1
|
Xiao H, Fang W, Lin M, Zhou Z, Fei H, Chen C. [A multiscale carotid plaque detection method based on two-stage analysis]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2024; 44:387-396. [PMID: 38501425 PMCID: PMC10954526 DOI: 10.12122/j.issn.1673-4254.2024.02.22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 03/20/2024]
Abstract
OBJECTIVE To develop a method for accurate identification of multiscale carotid plaques in ultrasound images. METHODS We proposed a two-stage carotid plaque detection method based on deep convolutional neural network (SM-YOLO).A series of algorithms such as median filtering, histogram equalization, and Gamma transformation were used to preprocess the dataset to improve image quality. In the first stage of the model construction, a candidate plaque set was built based on the YOLOX_l target detection network, using multiscale image training and multiscale image prediction strategies to accommodate carotid artery plaques of different shapes and sizes. In the second stage, the Histogram of Oriented Gradient (HOG) features and Local Binary Pattern (LBP) features were extracted and fused, and a Support Vector Machine (SVM) classifier was used to screen the candidate plaque set to obtain the final detection results. This model was compared quantitatively and visually with several target detection models (YOLOX_l, SSD, EfficientDet, YOLOV5_l, Faster R-CNN). RESULTS SM-YOLO achieved a recall of 89.44%, an accuracy of 90.96%, a F1-Score of 90.19%, and an AP of 92.70% on the test set, outperforming other models in all performance indicators and visual effects. The constructed model had a much shorter detection time than the Faster R-CNN model (only one third of that of the latter), thus meeting the requirements of real-time detection. CONCLUSION The proposed carotid artery plaque detection method has good performance for accurate identification of carotid plaques in ultrasound images.
Collapse
Affiliation(s)
- H Xiao
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - W Fang
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - M Lin
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| | - Z Zhou
- Guangzhou Shangyi Network Information Technology Co., Ltd., Guangzhou 510515, China
| | - H Fei
- Guangdong Provincial People's Hospital Affiliated to Southern Medical University, Guangzhou 510180, China
| | - C Chen
- School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China
| |
Collapse
|
2
|
Kopyto E, Czeczelewski M, Mikos E, Stępniak K, Kopyto M, Matuszek M, Nieoczym K, Czarnecki A, Kuczyńska M, Cheda M, Drelich-Zbroja A, Jargiełło T. Contrast-Enhanced Ultrasound Feasibility in Assessing Carotid Plaque Vulnerability-Narrative Review. J Clin Med 2023; 12:6416. [PMID: 37835061 PMCID: PMC10573420 DOI: 10.3390/jcm12196416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 09/25/2023] [Accepted: 09/28/2023] [Indexed: 10/15/2023] Open
Abstract
The risk assessment for carotid atherosclerotic lesions involves not only determining the degree of stenosis but also plaque morphology and its composition. Recently, carotid contrast-enhanced ultrasound (CEUS) has gained importance for evaluating vulnerable plaques. This review explores CEUS's utility in detecting carotid plaque surface irregularities and ulcerations as well as intraplaque neovascularization and its alignment with histology. Initial indications suggest that CEUS might have the potential to anticipate cerebrovascular incidents. Nevertheless, there is a need for extensive, multicenter prospective studies that explore the relationships between CEUS observations and patient clinical outcomes in cases of carotid atherosclerotic disease.
Collapse
Affiliation(s)
- Ewa Kopyto
- Students’ Scientific Society, Department of Interventional Radiology and Neuroradiology, Medical University of Lublin, 20-594 Lublin, Poland; (E.K.); (E.M.); (K.S.); (M.K.); (M.M.); (K.N.); (A.C.)
| | - Marcin Czeczelewski
- Students’ Scientific Society, Department of Interventional Radiology and Neuroradiology, Medical University of Lublin, 20-594 Lublin, Poland; (E.K.); (E.M.); (K.S.); (M.K.); (M.M.); (K.N.); (A.C.)
| | - Eryk Mikos
- Students’ Scientific Society, Department of Interventional Radiology and Neuroradiology, Medical University of Lublin, 20-594 Lublin, Poland; (E.K.); (E.M.); (K.S.); (M.K.); (M.M.); (K.N.); (A.C.)
