cognition is subject to heterogeneous pathologies, the exact relationships between markers of large and small vessel diseases and their composite prognostic effects on cognition requires further investigation.
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Affiliation(s)
- Kuo-Lun Huang
- Department of Neurology, Linkou Chang Gung Memorial Hospital, Taoyuan City, Taiwan
- College of Medicine, Chang Gung University, Taoyuan City, Taiwan
| | - Ting-Yu Chang
- Department of Neurology, Linkou Chang Gung Memorial Hospital, Taoyuan City, Taiwan
- College of Medicine, Chang Gung University, Taoyuan City, Taiwan
| | - Yi-Ming Wu
- College of Medicine, Chang Gung University, Taoyuan City, Taiwan
- Department of Radiology, Linkou Chang Gung Memorial Hospital, Taoyuan City, Taiwan
| | - Yeu-Jhy Chang
- Department of Neurology, Linkou Chang Gung Memorial Hospital, Taoyuan City, Taiwan
- College of Medicine, Chang Gung University, Taoyuan City, Taiwan
| | - Hsiu-Chuan Wu
- Department of Neurology, Linkou Chang Gung Memorial Hospital, Taoyuan City, Taiwan
- College of Medicine, Chang Gung University, Taoyuan City, Taiwan
| | - Chi-Hung Liu
- Department of Neurology, Linkou Chang Gung Memorial Hospital, Taoyuan City, Taiwan
- College of Medicine, Chang Gung University, Taoyuan City, Taiwan
| | - Tsong-Hai Lee
- Department of Neurology, Linkou Chang Gung Memorial Hospital, Taoyuan City, Taiwan
- College of Medicine, Chang Gung University, Taoyuan City, Taiwan
- Tsong-Hai Lee,
| | - Meng-Yang Ho
- Department of Neurology, Linkou Chang Gung Memorial Hospital, Taoyuan City, Taiwan
- College of Medicine, Chang Gung University, Taoyuan City, Taiwan
- Graduate Institute of Behavioral Sciences, Chang Gung University, Taoyuan City, Taiwan
- *Correspondence: Meng-Yang Ho,
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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:jcdd9100326. [PMID: 36286278 PMCID: PMC9604424 DOI: 10.3390/jcdd9100326] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [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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Suri JS, Paul S, Maindarkar MA, Puvvula A, Saxena S, Saba L, Turk M, Laird JR, Khanna NN, Viskovic K, Singh IM, Kalra M, Krishnan PR, Johri A, Paraskevas KI. Cardiovascular/Stroke Risk Stratification in Parkinson's Disease Patients Using Atherosclerosis Pathway and Artificial Intelligence Paradigm: A Systematic Review. Metabolites 2022; 12:metabo12040312. [PMID: 35448500 PMCID: PMC9033076 DOI: 10.3390/metabo12040312] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 03/25/2022] [Accepted: 03/29/2022] [Indexed: 12/20/2022] Open
Abstract
Parkinson’s disease (PD) is a severe, incurable, and costly condition leading to heart failure. The link between PD and cardiovascular disease (CVD) is not available, leading to controversies and poor prognosis. Artificial Intelligence (AI) has already shown promise for CVD/stroke risk stratification. However, due to a lack of sample size, comorbidity, insufficient validation, clinical examination, and a lack of big data configuration, there have been no well-explained bias-free AI investigations to establish the CVD/Stroke risk stratification in the PD framework. The study has two objectives: (i) to establish a solid link between PD and CVD/stroke; and (ii) to use the AI paradigm to examine a well-defined CVD/stroke risk stratification in the PD framework. The PRISMA search strategy selected 223 studies for CVD/stroke risk, of which 54 and 44 studies were related to the link between PD-CVD, and PD-stroke, respectively, 59 studies for joint PD-CVD-Stroke framework, and 66 studies were only for the early PD diagnosis without CVD/stroke link. Sequential biological links were used for establishing the hypothesis. For AI design, PD risk factors as covariates along with CVD/stroke as the gold standard were used for predicting the CVD/stroke risk. The most fundamental cause of CVD/stroke damage due to PD is cardiac autonomic dysfunction due to neurodegeneration that leads to heart failure and its edema, and this validated our hypothesis. Finally, we present the novel AI solutions for CVD/stroke risk prediction in the PD framework. The study also recommends strategies for removing the bias in AI for CVD/stroke risk prediction using the PD framework.
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Affiliation(s)
- Jasjit S. Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA; (A.P.); (I.M.S.)
