Low-cost preventive screening using carotid ultrasound in patients with diabetes

Viswanathan, Vijay; Jamthikar, Ankush; Gupta, Deep; N, Shanu; Puvvula, Anudeep; Khanna, Narendra N.; Saba, Luca; Omerzum, Tomaz; Višković, Klaudija; Mavrogeni, Sophie; Turk, Monika; Laird, John R.; Pareek, Gyan; Miner, Martin; Sfikakis, Petros P.; Protogerou, Athanasios; Kitas, George D.; Chitra, Surya; Joshi, Sujata; Fiscian, Henreitta; Folson, Aba Ankomaba; Wu, Dee H.; Ruzsa, Zoltán; Nicolaides, Andrew; Sharma, Aditya; Bhatt, Deepak L.; Suri, Jasjit S.

doi:10.2741/4850

Cited by 32 publications

(6 citation statements)

References 104 publications

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“…Image-based biomarkers, such as TPA, have shown to have a strong link with eGFR [ 266 ], and thus AI-based solution have adapted the usage of TPA in the modeling process. AtheroEdge systems were designed to keep both AI-based and non-AI-based methods at a low cost [ 267 ]. Note that the importance of the automated biomarker guidelines were recently revisited for CVD/stroke risk stratification [ 265 ], thus the above AI-based and non-AI-based methods are powerful solutions for CVD/stroke risk assessment.…”

Section: Deep Learning For Cvd/stroke Risk Assessment In Pd Patients ...mentioning

confidence: 99%

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review

Suri¹,

Maindarkar

Sauseng

et al. 2022

Diagnostics

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Background and Motivation: Parkinson’s disease (PD) is one of the most serious, non-curable, and expensive to treat. Recently, machine learning (ML) has shown to be able to predict cardiovascular/stroke risk in PD patients. The presence of COVID-19 causes the ML systems to become severely non-linear and poses challenges in cardiovascular/stroke risk stratification. Further, due to comorbidity, sample size constraints, and poor scientific and clinical validation techniques, there have been no well-explained ML paradigms. Deep neural networks are powerful learning machines that generalize non-linear conditions. This study presents a novel investigation of deep learning (DL) solutions for CVD/stroke risk prediction in PD patients affected by the COVID-19 framework. Method: The PRISMA search strategy was used for the selection of 292 studies closely associated with the effect of PD on CVD risk in the COVID-19 framework. We study the hypothesis that PD in the presence of COVID-19 can cause more harm to the heart and brain than in non-COVID-19 conditions. COVID-19 lung damage severity can be used as a covariate during DL training model designs. We, therefore, propose a DL model for the estimation of, (i) COVID-19 lesions in computed tomography (CT) scans and (ii) combining the covariates of PD, COVID-19 lesions, office and laboratory arterial atherosclerotic image-based biomarkers, and medicine usage for the PD patients for the design of DL point-based models for CVD/stroke risk stratification. Results: We validated the feasibility of CVD/stroke risk stratification in PD patients in the presence of a COVID-19 environment and this was also verified. DL architectures like long short-term memory (LSTM), and recurrent neural network (RNN) were studied for CVD/stroke risk stratification showing powerful designs. Lastly, we examined the artificial intelligence bias and provided recommendations for early detection of CVD/stroke in PD patients in the presence of COVID-19.Conclusion: The DL is a very powerful tool for predicting CVD/stroke risk in PD patients affected by COVID-19.

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Section: Deep Learning For Cvd/stroke Risk Assessment In Pd Patients ...mentioning

confidence: 99%

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review

Suri¹,

Maindarkar

Sauseng

et al. 2022

Diagnostics

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show abstract

“…A person's susceptibility to developing atherosclerosis may be identified by the risk factors associated with it. The risk factors responsible for atherosclerotic disease can be categorized into (a) conventional, such as hypertension, obesity, body mass index, ethnicity, gender, ethanol use, and smoking [48] and (b) blood biomarkers such as lipids, hemoglobin A1c (HbA1c) as diabetes index [35,49], estimated glomerular filtration rate (eGFR) as renal index [50], erythrocyte sedimentation rate (ESR) as rheumatoid arthritis index [51], homocysteine, triglycerides, and hCRP/c reactive protein. Fig.…”

Section: Cardiovascular Risk Factorsmentioning

confidence: 99%

“…The independent contribution of each risk predictor in the 10-year CVD risk is shown in Fig. 9 and is preferred as a preventive screening tool [35]. This tool has been applied to the bulb region and correlated to renal scores [50,[180][181][182] or rheumatology patients [183].…”

Section: Integration Of Image Phenotypes With Blood Biomarkersmentioning

confidence: 99%

“…Ultrasound imaging is the most popular lowcost, preventive screening solution, which is currently adopted for CVD/stroke risk assessment [34,35]. Compared to its other non-invasive counterparts [34], it is a portable and user-friendly imaging technique.…”

Section: Introductionmentioning

confidence: 99%

“…Compared to its other non-invasive counterparts [34], it is a portable and user-friendly imaging technique. For example, AtheroEdge™-a carotid image-based CVD risk calculator takes advantage of image phenotypes such as carotid intima-media thickness [36], plaque area, and its variability derived from the carotid scans [31,35,37]. Such a tool can be better used in the preventive framework provided that it can be applied to study the effect of nutrition [38][39][40][41] (see Fig.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Nutrition, atherosclerosis, arterial imaging, cardiovascular risk stratification, and manifestations in COVID-19 framework: a narrative review

2021

Front Biosci (Landmark Ed)

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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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Low-cost preventive screening using carotid ultrasound in patients with diabetes

Cited by 32 publications

References 104 publications

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review

Deep Learning Paradigm for Cardiovascular Disease/Stroke Risk Stratification in Parkinson’s Disease Affected by COVID-19: A Narrative Review

Nutrition, atherosclerosis, arterial imaging, cardiovascular risk stratification, and manifestations in COVID-19 framework: a narrative review

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

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