Atherosclerosis

2022

DOI: 10.1016/j.atherosclerosis.2022.06.1014

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International Union of Angiology (IUA) consensus paper on imaging strategies in atherosclerotic carotid artery imaging: From basic strategies to advanced approaches

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Pier Luigi Antignani²,

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et al.

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Cited by 31 publications

(28 citation statements)

References 219 publications

(276 reference statements)

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“…13,14 While the degree of luminal stenosis remains a critical feature, advances in US technology have likely led to identification of other US plaque features that require consideration as a culprit lesion in patients with stroke. 15,16 Unfortunately, a paucity of data specific to patients with ESUS exists.…”

Section: Ultrasoundmentioning

confidence: 99%

“…Technical advances in CT technology (ie, such as high-speed multidetector hardware and 3D reformatting software) have enabled detection of carotid artery disease on the scale of millimeters. 16 Because CTA is performed as a part of most institutional acute stroke algorithms, any features of nonstenotic plaques associated with ipsilateral stroke would be easily assessed in the clinical setting. Thus, CTA may, therefore, be the most practical technique to assess nonstenotic plaque features.…”

Section: Ctamentioning

confidence: 99%

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Nonstenotic Carotid Plaques and Embolic Stroke of Undetermined Source: A Multimodality Review

et al. 2022

AJNR Am J Neuroradiol

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Symptomatic nonstenotic carotid artery disease has been increasingly recognized as a thromboembolic source in patients who would otherwise be classified as having embolic stroke of undetermined source. Evidence suggests that certain plaque features seen on sonography, CT, and MR imaging in nonstenotic carotid artery disease may predispose to recurrent stroke in patients with embolic stroke of undetermined source. We performed a focused literature review to further study plaque features in the context of embolic stroke of undetermined source and to determine which plaque features may be associated with ipsilateral ischemic events in such patients. Plaque thickness as seen on both ultrasound and CT appears to have a consistent association with ipsilateral stroke in patients with embolic stroke of undetermined source across multiple studies. Intraplaque hemorrhage as seen on MR imaging is now understood to have a strong association with ipsilateral stroke in patients with embolic stroke of undetermined source. Continued study of various plaque features as seen on different modalities is warranted to uncover other potential associations.ABBREVIATIONS: ESUS ¼ embolic stroke of undetermined source; IPH ¼ intraplaque hemorrhage; LRNC ¼ lipid-rich necrotic core; SyNC ¼ symptomatic nonstenotic carotid artery disease; US ¼ ultrasound U p to one-third of strokes have no established mechanism and are considered to be cryptogenic. 1 In 2014, the term "embolic stroke of undetermined source" (ESUS) was established as a clinical entity in patients with nonlacunar cryptogenic stroke in which an embolic source was thought to be most likely, despite an appropriate diagnostic evaluation with negative findings (Fig 1). 2 This term (along with "cryptogenic stroke") has also been used to define patients who may have multiple, competing stroke etiologies in which a definitive source cannot be determined. Patients with ESUS have been estimated to have a .4% risk of recurrent stroke per annum despite being on antiplatelet medication. 1 Subsequent thought was directed to the idea that anticoagulation may be beneficial in such patients, given the likelihood that emboli originated from various nonevident atheroembolic sources. 3 This premise provided the impetus for 2 large clinical trials that compared anticoagulation with aspirin alone in patients with ESUS. 4,5 Despite this plausible theory, no benefit of anticoagulation was observed. These results spurred continued interest in additional

“…13,14 While the degree of luminal stenosis remains a critical feature, advances in US technology have likely led to identification of other US plaque features that require consideration as a culprit lesion in patients with stroke. 15,16 Unfortunately, a paucity of data specific to patients with ESUS exists.…”

Section: Ultrasoundmentioning

confidence: 99%

“…Technical advances in CT technology (ie, such as high-speed multidetector hardware and 3D reformatting software) have enabled detection of carotid artery disease on the scale of millimeters. 16 Because CTA is performed as a part of most institutional acute stroke algorithms, any features of nonstenotic plaques associated with ipsilateral stroke would be easily assessed in the clinical setting. Thus, CTA may, therefore, be the most practical technique to assess nonstenotic plaque features.…”