- Correspondence: ; Tel.: +1-(916)-749-5628
| | - Sudip Paul
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India; (S.P.); (M.A.M.)
| | - Maheshrao A. Maindarkar
- Department of Biomedical Engineering, North Eastern Hill University, Shillong 793022, India; (S.P.); (M.A.M.)
| | - Anudeep Puvvula
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA; (A.P.); (I.M.S.)
- Annu’s Hospitals for Skin & Diabetes, Gudur 524101, India
| | - Sanjay Saxena
- Department of CSE, International Institute of Information Technology, Bhuneshwar 751003, India;
| | - Luca Saba
- Department of Radiology, University of Cagliari, 09121 Cagliari, Italy;
| | - Monika Turk
- Deparment of Neurology, University Medical Centre Maribor, 1262 Maribor, Slovenia;
| | - John R. Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St. Helena, CA 94574, USA;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110001, India;
| | - Klaudija Viskovic
- Department of Radiology and Ultrasound, University Hospital for Infectious Diseases, 10000 Zagreb, Croatia;
| | - Inder M. Singh
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA 95661, USA; (A.P.); (I.M.S.)
| | - Mannudeep Kalra
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA;
| | | | - Amer Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada;
| | - Kosmas I. Paraskevas
- Department of Vascular Surgery, Central Clinic of Athens, 106 80 Athens, Greece;
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Unseen Artificial Intelligence-Deep Learning Paradigm for Segmentation of Low Atherosclerotic Plaque in Carotid Ultrasound: A Multicenter Cardiovascular Study. Diagnostics (Basel) 2021; 11:diagnostics11122257. [PMID: 34943494 PMCID: PMC8699942 DOI: 10.3390/diagnostics11122257] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 11/27/2021] [Accepted: 11/30/2021] [Indexed: 12/12/2022] Open
Abstract
Background: The early detection of carotid wall plaque is recommended in the prevention of cardiovascular disease (CVD) in moderate-risk patients. Previous techniques for B-mode carotid atherosclerotic wall plaque segmentation used artificial intelligence (AI) methods on monoethnic databases, where training and testing are from the “same” ethnic group (“Seen AI”). Therefore, the versatility of the system is questionable. This is the first study of its kind that uses the “Unseen AI” paradigm where training and testing are from “different” ethnic groups. We hypothesized that deep learning (DL) models should perform in 10% proximity between “Unseen AI” and “Seen AI”. Methodology: Two cohorts from multi-ethnic groups (330 Japanese and 300 Hong Kong (HK)) were used for the validation of our hypothesis. We used a four-layered UNet architecture for the segmentation of the atherosclerotic wall with low plaque. “Unseen AI” (training: Japanese, testing: HK or vice versa) and “Seen AI” experiments (single ethnicity or mixed ethnicity) were performed. Evaluation was conducted by measuring the wall plaque area. Statistical tests were conducted for its stability and reliability. Results: When using the UNet DL architecture, the “Unseen AI” pair one (Training: 330 Japanese and Testing: 300 HK), the mean accuracy, dice-similarity, and correlation-coefficient were 98.55, 78.38, and 0.80 (p < 0.0001), respectively, while for “Unseen AI” pair two (Training: 300 HK and Testing: 330 Japanese), these were 98.67, 82.49, and 0.87 (p < 0.0001), respectively. Using “Seen AI”, the same parameters were 99.01, 86.89 and 0.92 (p < 0.0001), respectively. Conclusion: We demonstrated that “Unseen AI” was in close proximity (<10%) to “Seen AI”, validating our DL model for low atherosclerotic wall plaque segmentation. The online system runs < 1 s.