| | - Karol Stępniak
- Students’ Scientific Society, Department of Interventional Radiology and Neuroradiology, Medical University of Lublin, 20-594 Lublin, Poland; (E.K.); (E.M.); (K.S.); (M.K.); (M.M.); (K.N.); (A.C.)
| | - Maja Kopyto
- Students’ Scientific Society, Department of Interventional Radiology and Neuroradiology, Medical University of Lublin, 20-594 Lublin, Poland; (E.K.); (E.M.); (K.S.); (M.K.); (M.M.); (K.N.); (A.C.)
| | - Małgorzata Matuszek
- Students’ Scientific Society, Department of Interventional Radiology and Neuroradiology, Medical University of Lublin, 20-594 Lublin, Poland; (E.K.); (E.M.); (K.S.); (M.K.); (M.M.); (K.N.); (A.C.)
| | - Karolina Nieoczym
- Students’ Scientific Society, Department of Interventional Radiology and Neuroradiology, Medical University of Lublin, 20-594 Lublin, Poland; (E.K.); (E.M.); (K.S.); (M.K.); (M.M.); (K.N.); (A.C.)
| | - Adam Czarnecki
- Students’ Scientific Society, Department of Interventional Radiology and Neuroradiology, Medical University of Lublin, 20-594 Lublin, Poland; (E.K.); (E.M.); (K.S.); (M.K.); (M.M.); (K.N.); (A.C.)
| | - Maryla Kuczyńska
- Department of Interventional Radiology and Neuroradiology, Medical University of Lublin, 20-594 Lublin, Poland; (M.K.); (M.C.); (A.D.-Z.); (T.J.)
| | - Mateusz Cheda
- Department of Interventional Radiology and Neuroradiology, Medical University of Lublin, 20-594 Lublin, Poland; (M.K.); (M.C.); (A.D.-Z.); (T.J.)
| | - Anna Drelich-Zbroja
- Department of Interventional Radiology and Neuroradiology, Medical University of Lublin, 20-594 Lublin, Poland; (M.K.); (M.C.); (A.D.-Z.); (T.J.)
| | - Tomasz Jargiełło
- Department of Interventional Radiology and Neuroradiology, Medical University of Lublin, 20-594 Lublin, Poland; (M.K.); (M.C.); (A.D.-Z.); (T.J.)
| |
Collapse
|
3
|
Quiroga A, Novi S, Martins G, Bortoletto LF, Avelar W, Guillaumon AT, Li LM, Cendes F, Mesquita RC. Quantification of the Tissue Oxygenation Delay Induced by Breath-Holding in Patients with Carotid Atherosclerosis. Metabolites 2022; 12:metabo12111156. [PMID: 36422296 PMCID: PMC9697605 DOI: 10.3390/metabo12111156] [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: 10/17/2022] [Revised: 11/16/2022] [Accepted: 11/19/2022] [Indexed: 11/24/2022] Open
Abstract
Carotid artery stenosis (CAS) is a common vascular disease with long-term consequences for the brain. Although CAS is strongly associated with impaired cerebral hemodynamics and neurodegeneration, the mechanisms underlying hemodynamic impairment in the microvasculature remain unknown. In this work, we employed functional near-infrared spectroscopy (fNIRS) to introduce a methodological approach for quantifying the temporal delay of the evoked hemodynamic response. The method was validated during a vasodilatory task (breath-holding) in 50 CAS patients and 20 controls. Our results suggest that the hemodynamic response to breath-holding can be delayed by up to 6 s in the most severe patients, a significant increase from the median 4 s measured for the control group (p = 0.01). In addition, the fraction of brain regions that responded to the task decreased as the CAS severity increased, from a median of 90% in controls to 73% in the most severe CAS group (p = 0.04). The presence of collateral circulation increases the response to breath-holding and decreases the average time delays across the brain, although the number of communicating arteries alone cannot predict these fNIRS-based hemodynamic variables (p > 0.09). Overall, this work proposes a method to quantitatively assess impaired cerebral hemodynamics in CAS patients.
