A special report on changing trends in preventive stroke/cardiovascular risk assessment via B-mode ultrasonography

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

doi:10.1016/b978-0-12-819445-4.00015-1

Cited by 20 publications

(70 citation statements)

References 111 publications

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“…One way of predicting the occurrence of these events is by performing risk assessment using conventional risk factors (CRF) that are responsible for the growth of atherosclerosis (3). However, CRF alone does not explain the elevated risk of CV/stroke events (4). This is because of the morphological variations in the atherosclerotic plaque that cannot be captured using CRF alone but which can easily be assessed using imaging modalities (5,6).…”

Section: Introductionmentioning

confidence: 99%

“…Imaging plays a vital role in developing a comprehensive preventive strategy to combat stroke and heart disease. Ultrasound and in particular carotid ultrasound (CUS) screening can easily be adapted in routine clinical practice compared to its other non-invasive counterparts such as computed tomography and magnetic resonance imaging (4,5). Even in the low-resolution images, CUS can capture the image-based phenotypes that reflect morphological variations in atherosclerotic plaque (4).…”

Section: Introductionmentioning

confidence: 99%

“…Ultrasound and in particular carotid ultrasound (CUS) screening can easily be adapted in routine clinical practice compared to its other non-invasive counterparts such as computed tomography and magnetic resonance imaging (4,5). Even in the low-resolution images, CUS can capture the image-based phenotypes that reflect morphological variations in atherosclerotic plaque (4). 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).…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Advancements in artificial intelligence (AI) are gaining popularity due to its ability to provide more accurate risk assessment in routine clinical practice (4). Specifically, machine learning (ML) algorithms have shown promising results in atherosclerotic plaque characterization and CV/ stroke risk stratification (4). Use of ML algorithms for accurate risk prediction requires the primary endpoints (CV events or cerebrovascular events) obtained from longitudinal trials which are expensive and require a large sample size (3,14,15).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

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: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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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Machine Learning in Stroke Medicine: Opportunities and Challenges for Risk Prediction and Prevention

Amann

2021

Advances in Neuroethics

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Stroke is one of the leading causes of mortality and disability worldwide, causing individual hardship and high economic cost for society. Reducing the global burden of stroke depends on a multi-pronged mission, and experts agree an important strategy in this mission is prevention. Prevention success can be bolstered through the strategic development and adoption of risk prediction tools. However, there are several limitations to risk prediction models currently available. A solution to some of these limitations may be found in machine learning (ML), a promising tool that can improve our ability to assess risk and ultimately prevent strokes.This chapter surveys the global burden of stroke and describes current practices for reducing stroke incidence and stroke mortality rates. In particular, the chapter reviews how ML applications are applied to stroke risk prediction and prevention and identifies important technological and methodological challenges for using ML in these contexts. The chapter concludes by drawing the readers’ attention to some of the questions and ethical challenges that arise as clinicians widely adopt ML-based applications in practice.

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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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A special report on changing trends in preventive stroke/cardiovascular risk assessment via B-mode ultrasonography

Cited by 20 publications

References 111 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

Machine Learning in Stroke Medicine: Opportunities and Challenges for Risk Prediction and Prevention

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

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