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Balestrieri A, Lucatelli P, Suri HS, Montisci R, Suri JS, Wintermark M, Serra A, Cheng X, Jinliang C, Sanfilippo R, Saba L. Volume of White Matter Hyperintensities, and Cerebral Micro-Bleeds. J Stroke Cerebrovasc Dis 2021; 30:105905. [PMID: 34107418 DOI: 10.1016/j.jstrokecerebrovasdis.2021.105905] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2020] [Revised: 05/11/2021] [Accepted: 05/15/2021] [Indexed: 11/26/2022] Open
Abstract
PURPOSE In the past years the significance of white matter hyperintensities (WMH) has gained raising attention because it is considered a marker of severity of different pathologies. Another condition that in the last years has been assessed in the neuroradiology field is cerebral microbleeds (CMB). The purpose of this work was to evaluate the association between the volume of WMH and the presence and characteristics of CMB. MATERIAL AND METHODS Sixty-five consecutive (males 45; median age 70) subjects were retrospectively analyzed with a 1.5 Tesla scanner. WMH volume was quantified with a semi-automated procedure considering the FLAIR MR sequences whereas the CMB were studied with the SWI technique and CMBs were classified as absent (grade 1), mild (grade 2; total number of CMBs: 1-2), moderate (grade 3; total number of CMBs: 3-10), and severe (grade 4; total number of CMBs: >10). Moreover, overall number of CMBs and the maximum diameter were registered. RESULTS Prevalence of CMBs was 30.76% whereas WMH 81.5%. Mann-Whitney test showed a statistically significant difference in WMH volume between subjects with and without CMBs (p < 0.001). Pearson analysis showed significant correlation between CMB grade, number and maximum diameter and WMH. The better ROC area under the curve (Az) was obtained by the hemisphere volume with a 0.828 (95% CI from 0.752 to 0,888; SD = 0.0427; p value = 0.001). The only parameters that showed a statistically significant association in the logistic regression analysis were Hemisphere volume of WMH (p = 0.001) and Cholesterol LDL (p = 0.0292). CONCLUSION In conclusion, the results of this study suggest the presence of a significant correlation between CMBs and volume of WMH. No differences were found between the different vascular territories.
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Affiliation(s)
- Antonella Balestrieri
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato, s.s. 554 Monserrato, Cagliari 09045, Italy
| | | | - Harman S Suri
- Stroke Diagnosis and Monitoring 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
| | - Roberto Montisci
- Department of Vascular Surgery, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato, s.s. 554 Monserrato Cagliari 09045, Italy
| | - Jasjit S Suri
- Stroke Diagnosis and Monitoring 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
| | | | - Alessandra Serra
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato, s.s. 554 Monserrato, Cagliari 09045, Italy
| | | | - Cheng Jinliang
- Department of Radiology, Beijing Jishuitan Hospital, Beijing, China
| | - Roberto Sanfilippo
- Department of Vascular Surgery, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato, s.s. 554 Monserrato Cagliari 09045, Italy
| | - Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), di Cagliari - Polo di Monserrato, s.s. 554 Monserrato, Cagliari 09045, Italy.
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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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JAMTHIKAR AD, PUVVULA A, GUPTA D, JOHRI AM, NAMBI V, KHANNA NN, SABA L, MAVROGENI S, LAIRD JR, PAREEK G, MINER M, SFIKAKIS PP, PROTOGEROU A, KITAS GD, NICOLAIDES A, SHARMA AM, VISWANATHAN V, RATHORE VS, KOLLURI R, BHATT DL, SURI JS. Cardiovascular disease and stroke risk assessment in patients with chronic kidney disease using integration of estimated glomerular filtration rate, ultrasonic image phenotypes, and artificial intelligence: a narrative review. INT ANGIOL 2021; 40:150-164. [DOI: 10.23736/s0392-9590.20.04538-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Low-Cost Office-Based Cardiovascular Risk Stratification Using Machine Learning and Focused Carotid Ultrasound in an Asian-Indian Cohort. J Med Syst 2020; 44:208. [DOI: 10.1007/s10916-020-01675-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Accepted: 11/09/2020] [Indexed: 12/13/2022]
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Ni L, Zhou F, Qing Z, Zhang X, Li M, Zhu B, Zhang B, Xu Y. The Asymmetry of White Matter Hyperintensity Burden Between Hemispheres Is Associated With Intracranial Atherosclerotic Plaque Enhancement Grade. Front Aging Neurosci 2020; 12:163. [PMID: 32655391 PMCID: PMC7324557 DOI: 10.3389/fnagi.2020.00163] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 05/12/2020] [Indexed: 11/29/2022] Open
Abstract