Collapse
Affiliation(s)
- Andrés Quiroga
- “Gleb Wataghin” Institute of Physics, University of Campinas, Campinas 13083-859, SP, Brazil
- Correspondence: (A.Q.); (R.C.M.)
| | - Sergio Novi
- “Gleb Wataghin” Institute of Physics, University of Campinas, Campinas 13083-859, SP, Brazil
| | - Giovani Martins
- “Gleb Wataghin” Institute of Physics, University of Campinas, Campinas 13083-859, SP, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas 13083-970, SP, Brazil
| | - Luis Felipe Bortoletto
- “Gleb Wataghin” Institute of Physics, University of Campinas, Campinas 13083-859, SP, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas 13083-970, SP, Brazil
| | - Wagner Avelar
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas 13083-970, SP, Brazil
- Clinical Hospital, University of Campinas, Campinas 13083-888, SP, Brazil
- Faculty of Medical Sciences, University of Campinas, Campinas 13083-894, SP, Brazil
| | - Ana Terezinha Guillaumon
- Clinical Hospital, University of Campinas, Campinas 13083-888, SP, Brazil
- Faculty of Medical Sciences, University of Campinas, Campinas 13083-894, SP, Brazil
| | - Li Min Li
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas 13083-970, SP, Brazil
- Clinical Hospital, University of Campinas, Campinas 13083-888, SP, Brazil
- Faculty of Medical Sciences, University of Campinas, Campinas 13083-894, SP, Brazil
| | - Fernando Cendes
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas 13083-970, SP, Brazil
- Clinical Hospital, University of Campinas, Campinas 13083-888, SP, Brazil
- Faculty of Medical Sciences, University of Campinas, Campinas 13083-894, SP, Brazil
| | - Rickson Coelho Mesquita
- “Gleb Wataghin” Institute of Physics, University of Campinas, Campinas 13083-859, SP, Brazil
- Brazilian Institute of Neuroscience and Neurotechnology, Campinas 13083-970, SP, Brazil
- Correspondence: (A.Q.); (R.C.M.)
| |
Collapse
|
4
|
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.
Collapse
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.
| |
Collapse
|
5
|
Pierro A, Modugno P, Iezzi R, Cilla S. Challenges and Pitfalls in CT-Angiography Evaluation of Carotid Bulb Stenosis: Is It Time for a Reappraisal? LIFE (BASEL, SWITZERLAND) 2022; 12:life12111678. [PMID: 36362834 PMCID: PMC9697210 DOI: 10.3390/life12111678] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 10/13/2022] [Accepted: 10/20/2022] [Indexed: 01/25/2023]
Abstract
We aimed to perform an anatomical evaluation of the carotid bulb using CT-angiography, implement a new reliable index for carotid stenosis quantification and to assess the accuracy of relationship between NASCET and ECST methods in a large adult population. The cross-sectional areas of the healthy carotid at five levels were measured by two experienced radiologists. A regression analysis was performed in order to quantify the relationship between the areas of the carotid bulb at different carotid bulbar level. A new index (Regression indeX, RegX) for carotid stenosis quantification was proposed. Five different stenoses with different grade in three bulbar locations were simulated for all patients for a total of 1365 stenoses and were used for a direct comparison of the RegX, NASCET, and ECST methods. The results of this study demonstrated that the RegX index provided a consistent and accurate measure of carotid stenosis through the application of the ECST method, avoiding the limitations of NASCET method. Furthermore, our results strongly depart from the consolidated relationships between NASCET and ECST values used in clinical practice and reported in extensive medical literature. In particular, we highlighted that a major misdiagnosis in patient selection for CEA could be introduced because of the large underestimation of real stenosis degree provided by the NASCET method. A reappraisal of carotid stenosis patients' work-up is evoked by the effectiveness of state-of-the-art noninvasive contemporary carotid imaging.
