Addressing bias: artificial intelligence in cardiovascular medicine

Tat, Emily; Bhatt, Deepak L.; Rabbat, Mark

doi:10.1016/s2589-7500(20)30249-1

Cited by 63 publications

(46 citation statements)

References 10 publications

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“…Each AI-enabled software requires rigorous validation prior to clinical implementation. There are concerns about unforeseen biases associated with socioeconomic and racial backgrounds of populations (29). Also, at present, some quantitative plaque analysis tools are still time-consuming, and this limits their application in clinical practice.…”

Section: Clinical Perspectivementioning

confidence: 99%

Machine learning-based advances in coronary computed tomography angiography

Benjamin¹,

Rabbat²

2021

Quant Imaging Med Surg

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Coronary computed tomography angiography (CCTA) is an effective imaging modality increasingly accepted as a firstline test to diagnose coronary artery disease (CAD). CCTA stands out among other diagnostic modalities with an ability to image various stages of atherosclerosis, including plaque progression and rupture. Plaque burden and characteristics by CCTA have been shown to have prognostic implications for patient management (1). Also, detecting early subclinical CAD may allow for interventions aimed at preventing the progression of coronary plaque and reducing coronary events (2). The aim of this article is to discuss the innovative tools derived from CCTA using artificial intelligence (AI) which would aid in risk stratification and medical decision making for patients with CAD. For many observers, machine learning is treated as a "black box" in which data are analyzed and results produced, without attempting to understand how or why. This has the potential to cause significant issues if systematic biases are introduced and not recognized. Automated plaque characterization software and detecting culprit lesionsAI has been used to develop deep convolutional neural networks (CNN) to classify CCTA in the correct Coronary Artery Disease Reporting and Data System (CAD-RADS) category and this was shown to be accurate and less timeconsuming (3). Some of the emerging technologies which use histologically validated, application-based tissue quantification to characterize atherosclerosis include the commercial software applications vascuCAP (Elucid Bioimaging, Boston, Massachusetts, USA) and Sureplaque TM (V7.5; Vital Images, MN, USA).Sureplaque TM software: SUREPlaque software (V7.5; Vital Images, MN, USA) uses color defined Hounsfield unit (HU) ranges to define plaque characteristics (Figure 1). The software is based on curved multiplanar reconstructions. It has shown good correlation to the morphometric parameters of atheroma, but fair correlation with regard to the relative plaque composition (4-6).vascuCAP TM software: model-based quantification algorithms, as used by vascuCAP, aim to reduce interscan and interobserver variability and allow for detailed characterization of morphological features including positive remodeling, lipid-rich necrotic core, and coronary artery plaque burden (7,8). van Assen et al. retrospectively studied 45 patients with suspected CAD of which 16 (36%) experienced major adverse cardiac events within 12 months. The software was used to evaluate lumen area, wall area, stenosis percentage, wall thickness, plaque burden, remodeling ratio, calcified area, lipid rich necrotic core area and matrix area. Regression analysis using clinical risk factors resulted in a prognostic accuracy of 63% with a corresponding area under the curve (AUC) of 0.587. The use of morphologic features alone resulted in an increased accuracy of 77% with an AUC of 0.94. Combining both clinical risk factors and morphological features in a multivariate logistic regression analysis increased the accuracy to 87% with a sim...

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Section: Clinical Perspectivementioning

confidence: 99%

Machine learning-based advances in coronary computed tomography angiography

Benjamin¹,

Rabbat²

2021

Quant Imaging Med Surg

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“…To spread the application of AI models in real-world clinical practice, however, some potential limitations, pitfalls and ethical considerations need to be considered ( 102 ).…”

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence Based Multimodality Imaging: A New Frontier in Coronary Artery Disease Management

Maragna

Giacari

Guglielmo

et al. 2021

Front. Cardiovasc. Med.

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Coronary artery disease (CAD) represents one of the most important causes of death around the world. Multimodality imaging plays a fundamental role in both diagnosis and risk stratification of acute and chronic CAD. For example, the role of Coronary Computed Tomography Angiography (CCTA) has become increasingly important to rule out CAD according to the latest guidelines. These changes and others will likely increase the request for appropriate imaging tests in the future. In this setting, artificial intelligence (AI) will play a pivotal role in echocardiography, CCTA, cardiac magnetic resonance and nuclear imaging, making multimodality imaging more efficient and reliable for clinicians, as well as more sustainable for healthcare systems. Furthermore, AI can assist clinicians in identifying early predictors of adverse outcome that human eyes cannot see in the fog of “big data.” AI algorithms applied to multimodality imaging will play a fundamental role in the management of patients with suspected or established CAD. This study aims to provide a comprehensive overview of current and future AI applications to the field of multimodality imaging of ischemic heart disease.

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“…An example of such AI has been demonstrated in STEMI care, where a standardized approach reduced the disparities in care of women and improving outcomes when presenting with STEMI 81 . While the use of AI has the potential to improve cardiovascular outcomes by removing bias, it is no panacea for racism in medicine; the data entered into the AI systems must be clear of bias and the clinicians supervising/overseeing the process must not contribute their bias to the algorithms 82 .…”

Section: Time To Intervene: Reducing the Impact Of Racism And Racial Bias In Cardiovascular Carementioning

confidence: 99%

“…Although the use of AI has the potential to improve cardiovascular outcomes by removing bias, it is no panacea for racism in medicine; the data entered into the AI systems must be clear of bias, and the clinicians supervising/overseeing the process must not contribute their bias to the algorithms. 81 …”

Section: Time To Intervene: Reducing the Impact Of Racism And Racial Bias In Cardiovascular Carementioning

confidence: 99%

Racism and Cardiology: A Global Call to Action

et al. 2021

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This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

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Addressing bias: artificial intelligence in cardiovascular medicine

Cited by 63 publications

References 10 publications

Machine learning-based advances in coronary computed tomography angiography

Machine learning-based advances in coronary computed tomography angiography

Artificial Intelligence Based Multimodality Imaging: A New Frontier in Coronary Artery Disease Management

Racism and Cardiology: A Global Call to Action

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