Performance evaluation of 10-year ultrasound image-based stroke/cardiovascular (CV) risk calculator by comparing against ten conventional CV risk calculators: A diabetic study

Khanna, Narendra N.; Jamthikar, Ankush; Gupta, Deep; Nicolaides, Andrew; Araki, Tadashi; Saba, Luca; Cuadrado-Godia, Elisa; Sharma, Aditya; Omerzu, Tomaž; Suri, Harman S.; Gupta, Ajay; Mavrogeni, Sophie; Turk, Monika; Laird, John R.; Protogerou, Athanasios; Sfikakis, Petros P.; Kitas, George D.; Viswanathan, Vijay; Suri, Jasjit S.

doi:10.1016/j.compbiomed.2019.01.002

Cited by 45 publications

(72 citation statements)

References 60 publications

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“…We believe that the AtheroRisk-Integrated ML system is efficient, accurate, and affordable. Risk stratification performed using EEGS is the first step to prevent higher costs and simplicity in design (4,11,12,29,41). Although the proposed study has clearly met the pre-defined hypothesis, more modifications can be possible to improve the CV/ stroke risk assessment such as conducting a multicenter study.…”

Section: Study Limitations and Future Scopementioning

confidence: 99%

“…Two operators (with 15 years of experience) analyzed the scans. We used the CUS image acquisition protocol presented before (11,12,21,22). The analytical approach and study design for this manuscript are unique with respect to previously published reports using this cohort (11,12,21,22).…”

Section: Study Population and Image Acquisitionmentioning

confidence: 99%

“…Furthermore, the carotid ultrasound image-based phenotypes (CUSIP) such as carotid intima-media thickness (cIMT), carotid plaque (CP), and carotid artery stenosis are considered the most significant biomarkers of CV/stroke events (7)(8)(9)(10). Thus, integrating these CUSIP with CRF can convey a larger power of risk assessment (11)(12)(13). Such risk assessment systems are affordable but do not incorporate the full capabilities of deep learning algorithms which are now emerging as powerful and novel tools in healthcare.…”

Section: Introductionmentioning

confidence: 99%

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A low-cost machine learning-based cardiovascular/stroke risk assessment system: integration of conventional factors with image phenotypes

Jamthikar¹,

Gupta²,

Khanna³

et al. 2019

Cardiovasc. Diagn. Ther.

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Background: Most cardiovascular (CV)/stroke risk calculators using the integration of carotid ultrasound image-based phenotypes (CUSIP) with conventional risk factors (CRF) have shown improved risk stratification compared with either method. However such approaches have not yet leveraged the potential of machine learning (ML). Most intelligent ML strategies use follow-ups for the endpoints but are costly and time-intensive. We introduce an integrated ML system using stenosis as an endpoint for training and determine whether such a system can lead to superior performance compared with the conventional ML system. Methods: The ML-based algorithm consists of an offline and online system. The offline system extracts 47 features which comprised of 13 CRF and 34 CUSIP. Principal component analysis (PCA) was used to select the most significant features. These offline features were then trained using the event-equivalent gold standard (consisting of percentage stenosis) using a random forest (RF) classifier framework to generate training coefficients. The online system then transforms the PCA-based test features using offline trained coefficients to predict the risk labels on test subjects. The above ML system determines the area under the curve (AUC) using a 10-fold cross-validation paradigm. The above system so-called "AtheroRisk-Integrated" was compared against "AtheroRisk-Conventional", where only 13 CRF were considered in a feature set. Results: Left and right common carotid arteries of 202 Japanese patients (Toho University, Japan) were retrospectively examined to obtain 395 ultrasound scans. AtheroRisk-Integrated system [AUC =0.80, P<0.0001, 95% confidence interval (CI): 0.77 to 0.84] showed an improvement of ~18% against AtheroRisk-Conventional ML (AUC =0.68, P<0.0001, 95% CI: 0.64 to 0.72). Conclusions: ML-based integrated model with the event-equivalent gold standard as percentage stenosis is powerful and offers low cost and high performance CV/stroke risk assessment.

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Section: Study Limitations and Future Scopementioning

confidence: 99%

Section: Study Population and Image Acquisitionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A low-cost machine learning-based cardiovascular/stroke risk assessment system: integration of conventional factors with image phenotypes

Jamthikar¹,

Gupta²,

Khanna³

et al. 2019

Cardiovasc. Diagn. Ther.