Collapse
Affiliation(s)
- Antonio Pierro
- Radiology Department, Cardarelli Regional Hospital, 86100 Campobasso, Italy
| | - Pietro Modugno
- Vascular Surgery Unit, Gemelli Molise Hospital, 86100 Campobasso, Italy
| | - Roberto Iezzi
- Radiology Department, Fondazione Policlinico Universitario A. Gemelli-IRCCS, 00168 Rome, Italy
| | - Savino Cilla
- Medical Physics Unit, Gemelli Molise Hospital, 86100 Campobasso, Italy
- Correspondence:
| |
Collapse
|
6
|
Jia Y, Liu X, Zhang L, Kong X, Chen S, Zhang L, Wang J, Shu S, Liu J, Fu X, Liu D, Wang J, Shi H. Integrated head and neck imaging of symptomatic patients with stroke using simultaneous non-contrast cardiovascular magnetic resonance angiography and intraplaque hemorrhage imaging as compared with digital subtraction angiography. J Cardiovasc Magn Reson 2022; 24:19. [PMID: 35307027 PMCID: PMC8935695 DOI: 10.1186/s12968-022-00849-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Accepted: 02/17/2022] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Both stenosis rate and intraplaque hemorrhage (IPH) are important predictors of stroke risk. Simultaneous non-contrast angiography and intraplaque hemorrhage (SNAP) cardiovascular magnetic resonance (CMR) imaging can detect both stenosis rate and IPH. We aimed to evaluate consistency between SNAP and digital subtraction angiography (DSA) to assess symptomatic patients with stroke and explore the performance of SNAP to identify IPH and the clinical factors associated with IPH. METHODS Eighty-one symptomatic patients with stroke, admitted to Wuhan Union Hospital who underwent CMR high-resolution vessel wall imaging (HR-VWI) and SNAP, were retrospectively identified. For patients who received interventional therapy, the imaging functions of SNAP and HR-VWI were compared with DSA. The diameters of the intracranial and carotid vessels were measured, and stenotic vessels were identified. The consistency of SNAP and HR-VWI in identifying IPH was also examined, and the correlations between IPH and clinical factors were analyzed. RESULTS SNAP was more consistent with DSA than HR-VWI in measuring vascular stenosis (intraclass correlation coefficient [ICC]SNAP-DSA = 0.917, ICC HR-VWI-DSA = 0.878). Regarding the diameter measurements of each intracranial and carotid vessel segment, SNAP was superior or similar to HR-VWI, and both were consistent with DSA in the measurement of major intracranial vascular segments. HR-VWI and SNAP exhibited acceptable agreement in identifying IPH (Kappa = 0.839, 95% confidence interval [CI]: 0.704-0.974). Patients who underwent interventional therapy had a higher plaque burden (P < 0.001). Patients with IPH had lower levels of high-density lipoprotein cholesterol (HDL) (P = 0.038) and higher levels of blood glucose (P = 0.007) and cystatin C (P = 0.040). CONCLUSIONS CMR SNAP is consistent with DSA in measuring vessel diameters and identifying atherosclerosis stenosis in each intracranial and carotid vessel segment. SNAP is also a potential alternative to HR-VWI in identifying stenosis and IPH.
Collapse
Affiliation(s)
- Yuxi Jia
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Xiaoming Liu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Lan Zhang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Xiangchuang Kong
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Shuo Chen
- Center for Biomedical Imaging Research, Tsinghua University School of Medicine, Haidian District, Beijing, China
| | - Lei Zhang
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Jiazheng Wang
- Clinical & Technical Solutions, Philips Healthcare, Beijing, China
| | - Shenglei Shu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Jia Liu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Xiaona Fu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Dingxi Liu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China
| | - Jing Wang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China.
| | - Heshui Shi
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China.
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, 430022, China.