Purpose The contribution of intracranial atherosclerotic stenosis (ICAS) to the development of white matter hyperintensities (WMHs) has not been fully elucidated. We aimed to retrospectively assess the relationship between WMH burden and unilateral ICAS by combined examination of lumen stenosis, plaque enhancement, and cerebral perfusion. Materials and methods A cross-sectional study of 41 patients with symptomatic unilateral ICAS (mean age 57 ± 10 years; 26 males) was conducted. Detailed clinical data, including vascular risk factors, were obtained. WMH volume was derived from 3D-fluid-attenuated inversion recovery (3D-FLAIR) and was assessed by using a validated semi-automated protocol. Lumen stenosis, plaque enhancement, and cerebral perfusion (assessed on time-to-peak parameter using the Alberta Stroke Program Early CT score (TTP-ASPECTS) scale) were evaluated. The WMH volumes of peri-ventricular (PWMH) and deep (DWMH) white matter were calculated separately and compared between hemispheres. Associations between WMH volume (inter-hemispheric volume difference, ipsilateral and contralateral to the ICAS site separately), unilateral ICAS imaging metrics, and vascular risk factors were assessed by using linear regression. Results The DWMH volume ipsilateral to the ICAS site (ipsilateral DWMH volume) was significantly greater than that of the contralateral site (P < 0.001), while the PWMH volume difference between hemispheres did not reach statistical significance. The inter-hemispheric DWMH volume difference was significantly associated with a higher plaque enhancement grade (β = 0.436, P = 0.005) and inversely associated with cerebral hypoperfusion (lower TTP-ASPECTS) (β = −0.613, P < 0.001). In the between-subject multivariable regression analysis, while older age (β = 0.323, P = 0.025), hypoperfusion (β = −0.394, P = 0.007), and hypertension (β = 0.378, P = 0.011) were independently associated with ipsilateral DWMH volume, plaque enhancement did not show an association with ipsilateral DWMH volume. The association between ipsilateral DWMH volume and lumen stenosis approached statistical significance (β = 0.274, P = 0.084). Conclusion The DWMH was attributed to chronic hypoperfusion secondary to atherosclerotic stenosis. The association between the asymmetry of deep white matter lesions and plaque enhancement might suggest that increased deep white matter lesions are those ischemic lesions, which are more prone to the development of stroke.
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Affiliation(s)
- Ling Ni
- Department of Neurology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China.,Department of Radiology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Fei Zhou
- Department of Radiology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Zhao Qing
- Department of Radiology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China.,Institute of Brain Science, Nanjing University, Nanjing, China
| | - Xin Zhang
- Department of Radiology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Ming Li
- Department of Radiology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Bin Zhu
- Department of Radiology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
| | - Bing Zhang
- Department of Radiology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China.,Institute of Brain Science, Nanjing University, Nanjing, China
| | - Yun Xu
- Department of Neurology, Affiliated Drum Tower Hospital, Medical School of Nanjing University, Nanjing, China
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Biswas M, Saba L, Chakrabartty S, Khanna NN, Song H, Suri HS, Sfikakis PP, Mavrogeni S, Viskovic K, Laird JR, Cuadrado-Godia E, Nicolaides A, Sharma A, Viswanathan V, Protogerou A, Kitas G, Pareek G, Miner M, Suri JS. Two-stage artificial intelligence model for jointly measurement of atherosclerotic wall thickness and plaque burden in carotid ultrasound: A screening tool for cardiovascular/stroke risk assessment. Comput Biol Med 2020; 123:103847. [PMID: 32768040 DOI: 10.1016/j.compbiomed.2020.103847] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 06/04/2020] [Accepted: 06/04/2020] [Indexed: 12/14/2022]
Abstract
MOTIVATION The early screening of cardiovascular diseases (CVD) can lead to effective treatment. Thus, accurate and reliable atherosclerotic carotid wall detection and plaque measurements are crucial. Current measurement methods are time-consuming and do not utilize the power of knowledge-based paradigms such as artificial intelligence (AI). We present an AI-based methodology for the joint automated detection and measurement of wall thickness and carotid plaque (CP) in the form of carotid intima-media thickness (cIMT) and total plaque area (TPA), a class of AtheroEdge™ system (AtheroPoint™, CA, USA). METHOD The novel system consists of two stages, and each stage comprises an independent deep learning (DL) model. In Stage I, the first DL model segregates the common carotid artery (CCA) patches from ultrasound (US) images into the rectangular wall and non-wall patches. The characterized wall patches are integrated to form the region of interest (ROI), which is then fed into Stage II. In Stage II, the second DL model segments the far wall region. Lumen-intima (LI) and media-adventitial (MA) boundaries are then extracted from the wall region, which is then used for cIMT and PA measurement. RESULTS Using the database of 250 carotid scans, the cIMT error using the AI model is 0.0935±0.0637 mm, which is lower than those of all previous methods. The PA error is found to be 2.7939±2.3702 mm2. The system's correlation coefficient (CC) between AI and ground truth (GT) values for cIMT is 0.99 (p < 0.0001), which is higher compared with the CC of 0.96 (p < 0.0001) shown by the earlier DL method. The CC for PA between AI and GT values is 0.89 (p < 0.0001). CONCLUSION A novel AI-based strategy was applied to carotid US images for the joint detection of carotid wall thickness (cWT) and plaque area (PA), followed by cIMT and PA measurement. This AI-based strategy shows improved performance using the patch technique compared with previous methods using full carotid scans.
