A machine learning framework for risk prediction of multi-label cardiovascular events based on focused carotid plaque B-Mode ultrasound: A Canadian study

Jamthikar, Ankush; Gupta, Deep; Johri, Amer M.; Mantella, Laura E.; Saba, Luca; Suri, Jasjit S.

doi:10.1016/j.compbiomed.2021.105102

Cited by 22 publications

(14 citation statements)

References 54 publications

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“…ML-based algorithms construct the outcome, which is based on various linear and non-linear patterns found in the input risk predictors [ 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 ]. This is a critical feature of AI-driven algorithms that differentiates them from other traditional CVD risk assessments.…”

Section: An Overview Of Artificial Intelligence Applications In Healt...mentioning

confidence: 99%

“…Recently, it has been explained that ML applications have dominated the field of medical imaging, including diabetes [ 101 , 102 ], cardiovascular disease [ 79 , 84 , 103 ], liver [ 98 , 104 ], thyroid [ 105 , 106 ], ovarian [ 29 , 107 ], and prostate cancers [ 108 ], as well as risk characterization using coronary and vascular screening [ 107 , 109 ] using carotid angiography [ 110 ]. Numerous medical imaging modalities can depict COVID-19 symptoms and lesions, in magnetic resonance imaging (MRI) [ 37 , 111 ], computed tomography (CT) [ 112 ], ultrasonography (US) [ 113 ], and CT for lung imaging [ 37 , 111 ].…”

Section: An Overview Of Artificial Intelligence Applications In Healt...mentioning

confidence: 99%

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Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

Khanna¹,

Maindarkar

Viswanathan

et al. 2022

Healthcare

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Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals.

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Section: An Overview Of Artificial Intelligence Applications In Healt...mentioning

confidence: 99%

Section: An Overview Of Artificial Intelligence Applications In Healt...mentioning

confidence: 99%

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

Khanna¹,

Maindarkar

Viswanathan

et al. 2022

Healthcare

Self Cite

View full text Add to dashboard Cite

show abstract

“…One significant challenge is the complexity of interpreting and integrating vast amounts of diverse data, the so-called “big” data, 29 as PM harnesses a wealth of different data types, including genetic information, molecular profiling, electronic health records (EHRs), laboratory tests, patient histories, lifestyle data, and environmental factors motivating for a composite design. 30 However, the seamless extraction and harmonization of these multifaceted data originating from multiple sources in a meaningful and standardised way poses technical and logistical challenges. Gathering patient data is the first step in orchestrating PM, such as genetic data from deoxyribonucleic acid (DNA) sequencing techniques, biomarker data from blood tests, and lifestyle data from wearables and health monitoring devices.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review

Singh,

Kumar,

Khanna

et al. 2024

eClinicalMedicine

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“…[8][9][10] ML-based multi-label classification algorithms can provide better cardiovascular event prediction. 11 Such classification techniques include problem transformation and algorithm adaptation methods. The overall multi-label classification accuracy, sensitivity and specificity of these methods yield the best prediction for cardiovascular endpoints.…”

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confidence: 99%

“…The overall multi-label classification accuracy, sensitivity and specificity of these methods yield the best prediction for cardiovascular endpoints. 11 For example, ML algorithms may predict abdominal aortic aneurysm (AAA) expansion and risk of rupture. 12 Consequently, ML could help assess the indications to treat and improve the surveillance of small asymptomatic AAAs.…”

mentioning

confidence: 99%

Applications of Artificial Intelligence in Vascular Diseases

Paraskevas¹,

Saba

Suri³

2022

Angiology

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In their comprehensive article, Lareyre et al. evaluated scientific publications on artificial intelligence (AI) on noncardiac vascular diseases. 1 The authors provided a quantitative assessment of research output on publications related to AI in carotid artery stenosis, aortic and peripheral artery disease. 1 The bibliometric analysis of original articles published on AI in non-cardiac vascular diseases showed increased numbers over the past 5 years. 1 AI, with its numerous applications, has emerged as an indispensable part of healthcare. By means of its 2 subcategories, namely, machine learning (ML) and deep learning (DL), AI has expanded over the field of medical imaging and medical diagnosis to cover all aspects of vascular disease, from diagnosis to staging and treatment. 2,3 Atherosclerotic plaque formation in the carotid or peripheral arteries is a dynamic process. Advancements in ML and DL have made it possible to identify, classify and characterize the atherosclerotic plaque. 4 The nature of AI design has evolved over time, starting from manual, signal processing methods and eventually leading to AI learning models. 4 ML can be used for plaque characterization based on ultrasound, computed tomography (CT) or magnetic resonance angiography (MRI). While AI models may independently operate on each imaging technique, there are differences and similarities covering various aspects, such as multiresolution, extracted and texture/histogram features, performance metrics, segmentation and classification. 4 Diet and nutrition contribute to cardiovascular events in countries with high socio-demographic index. 5,6 Defining the role of nutrition in the formation of carotid atherosclerosis can determine cerebrovascular/cardiovascular events and all-cause mortality. 5,6 Another way to improve risk assessment is to use AI-based algorithms for cardiovascular risk stratification. 7 Such AI-based algorithms can handle a large number of risk predictors in a model. 7 Furthermore, since AI-based algorithms are data-specific algorithms, they capture and learn from the complex non-linear interrelationship among the risk predictors. 7 Conventional cardiovascular risk calculators (e.g. the Framingham risk score) have certain drawbacks when

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A machine learning framework for risk prediction of multi-label cardiovascular events based on focused carotid plaque B-Mode ultrasound: A Canadian study

Cited by 22 publications

References 54 publications

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review

Applications of Artificial Intelligence in Vascular Diseases

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