| |
Collapse
|
7
|
Suri JS, Bhagawati M, Paul S, Protogerou AD, Sfikakis PP, Kitas GD, Khanna NN, Ruzsa Z, Sharma AM, Saxena S, Faa G, Laird JR, Johri AM, Kalra MK, Paraskevas KI, Saba L. A Powerful Paradigm for Cardiovascular Risk Stratification Using Multiclass, Multi-Label, and Ensemble-Based Machine Learning Paradigms: A Narrative Review. Diagnostics (Basel) 2022; 12:diagnostics12030722. [PMID: 35328275 PMCID: PMC8947682 DOI: 10.3390/diagnostics12030722] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/10/2022] [Accepted: 03/13/2022] [Indexed: 12/16/2022] Open
Abstract
Background and Motivation: Cardiovascular disease (CVD) causes the highest mortality globally. With escalating healthcare costs, early non-invasive CVD risk assessment is vital. Conventional methods have shown poor performance compared to more recent and fast-evolving Artificial Intelligence (AI) methods. The proposed study reviews the three most recent paradigms for CVD risk assessment, namely multiclass, multi-label, and ensemble-based methods in (i) office-based and (ii) stress-test laboratories. Methods: A total of 265 CVD-based studies were selected using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model. Due to its popularity and recent development, the study analyzed the above three paradigms using machine learning (ML) frameworks. We review comprehensively these three methods using attributes, such as architecture, applications, pro-and-cons, scientific validation, clinical evaluation, and AI risk-of-bias (RoB) in the CVD framework. These ML techniques were then extended under mobile and cloud-based infrastructure. Findings: Most popular biomarkers used were office-based, laboratory-based, image-based phenotypes, and medication usage. Surrogate carotid scanning for coronary artery risk prediction had shown promising results. Ground truth (GT) selection for AI-based training along with scientific and clinical validation is very important for CVD stratification to avoid RoB. It was observed that the most popular classification paradigm is multiclass followed by the ensemble, and multi-label. The use of deep learning techniques in CVD risk stratification is in a very early stage of development. Mobile and cloud-based AI technologies are more likely to be the future. Conclusions: AI-based methods for CVD risk assessment are most promising and successful. Choice of GT is most vital in AI-based models to prevent the RoB. The amalgamation of image-based strategies with conventional risk factors provides the highest stability when using the three CVD paradigms in non-cloud and cloud-based frameworks.
Collapse
Affiliation(s)
- Jasjit S. Suri
- Stroke Diagnostic and Monitoring Division, AtheroPoint™, Roseville, CA 95661, USA
- Correspondence: ; Tel.: +1-(916)-749-5628
| | - Mrinalini Bhagawati
- Department of Biomedical Engineering, North-Eastern Hill University, Shillong 793022, India; (M.B.); (S.P.)
| | - Sudip Paul
- Department of Biomedical Engineering, North-Eastern Hill University, Shillong 793022, India; (M.B.); (S.P.)
| | - Athanasios D. Protogerou
- Research Unit Clinic, Laboratory of Pathophysiology, Department of Cardiovascular Prevention, National and Kapodistrian University of Athens, 11527 Athens, Greece;
| | - Petros P. Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, 11527 Athens, Greece;
| | - George D. Kitas
- Arthritis Research UK Centre for Epidemiology, Manchester University, Manchester 46962, UK;
| | - Narendra N. Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110020, India;
| | - Zoltan Ruzsa
- Department of Internal Medicines, Invasive Cardiology Division, University of Szeged, 6720 Szeged, Hungary;
| | - Aditya M. Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22903, USA;
| | - Sanjay Saxena
- Department of CSE, International Institute of Information Technology, Bhubaneswar 751003, India;
| | - Gavino Faa
- Department of Pathology, A.O.U., di Cagliari-Polo di Monserrato s.s., 09045 Cagliari, Italy;
| | - John R. Laird
- Cardiology Department, St. Helena Hospital, St. Helena, CA 94574, USA;
| | - Amer M. Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada;
| | - Manudeep K. Kalra
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA;
| | - Kosmas I. Paraskevas
- Department of Vascular Surgery, Central Clinic of Athens, N. Iraklio, 14122 Athens, Greece;
| | - Luca Saba