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Affiliation(s)
| | - Luca Saba
- Department of Radiology, A.O.U., Italy
| | | | - Narender N Khanna
- Cardiology Department, Indraprastha Apollo Hospitals, New Delhi, India
| | | | | | | | | | - Klaudija Viskovic
- Radiology and Ultrasound, University Hospital for Infectious Diseases, Zagreb, Croatia
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA
| | | | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, London, UK; Department of Biological Sciences, University of Cyprus, Nicosia, Cyprus
| | - Aditya Sharma
- Division of Cardiovascular Medicine, University of Virginia, VA, USA
| | - Vijay Viswanathan
- MV Hospital for Diabetes and Professor M Viswanathan Diabetes Research Centre, Chennai, India
| | | | - George Kitas
- Department of Rheumatology, University of Manchester, Dudley, UK
| | - Gyan Pareek
- Minimally Invasive Urology Institute, Brown University, Providence, RI, USA
| | - Martin Miner
- Men's Health Center, Miriam Hospital Providence, Rhode Island, USA
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA.
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11
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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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12
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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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13
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Effect of carotid image-based phenotypes on cardiovascular risk calculator: AECRS1.0. Med Biol Eng Comput 2019; 57:1553-1566. [PMID: 30989577 DOI: 10.1007/s11517-019-01975-2] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2018] [Accepted: 03/21/2019] [Indexed: 12/11/2022]
Abstract
Today, the 10-year cardiovascular risk largely relies on conventional cardiovascular risk factors (CCVRFs) and suffers from the effect of atherosclerotic wall changes. In this study, we present a novel risk calculator AtheroEdge Composite Risk Score (AECRS1.0), designed by fusing CCVRF with ultrasound image-based phenotypes. Ten-year risk was computed using the Framingham Risk Score (FRS), United Kingdom Prospective Diabetes Study 56 (UKPDS56), UKPDS60, Reynolds Risk Score (RRS), and pooled composite risk (PCR) score. AECRS1.0 was computed by measuring the 10-year five carotid phenotypes such as IMT (ave., max., min.), IMT variability, and total plaque area (TPA) by fusing eight CCVRFs and then compositing them. AECRS1.0 was then benchmarked against the five conventional cardiovascular risk calculators by computing the receiver operating characteristics (ROC) and area under curve (AUC) values with a 95% CI. Two hundred four IRB-approved Japanese patients' left/right common carotid arteries (407 ultrasound scans) were collected with a mean age of 69 ± 11 years. The calculators gave the following AUC: FRS, 0.615; UKPDS56, 0.576; UKPDS60, 0.580; RRS, 0.590; PCRS, 0.613; and AECRS1.0, 0.990. When fusing CCVRF, TPA reported the highest AUC of 0.81. The patients were risk-stratified into low, moderate, and high risk using the standardized thresholds. The AECRS1.0 demonstrated the best performance on a Japanese diabetes cohort when compared with five conventional calculators. Graphical abstract AECRS1.0: Carotid ultrasound image phenotype-based 10-year cardiovascular risk calculator. The figure provides brief overview of the proposed carotid image phenotype-based 10-year cardiovascular risk calculator called AECRS1.0. AECRS1.0 was also benchmarked against five conventional cardiovascular risk calculators (Framingham Risk Score (FRS), United Kingdom Prospective Diabetes Study 56 (UKPDS56), UKPDS60, Reynolds Risk Score (RRS), and pooled composite risk (PCR) score).