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“…Recent studies have combined the effect of these image-based phenotypes with conventional risk factors, including pro-inflammatory markers like erythrocyte sedimentation rate (ESR), to perform CVD risk assessment [42][43][44]. Such integrated risk calculators have demonstrated better CVD risk stratification when compared to traditional CVD risk calculators in non-RA patients [42,43,45].…”

Section: Introductionmentioning

confidence: 99%

Cardiovascular risk assessment in patients with rheumatoid arthritis using carotid ultrasound B-mode imaging

Jamthikar

Gupta

Puvvula³

et al. 2020

Rheumatol Int

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Rheumatoid arthritis (RA) is a systemic chronic inflammatory disease that affects synovial joints and has various extraarticular manifestations, including atherosclerotic cardiovascular disease (CVD). Patients with RA experience a higher risk of CVD, leading to increased morbidity and mortality. Inflammation is a common phenomenon in RA and CVD. The pathophysiological association between these diseases is still not clear, and, thus, the risk assessment and detection of CVD in such patients is of clinical importance. Recently, artificial intelligence (AI) has gained prominence in advancing healthcare and, therefore, may further help to investigate the RA-CVD association. There are three aims of this review: (1) to summarize the three pathophysiological pathways that link RA to CVD; (2) to identify several traditional and carotid ultrasound image-based CVD risk calculators useful for RA patients, and (3) to understand the role of artificial intelligence in CVD risk assessment in RA patients. Our search strategy involves extensively searches in PubMed and Web of Science databases using search terms associated with CVD risk assessment in RA patients. A total of 120 peer-reviewed articles were screened for this review. We conclude that (a) two of the three pathways directly affect the atherosclerotic process, leading to heart injury, (b) carotid ultrasound image-based calculators have shown superior performance compared with conventional calculators, and (c) AI-based technologies in CVD risk assessment in RA patients are aggressively being adapted for routine practice of RA patients.

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“… 10 , 11 , 12 , 13 , 14 , 15 , 9 The primary reason for this poor performance is the dependence of such models on the cardiovascular risk factors (CRF) that does not provide complete information about cardiovascular health of patients. 16 , 17 , 18 , 19 , 20…”

Section: Introductionmentioning

confidence: 99%

Cardiovascular/stroke risk prevention: A new machine learning framework integrating carotid ultrasound image-based phenotypes and its harmonics with conventional risk factors

Jamthikar

Gupta

Khanna³

et al. 2020

Indian Heart Journal

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Motivation Machine learning (ML)-based stroke risk stratification systems have typically focused on conventional risk factors (CRF) ( AtheroRisk-conventional ). Besides CRF, carotid ultrasound image phenotypes (CUSIP) have shown to be powerful phenotypes risk stratification. This is the first ML study of its kind that integrates CUSIP and CRF for risk stratification ( AtheroRisk-integrated ) and compares against AtheroRisk-conventional . Methods Two types of ML-based setups called (i) AtheroRisk-integrated and (ii) AtheroRisk-conventional were developed using random forest (RF) classifiers. AtheroRisk-conventional uses a feature set of 13 CRF such as age, gender, hemoglobin A1c, fasting blood sugar, low-density lipoprotein, and high-density lipoprotein (HDL) cholesterol, total cholesterol (TC), a ratio of TC and HDL, hypertension, smoking, family history, triglyceride, and ultrasound-based carotid plaque score. AtheroRisk-integrated system uses the feature set of 38 features with a combination of 13 CRF and 25 CUSIP features (6 types of current CUSIP, 6 types of 10-year CUSIP, 12 types of quadratic CUSIP (harmonics), and age-adjusted grayscale median). Logistic regression approach was used to select the significant features on which the RF classifier was trained. The performance of both ML systems was evaluated by area-under-the-curve (AUC) statistics computed using a leave-one-out cross-validation protocol. Results Left and right common carotid arteries of 202 Japanese patients were retrospectively examined to obtain 404 ultrasound scans. RF classifier showed higher improvement in AUC (~ 57% ) for leave-one-out cross-validation protocol. Using RF classifier, AUC statistics for AtheroRisk-integrated system was higher (AUC = 0.99 ,p-value<0.001) compared to AtheroRisk-conventional (AUC = 0.63 ,p-value<0.001). Conclusion The AtheroRisk-integrated ML system outperforms the AtheroRisk-conventional ML system using RF classifier.

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Performance evaluation of 10-year ultrasound image-based stroke/cardiovascular (CV) risk calculator by comparing against ten conventional CV risk calculators: A diabetic study

Cited by 45 publications

References 60 publications

A low-cost machine learning-based cardiovascular/stroke risk assessment system: integration of conventional factors with image phenotypes

A low-cost machine learning-based cardiovascular/stroke risk assessment system: integration of conventional factors with image phenotypes

Cardiovascular risk assessment in patients with rheumatoid arthritis using carotid ultrasound B-mode imaging

Cardiovascular/stroke risk prevention: A new machine learning framework integrating carotid ultrasound image-based phenotypes and its harmonics with conventional risk factors

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