Section: Ctamentioning

confidence: 99%

Nonstenotic Carotid Plaques and Embolic Stroke of Undetermined Source: A Multimodality Review

et al. 2022

AJNR Am J Neuroradiol

View full text Add to dashboard Cite

Symptomatic nonstenotic carotid artery disease has been increasingly recognized as a thromboembolic source in patients who would otherwise be classified as having embolic stroke of undetermined source. Evidence suggests that certain plaque features seen on sonography, CT, and MR imaging in nonstenotic carotid artery disease may predispose to recurrent stroke in patients with embolic stroke of undetermined source. We performed a focused literature review to further study plaque features in the context of embolic stroke of undetermined source and to determine which plaque features may be associated with ipsilateral ischemic events in such patients. Plaque thickness as seen on both ultrasound and CT appears to have a consistent association with ipsilateral stroke in patients with embolic stroke of undetermined source across multiple studies. Intraplaque hemorrhage as seen on MR imaging is now understood to have a strong association with ipsilateral stroke in patients with embolic stroke of undetermined source. Continued study of various plaque features as seen on different modalities is warranted to uncover other potential associations.ABBREVIATIONS: ESUS ¼ embolic stroke of undetermined source; IPH ¼ intraplaque hemorrhage; LRNC ¼ lipid-rich necrotic core; SyNC ¼ symptomatic nonstenotic carotid artery disease; US ¼ ultrasound U p to one-third of strokes have no established mechanism and are considered to be cryptogenic. 1 In 2014, the term "embolic stroke of undetermined source" (ESUS) was established as a clinical entity in patients with nonlacunar cryptogenic stroke in which an embolic source was thought to be most likely, despite an appropriate diagnostic evaluation with negative findings (Fig 1). 2 This term (along with "cryptogenic stroke") has also been used to define patients who may have multiple, competing stroke etiologies in which a definitive source cannot be determined. Patients with ESUS have been estimated to have a .4% risk of recurrent stroke per annum despite being on antiplatelet medication. 1 Subsequent thought was directed to the idea that anticoagulation may be beneficial in such patients, given the likelihood that emboli originated from various nonevident atheroembolic sources. 3 This premise provided the impetus for 2 large clinical trials that compared anticoagulation with aspirin alone in patients with ESUS. 4,5 Despite this plausible theory, no benefit of anticoagulation was observed. These results spurred continued interest in additional

“…The positive economic effect is a critical decision element in determining whether to invest in an AI solution in the healthcare business [ 51 ]. The healthcare provider and insurance businesses, in addition to the medical and pharmaceutical technology sectors, are significantly affected [ 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 ]. However, the broad economic impact of digital health solutions, in general, has been extensively studied in the presented paper.…”

Section: Introductionmentioning

confidence: 99%

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment

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³

et al. 2022

Self Cite

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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.

“…A major cause of stroke and CVD is atherosclerosis disease of the arteries [ 12 , 13 ], particularly in the intimal and media walls [ 14 ]. Different stages of plaque constitute soft plaque, such as lipid and macrophages, and hard plaque, such as calcium [ 15 ], or so-called plaque morphology, which is helpful in the diagnosis of atherosclerosis disease severity [ 16 , 17 ].…”

Section: Introductionmentioning

confidence: 99%

Attention-Based UNet Deep Learning Model for Plaque Segmentation in Carotid Ultrasound for Stroke Risk Stratification: An Artificial Intelligence Paradigm

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²

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³

et al. 2022

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Stroke and cardiovascular diseases (CVD) significantly affect the world population. The early detection of such events may prevent the burden of death and costly surgery. Conventional methods are neither automated nor clinically accurate. Artificial Intelligence-based methods of automatically detecting and predicting the severity of CVD and stroke in their early stages are of prime importance. This study proposes an attention-channel-based UNet deep learning (DL) model that identifies the carotid plaques in the internal carotid artery (ICA) and common carotid artery (CCA) images. Our experiments consist of 970 ICA images from the UK, 379 CCA images from diabetic Japanese patients, and 300 CCA images from post-menopausal women from Hong Kong. We combined both CCA images to form an integrated database of 679 images. A rotation transformation technique was applied to 679 CCA images, doubling the database for the experiments. The cross-validation K5 (80% training: 20% testing) protocol was applied for accuracy determination. The results of the Attention-UNet model are benchmarked against UNet, UNet++, and UNet3P models. Visual plaque segmentation showed improvement in the Attention-UNet results compared to the other three models. The correlation coefficient (CC) value for Attention-UNet is 0.96, compared to 0.93, 0.96, and 0.92 for UNet, UNet++, and UNet3P models. Similarly, the AUC value for Attention-UNet is 0.97, compared to 0.964, 0.966, and 0.965 for other models. Conclusively, the Attention-UNet model is beneficial in segmenting very bright and fuzzy plaque images that are hard to diagnose using other methods. Further, we present a multi-ethnic, multi-center, racial bias-free study of stroke risk assessment.

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