- Department of Radiology, A.O.U., di Cagliari-Polo di Monserrato s.s., 09045 Cagliari, Italy;
| |
Collapse
|
8
|
Suri JS, Bhagawati M, Paul S, Protogeron A, Sfikakis PP, Kitas GD, Khanna NN, Ruzsa Z, Sharma AM, Saxena S, Faa G, Paraskevas KI, Laird JR, Johri AM, Saba L, Kalra M. Understanding the bias in machine learning systems for cardiovascular disease risk assessment: The first of its kind review. Comput Biol Med 2022; 142:105204. [DOI: 10.1016/j.compbiomed.2021.105204] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Revised: 12/29/2021] [Accepted: 12/29/2021] [Indexed: 02/09/2023]
|
9
|
Konstantonis G, Singh KV, Sfikakis PP, Jamthikar AD, Kitas GD, Gupta SK, Saba L, Verrou K, Khanna NN, Ruzsa Z, Sharma AM, Laird JR, Johri AM, Kalra M, Protogerou A, Suri JS. Cardiovascular disease detection using machine learning and carotid/femoral arterial imaging frameworks in rheumatoid arthritis patients. Rheumatol Int 2022; 42:215-239. [PMID: 35013839 DOI: 10.1007/s00296-021-05062-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Accepted: 11/29/2021] [Indexed: 12/31/2022]
Abstract
The study proposes a novel machine learning (ML) paradigm for cardiovascular disease (CVD) detection in individuals at medium to high cardiovascular risk using data from a Greek cohort of 542 individuals with rheumatoid arthritis, or diabetes mellitus, and/or arterial hypertension, using conventional or office-based, laboratory-based blood biomarkers and carotid/femoral ultrasound image-based phenotypes. Two kinds of data (CVD risk factors and presence of CVD-defined as stroke, or myocardial infarction, or coronary artery syndrome, or peripheral artery disease, or coronary heart disease) as ground truth, were collected at two-time points: (i) at visit 1 and (ii) at visit 2 after 3 years. The CVD risk factors were divided into three clusters (conventional or office-based, laboratory-based blood biomarkers, carotid ultrasound image-based phenotypes) to study their effect on the ML classifiers. Three kinds of ML classifiers (Random Forest, Support Vector Machine, and Linear Discriminant Analysis) were applied in a two-fold cross-validation framework using the data augmented by synthetic minority over-sampling technique (SMOTE) strategy. The performance of the ML classifiers was recorded. In this cohort with overall 46 CVD risk factors (covariates) implemented in an online cardiovascular framework, that requires calculation time less than 1 s per patient, a mean accuracy and area-under-the-curve (AUC) of 98.40% and 0.98 (p < 0.0001) for CVD presence detection at visit 1, and 98.39% and 0.98 (p < 0.0001) at visit 2, respectively. The performance of the cardiovascular framework was significantly better than the classical CVD risk score. The ML paradigm proved to be powerful for CVD prediction in individuals at medium to high cardiovascular risk.
Collapse
Affiliation(s)
- George Konstantonis
- Rheumatology Unit, National Kapodistrian University of Athens, Athens, Greece
| | | | - Petros P Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, Athens, Greece
| | - Ankush D Jamthikar
- Research Scientist, AtheroPoint™, USA, Roseville, CA, USA.,Visvesvaraya National Institute of Technology, Nagpur, India
| | - George D Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK.,Arthritis Research UK Epidemiology Unit, Manchester University, Manchester, M13, UK
| | - Suneet K Gupta
- Department of Computer Science, Bennett University, Gr. Noida, India
| | - Luca Saba
- Department of Radiology, University of Cagliari, Cagliari, Italy
| | - Kleio Verrou
- Department of Medicine, National and Kapodistrian University of Athens, Athens, Greece
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha Apollo Hospitals, New Delhi, India
| | - Zoltan Ruzsa
- Department of Internal Medicines, Invasive Cardiology Division, University of Szeged, Szeged, Hungary
| | - Aditya M 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
| | - Amer M Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, ON, Canada
| | - Manudeep Kalra
- Department of Radiology, Massachusetts General Hospital, 55 Fruit Street, Boston, MA, USA
| | - Athanasios Protogerou
- Cardiovascular Prevention Unit, Department of Pathophysiology, National Kapodistrian University of Athens, Athens, Greece
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, 95661, USA.