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14
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Cuadrado-Godia E, Jamthikar AD, Gupta D, Khanna NN, Araki T, Maniruzzaman M, Saba L, Nicolaides A, Sharma A, Omerzu T, Suri HS, Gupta A, Mavrogeni S, Turk M, Laird JR, Protogerou A, Sfikakis P, Kitas GD, Viswanathan V, Suri JS. Ranking of stroke and cardiovascular risk factors for an optimal risk calculator design: Logistic regression approach. Comput Biol Med 2019; 108:182-195. [PMID: 31005010 DOI: 10.1016/j.compbiomed.2019.03.020] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2019] [Revised: 03/21/2019] [Accepted: 03/21/2019] [Indexed: 12/11/2022]
Abstract
PURPOSE Conventional cardiovascular risk factors (CCVRFs) and carotid ultrasound image-based phenotypes (CUSIP) are independently associated with long-term risk of cardiovascular (CV) disease. In this study, 26 cardiovascular risk (CVR) factors which consisted of a combination of CCVRFs and CUSIP together were ranked. Further, an optimal risk calculator using AtheroEdge composite risk score (AECRS1.0) was designed and benchmarked against seven conventional CV risk (CVR) calculators. METHODS Two types of ranking were performed: (i) ranking of 26 CVR factors and (ii) ranking of eight types of 10-year risk calculators. In the first case, multivariate logistic regression was used to compute the odds ratio (OR) and in the second, receiver operating characteristic curves were used to evaluate the performance of eight types of CVR calculators using SPSS23.0 and MEDCALC12.0 with validation against STATA15.0. RESULTS The left and right common carotid arteries (CCA) of 202 Japanese patients were examined to obtain 404 ultrasound scans. CUSIP ranked in the top 50% of the 26 covariates. Intima-media thickness variability (IMTV) and IMTV10yr were the most influential carotid phenotypes for left CCA (OR = 250, P < 0.0001 and OR = 207, P < 0.0001 respectively) and right CCA (OR = 1614, P < 0.0001 and OR = 626, P < 0.0001 respectively). However, for the mean CCA, AECRS1.0 and AECRS1.010yr reported the most highly significant OR among all the CVR factors (OR = 1.073, P < 0.0001 and OR = 1.104, P < 0.0001). AECRS1.010yr also reported highest area-under-the-curve (AUC = 0.904, P < 0.0001) compared to seven types of conventional calculators. Age and glycated haemoglobin reported highest OR (1.96, P < 0.0001 and 1.05, P = 0.012) among all other CCVRFs. CONCLUSION AECRS1.010yr demonstrated the best performance due to presence of CUSIP and ranked at the first place with highest AUC.
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Affiliation(s)
| | | | - Deep Gupta
- Department of ECE, VNIT, Nagpur, Maharashtra, India
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
| | - Tadashi Araki
- Division of Cardiovascular Medicine, Toho University, Tokyo, Japan
| | - Md Maniruzzaman
- Statistics Discipline, Khulna University, Khulna, Bangladesh
| | - Luca Saba
- Department of Radiology, University of Cagliari, Italy
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, University of Cyprus, Nicosia, Cyprus
| | - Aditya Sharma
- Cardiovascular Medicine, University of Virginia, VA, USA
| | - Tomaz Omerzu
- Department of Neurology, University Medical Centre Maribor, Slovenia
| | | | - Ajay Gupta
- Department of Radiology, Cornell Medical Center, NY, USA
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece
| | - Monika Turk
- Department of Neurology, University Medical Centre Maribor, Maribor, Slovenia
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA
| | - Athanasios Protogerou
- Department of Cardiovascular Prevention & Research Unit Clinic & Laboratory of Pathophysiology, National and Kapodistrian Univ. of Athens, Greece
| | - Petros Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, Greece
| | - George D Kitas
- Research & Development-Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, United Kingdom
| | - Vijay Viswanathan
- MV Hospital for Diabetes and Professor M Viswanathan Diabetes Research Centre, Chennai, India
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA.
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15
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Khanna NN, Jamthikar AD, Araki T, Gupta D, Piga M, Saba L, Carcassi C, Nicolaides A, Laird JR, Suri HS, Gupta A, Mavrogeni S, Kitas GD, Suri JS. Nonlinear model for the carotid artery disease 10-year risk prediction by fusing conventional cardiovascular factors to carotid ultrasound image phenotypes: A Japanese diabetes cohort study. Echocardiography 2019; 36:345-361. [PMID: 30623485 DOI: 10.1111/echo.14242] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2018] [Accepted: 12/04/2018] [Indexed: 12/11/2022] Open
Abstract
MOTIVATION This study presents a novel nonlinear model which can predict 10-year carotid ultrasound image-based phenotypes by fusing nine traditional cardiovascular risk factors (ethnicity, gender, age, artery type, body mass index, hemoglobin A1c, hypertension, low-density lipoprotein, and smoking) with five types of carotid automated image phenotypes (three types of carotid intima-media thickness (IMT), wall variability, and total plaque area). METHODOLOGY Two-step process was adapted: First, five baseline carotid image-based phenotypes were automatically measured using AtheroEdge™ (AtheroPoint™ , CA, USA) system by two operators (novice and experienced) and an expert. Second, based on the annual progression rates of cIMT due to nine traditional cardiovascular risk factors, a novel nonlinear model was adapted for 10-year predictions of carotid phenotypes. RESULTS Institute review board (IRB) approved 204 Japanese patients' left/right common carotid artery (407 ultrasound scans) was collected with a mean age of 69 ± 11 years. Age and hemoglobin were reported to have a high influence on the 10-year carotid phenotypes. Mean correlation coefficient (CC) between 10-year carotid image-based phenotype and age was improved by 39.35% in males and 25.38% in females. The area under the curves for the 10-year measurements of five phenotypes IMTave10yr , IMTmax10yr , IMTmin10yr , IMTV10yr , and TPA10yr were 0.96, 0.94, 0.90, 1.0, and 1.0. Inter-operator variability between two operators showed significant CC (P < 0.0001). CONCLUSIONS A nonlinear model was developed and validated by fusing nine conventional CV risk factors with current carotid image-based phenotypes for predicting the 10-year carotid ultrasound image-based phenotypes which may be used risk assessment.