| |
Collapse
|
10
|
Lembo M, Manzi MV, Mancusi C, Morisco C, Rao MAE, Cuocolo A, Izzo R, Trimarco B. Advanced imaging tools for evaluating cardiac morphological and functional impairment in hypertensive disease. J Hypertens 2022; 40:4-14. [PMID: 34582136 PMCID: PMC10871661 DOI: 10.1097/hjh.0000000000002967] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 06/29/2021] [Accepted: 06/30/2021] [Indexed: 01/19/2023]
Abstract
Arterial hypertension represents a systemic burden, and it is responsible of various morphological, functional and tissue modifications affecting the heart and the cardiovascular system. Advanced imaging techniques, such as speckle tracking and three-dimensional echocardiography, cardiac magnetic resonance, computed tomography and PET-computed tomography, are able to identify cardiovascular injury at different stages of arterial hypertension, from subclinical alterations and overt organ damage to possible complications related to pressure overload, thus giving a precious contribution for guiding timely and appropriate management and therapy, in order to improve diagnostic accuracy and prevent disease progression. The present review focuses on the peculiarity of different advanced imaging tools to provide information about different and multiple morphological and functional aspects involved in hypertensive cardiovascular injury. This evaluation emphasizes the usefulness of the emerging multiimaging approach for a comprehensive overview of arterial hypertension induced cardiovascular damage.
Collapse
Affiliation(s)
- Maria Lembo
- Department of Advanced Biomedical Sciences, Federico II University of Naples, Naples, Italy
| | | | | | | | | | | | | | | |
Collapse
|
11
|
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.
Collapse
|
12
|
Saba L, Sanagala SS, Gupta SK, Koppula VK, Johri AM, Khanna NN, Mavrogeni S, Laird JR, Pareek G, Miner M, Sfikakis PP, Protogerou A, Misra DP, Agarwal V, Sharma AM, Viswanathan V, Rathore VS, Turk M, Kolluri R, Viskovic K, Cuadrado-Godia E, Kitas GD, Sharma N, Nicolaides A, Suri JS. Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:1206. [PMID: 34430647 PMCID: PMC8350643 DOI: 10.21037/atm-20-7676] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 02/25/2021] [Indexed: 12/12/2022]
Abstract
Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most.
Collapse
Affiliation(s)
- Luca Saba
- Department of Radiology, Azienda Ospedaliero Universitaria (AOU), Cagliari, Italy
| | - Skandha S Sanagala
- CSE Department, CMR College of Engineering & Technology, Hyderabad, India.,CSE Department, Bennett University, Greater Noida, UP, India
| | - Suneet K Gupta
- CSE Department, Bennett University, Greater Noida, UP, India
| | - Vijaya K Koppula
- CSE Department, CMR College of Engineering & Technology, Hyderabad, India
| | - Amer M Johri
- Department of Medicine, Division of Cardiology, Queen's University, Kingston, Ontario, 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
| | - Petros P Sfikakis
- Rheumatology Unit, National Kapodistrian University of Athens, Greece
| | - Athanasios Protogerou
- Department of Cardiovascular Prevention, National and Kapodistrian University of Athens, Athens, Greece
| | - Durga P Misra
- Department of Clinical Immunology and Rheumatology, SGPGIMS, Lucknow, India
| | - Vikas Agarwal
- Department of Clinical Immunology and Rheumatology, SGPGIMS, Lucknow, India
| | - Aditya M Sharma
- Division of Cardiovascular Medicine, University of Virginia, VA, USA
| | - Vijay Viswanathan
- MV Hospital for Diabetes & Professor M Viswanathan Diabetes Research Centre, Chennai, India
| | - Vijay S Rathore
- Nephrology Department, Kaiser Permanente, Sacramento, CA, USA
| | - Monika Turk
- The Hanse-Wissenschaftskolleg Institute for Advanced Study, Delmenhorst, Germany
| | | | | | | | - George D Kitas
- R & D Academic Affairs, Dudley Group NHS Foundation Trust, Dudley, UK
| | - Neeraj Sharma
- Department of Biomedical Engineering, IIT-BHU, Banaras, UP, India
| | - Andrew Nicolaides
- Vascular Screening and Diagnostic Centre, University of Nicosia, Nicosia, Cyprus
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
| |
Collapse
|
13
|
Viswanathan V, Puvvula A, Jamthikar AD, Saba L, Johri AM, Kotsis V, Khanna NN, Dhanjil SK, Majhail M, Misra DP, Agarwal V, Kitas GD, Sharma AM, Kolluri R, Naidu S, Suri JS. Bidirectional link between diabetes mellitus and coronavirus disease 2019 leading to cardiovascular disease: A narrative review. World J Diabetes 2021; 12:215-237. [PMID: 33758644 PMCID: PMC7958478 DOI: 10.4239/wjd.v12.i3.215] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 12/20/2020] [Accepted: 02/11/2021] [Indexed: 02/06/2023] Open
Abstract
Coronavirus disease 2019 (COVID-19) is a global pandemic where several comorbidities have been shown to have a significant effect on mortality. Patients with diabetes mellitus (DM) have a higher mortality rate than non-DM patients if they get COVID-19. Recent studies have indicated that patients with a history of diabetes can increase the risk of severe acute respiratory syndrome coronavirus 2 infection. Additionally, patients without any history of diabetes can acquire new-onset DM when infected with COVID-19. Thus, there is a need to explore the bidirectional link between these two conditions, confirming the vicious loop between "DM/COVID-19". This narrative review presents (1) the bidirectional association between the DM and COVID-19, (2) the manifestations of the DM/COVID-19 loop leading to cardiovascular disease, (3) an understanding of primary and secondary factors that influence mortality due to the DM/COVID-19 loop, (4) the role of vitamin-D in DM patients during COVID-19, and finally, (5) the monitoring tools for tracking atherosclerosis burden in DM patients during COVID-19 and "COVID-triggered DM" patients. We conclude that the bidirectional nature of DM/COVID-19 causes acceleration towards cardiovascular events. Due to this alarming condition, early monitoring of atherosclerotic burden is required in "Diabetes patients during COVID-19" or "new-onset Diabetes triggered by COVID-19 in Non-Diabetes patients".
Collapse
Affiliation(s)
- Vijay Viswanathan
- M Viswanathan Hospital for Diabetes, M Viswanathan Diabetes Research Centre, Chennai 600013, India
| | - Anudeep Puvvula
- Annu’s Hospitals for Skin and Diabetes, Nellore 524101, Andhra Pradesh, India
| | - Ankush D Jamthikar
- Department of Electronics and Communications, Visvesvaraya National Institute of Technology, Nagpur 440010, Maharashtra, India
| | - Luca Saba
- Department of Radiology, University of Cagliari, Monserrato 09045, Cagliari, Italy
| | - Amer M Johri
- Department of Medicine, Division of Cardiology, Queen’s University, Kingston, ON K7L 3N6, Canada
| | - Vasilios Kotsis
- 3rd Department of Internal Medicine, Hypertension Center, Papageorgiou Hospital, Aristotle University of Thessaloniki, Thessaloniki 541-24, Greece
| | - Narendra N Khanna
- Department of Cardiology, Indraprastha APOLLO Hospitals, New Delhi 110020, India
| | - Surinder K Dhanjil
- Stroke Diagnosis and Monitoring Division, AtheroPoint™ LLC, CA 95661, United States
| | - Misha Majhail
- Stroke Diagnosis and Monitoring Division, AtheroPoint™, Roseville, CA 95661, United States
| | - Durga Prasanna Misra
- Department of Clinical Immunology and Rheumatology, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India
| | - Vikas Agarwal
- Departments of Medicine, Sanjay Gandhi Postgraduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India
| | - George D Kitas
- Academic Affairs, Dudley Group NHS Foundation Trust, Dudley DY1 2HQ, United Kingdom
- Arthritis Research UK Epidemiology Unit, Manchester University, Manchester M13 9PL, United Kingdom
| | - Aditya M Sharma
- Division of Cardiovascular Medicine, University of Virginia, Charlottesville, VA 22908, United States
| | - Raghu Kolluri
- OhioHealth Heart and Vascular, Ohio, OH 43082, United States
| | - Subbaram Naidu
- Electrical Engineering Department, University of Minnesota, Duluth, MN 55812, United States
| | - Jasjit S Suri
- Stroke Diagnosis and Monitoring Division, AtheroPoint™, Roseville, CA 95661, United States
| |
Collapse
|
14
|
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]
|