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Affiliation(s)
- Narendra N Khanna
- Department of Cardiology, Indraprastha Apollo Hospitals, New Delhi, India
| | - Ankush D Jamthikar
- Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur, India
| | - Tadashi Araki
- Division of Cardiovascular Medicine, Toho University Ohashi Medical Center, Tokyo, Japan
| | - Deep Gupta
- Department of Electronics and Communication Engineering, Visvesvaraya National Institute of Technology, Nagpur, India
| | - Matteo Piga
- Department of Rheumatology, University Clinic and AOU of Cagliari, Cagliari, Italy
| | - Luca Saba
- Department of Radiology, University of Cagliari, Cagliari, Italy
| | - Carlo Carcassi
- Department of Genetics, University of Cagliari, Cagliari, Italy
| | - Andrew Nicolaides
- Department of Vascular Surgery, Imperial College, London, UK.,Vascular Diagnostic Center, University of Cyprus, Nicosia, Cyprus
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, California
| | | | - Ajay Gupta
- Department of Radiology and Feil Family Brain and Mind Research Institute, Weill Cornell Medical Center, New York, New York
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece
| | - George D Kitas
- Arthritis Research UK Centre for Epidemiology, Manchester University, Manchester, UK.,Director of Research & Development-Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPointTM, Roseville, California
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16
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Khanna NN, Jamthikar AD, Gupta D, Nicolaides A, Araki T, Saba L, Cuadrado-Godia E, Sharma A, Omerzu T, Suri HS, Gupta A, Mavrogeni S, Turk M, Laird JR, Protogerou A, Sfikakis PP, Kitas GD, Viswanathan V, Suri JS. Performance evaluation of 10-year ultrasound image-based stroke/cardiovascular (CV) risk calculator by comparing against ten conventional CV risk calculators: A diabetic study. Comput Biol Med 2019; 105:125-143. [PMID: 30641308 DOI: 10.1016/j.compbiomed.2019.01.002] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2018] [Revised: 01/04/2019] [Accepted: 01/05/2019] [Indexed: 12/11/2022]
Abstract
MOTIVATION AtheroEdge Composite Risk Score (AECRS1.010yr) is an integrated stroke/cardiovascular risk calculator that was recently developed and computes the 10-year risk of carotid image phenotypes by integrating conventional cardiovascular risk factors (CCVRFs). It is therefore important to understand how closely AECRS1.010yr is associated with the ten other currently available conventional cardiovascular risk calculators (CCVRCs). METHODS The Institutional Review Board of Toho University approved the examination of the left/right common carotid arteries of 202 Japanese patients. Step 1 consists of measurement of AECRS1.010yr, given current image phenotypes and CCVRFs. Step 2 consists of computing the risk score using ten different CCVRCs given CCVR factors: QRISK3, Framingham Risk Score (FRS), United Kingdom Prospective Diabetes Study (UKPDS) 56, UKPDS60, Reynolds Risk Score (RRS), Pooled cohort Risk Score (PCRS or ASCVD), Systematic Coronary Risk Evaluation (SCORE), Prospective Cardiovascular Munster Study (PROCAM) calculator, NIPPON, and World Health Organization (WHO) risk. Step 3 consists of computing the closeness factor between AECRS1.010yr and ten CCVRCs using cumulative ranking index derived using eight different statistically derived metrics. RESULTS AECRS1.010yr reported the highest area-under-the-curve (0.927;P < 0.001) among all the risk calculators. The top three CCVRCs closest to AECRS1.010yr were QRISK3, FRS, and UKPDS60 with cumulative ranking scores of 2.1, 3.0, and 3.8, respectively. CONCLUSION AECRS1.010yr produced the largest AUC due to the integration of image-based phenotypes with CCVR factors, and ranked at first place with the highest AUC. Cumulative ranking of ten CCVRCs demonstrated that QRISK3 was the closest calculator to AECRS1.010yr, which is also consistent with the industry trend.
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Affiliation(s)
- Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi, India
| | - Ankush D Jamthikar
- Department of Electronics and Communication Engineering, VNIT, Nagpur, India
| | - Deep Gupta
- Department of Electronics and Communication Engineering, VNIT, Nagpur, India
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, University of Cyprus, Nicosia, Cyprus
| | - Tadashi Araki
- Division of Cardiovascular Medicine, Toho University, Tokyo, Japan
| | - Luca Saba
- Department of Radiology, University of Cagliari, Italy
| | | | - Aditya Sharma
- Cardiovascular Medicine, University of Virginia, VA, USA
| | - Tomaz Omerzu
- Department of Neurology, University Medical Centre Maribor, Slovenia
| | | | - Ajay Gupta
- Department of Radiology, Weill Cornell Medicine, NY, USA
| | - Sophie Mavrogeni
- Cardiology Clinic, Onassis Cardiac Surgery Center, Athens, Greece
| | - Monika Turk
- Neurology Dept., University Medical Centre Maribor, Maribor, Slovenia
| | - John R Laird
- Heart and Vascular Institute, Adventist Health St. Helena, St Helena, CA, USA
| | - Athanasios Protogerou
- Department of Cardiovascular Prevention & Research Unit Clinic & Laboratory of Pathophysiology, National and Kapodistrian Univ. of Athens, Greece
| | - Petros P Sfikakis
- Joint Rheumatology Program, National Kapodistrian University of Athens Medical School, Greece
| | - George D Kitas
- Dudley Group NHS Foundation Trust, Dudley, United Kingdom
| | - Vijay Viswanathan
- MV Hospital for Diabetes and Professor M Viswanathan Diabetes Research Centre, Chennai, India
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA.
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17
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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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Saba L, Sanfilippo R, Balestrieri A, Zaccagna F, Argiolas GM, Suri JS, Montisci R. Relationship between Carotid Computed Tomography Dual-Energy and Brain Leukoaraiosis. J Stroke Cerebrovasc Dis 2017; 26:1824-1830. [PMID: 28527587 DOI: 10.1016/j.jstrokecerebrovasdis.2017.04.016] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2016] [Revised: 03/27/2017] [Accepted: 04/14/2017] [Indexed: 01/23/2023] Open
Abstract
BACKGROUND The purpose of this study was to assess if there is a correlation between the carotid computed tomography (CT) Hounsfield unit (HU)-based plaque attenuation values measured using dual-energy CT (DECT) scanner and brain leukoaraiosis (LA). METHODS Fifty consecutive patients (34 males, 16 females; mean age, 69 years; age range, 46-84 years) who underwent carotid CT and brain magnetic resonance imaging were included in the study. CT examinations were performed with a DECT scanner, and LA lesion volume quantification was performed using a semiautomated segmentation technique. RESULTS We found an inverse statistically significant correlation between the HU-based carotid artery plaque attenuation and the LA lesion volume. Because of the presence of calcified plaques, a second model was calculated at low kiloelectron volt levels from 66 to 100 and 100 kV by taking into consideration the fatty and mixed plaques, and this further led to the associations between HU-based attenuation and LA volume in brain and vascular territories. CONCLUSIONS The results of our study suggest that the associations between HU attenuation of the carotid artery plaques (with the exclusion of calcified plaques) and the volume of LA are emphasized at low keV energy levels.
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Affiliation(s)
- Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari 09045, Italy.
| | - Roberto Sanfilippo
- Department of Vascular Surgery, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari 09045, Italy
| | - Antonella Balestrieri
- Department of Radiology, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari 09045, Italy
| | - Fulvio Zaccagna
- Department of Radiology, University of Cambridge School of Clinical Medicine, Cambridge, CB2 0QQ, UK
| | | | - Jasjit S Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint LLC, Roseville, CA, USA
| | - Roberto Montisci
- Department of Vascular Surgery, Azienda Ospedaliero Universitaria (A.O.U.), Cagliari 09045, Italy
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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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