Advancing cardiovascular medicine with machine learning: Progress, potential, and perspective

Barrios, Joshua; Tison, Geoffrey H.

doi:10.1016/j.xcrm.2022.100869

Cited by 4 publications

(2 citation statements)

References 85 publications

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“…AI algorithms provide standardized guidelines and measurements, automating the generation of echocardiography reports [54,55]. Additionally, they identify errors and inconsistencies in interpretations, flagging measurement discrepancies and highlighting abnormalities [56][57][58][59]. This ensures thorough and guidelinealigned interpretations, enhancing research outcomes and evidence-based practice.…”

Section: Benefits and Implicationsmentioning

confidence: 99%

Embracing AI: The Imperative Tool for Echo Labs to Stay Ahead of the Curve

Vasile,

Iriart

2023

Diagnostics

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Advancements in artificial intelligence (AI) have rapidly transformed various sectors, and the field of echocardiography is no exception. AI-driven technologies hold immense potential to revolutionize echo labs’ diagnostic capabilities and improve patient care. This paper explores the importance for echo labs to embrace AI and stay ahead of the curve in harnessing its power. Our manuscript provides an overview of the growing impact of AI on medical imaging, specifically echocardiography. It highlights how AI-driven algorithms can enhance image quality, automate measurements, and accurately diagnose cardiovascular diseases. Additionally, we emphasize the importance of training echo lab professionals in AI implementation to optimize its integration into routine clinical practice. By embracing AI, echo labs can overcome challenges such as workload burden and diagnostic accuracy variability, improving efficiency and patient outcomes. This paper highlights the need for collaboration between echocardiography laboratory experts, AI researchers, and industry stakeholders to drive innovation and establish standardized protocols for implementing AI in echocardiography. In conclusion, this article emphasizes the importance of AI adoption in echocardiography labs, urging practitioners to proactively integrate AI technologies into their workflow and take advantage of their present opportunities. Embracing AI is not just a choice but an imperative for echo labs to maintain their leadership and excel in delivering state-of-the-art cardiac care in the era of advanced medical technologies.

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Section: Benefits and Implicationsmentioning

confidence: 99%

Embracing AI: The Imperative Tool for Echo Labs to Stay Ahead of the Curve

Vasile,

Iriart

2023

Diagnostics

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“…Machine learning (ML) has been successfully employed in medicine to establish and develop accurate models ( 17 ). It outperforms conventional statistical methods by automatically training itself and improving its performance without the need for intricate programming.…”

Section: Introductionmentioning

confidence: 99%

A cost-effective, machine learning-driven approach for screening arterial functional aging in a large-scale Chinese population

Miao,

Dong,

Liu

et al. 2024

Front. Public Health

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IntroductionAn easily accessible and cost-free machine learning model based on prior probabilities of vascular aging enables an application to pinpoint high-risk populations before physical checks and optimize healthcare investment.MethodsA dataset containing questionnaire responses and physical measurement parameters from 77,134 adults was extracted from the electronic records of the Health Management Center at the Third Xiangya Hospital. The least absolute shrinkage and selection operator and recursive feature elimination-Lightweight Gradient Elevator were employed to select features from a pool of potential covariates. The participants were randomly divided into training (70%) and test cohorts (30%). Four machine learning algorithms were applied to build the screening models for elevated arterial stiffness (EAS), and the performance of models was evaluated by calculating the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy.ResultsFourteen easily accessible features were selected to construct the model, including “systolic blood pressure” (SBP), “age,” “waist circumference,” “history of hypertension,” “sex,” “exercise,” “awareness of normal blood pressure,” “eat fruit,” “work intensity,” “drink milk,” “eat bean products,” “smoking,” “alcohol consumption,” and “Irritableness.” The extreme gradient boosting (XGBoost) model outperformed the other three models, achieving AUC values of 0.8722 and 0.8710 in the training and test sets, respectively. The most important five features are SBP, age, waist, history of hypertension, and sex.ConclusionThe XGBoost model ideally assesses the prior probability of the current EAS in the general population. The integration of the model into primary care facilities has the potential to lower medical expenses and enhance the management of arterial aging.

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Innovative Ansätze der Gesundheitsprävention chronischer Erkrankungen am Beispiel der muskuloskelettalen Erkrankungen

Detert,

Detert

2023

Innovationen in Der Gesundheitsversorgung

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Advancing cardiovascular medicine with machine learning: Progress, potential, and perspective

Cited by 4 publications

References 85 publications

Embracing AI: The Imperative Tool for Echo Labs to Stay Ahead of the Curve

Embracing AI: The Imperative Tool for Echo Labs to Stay Ahead of the Curve

A cost-effective, machine learning-driven approach for screening arterial functional aging in a large-scale Chinese population

Innovative Ansätze der Gesundheitsprävention chronischer Erkrankungen am Beispiel der muskuloskelettalen Erkrankungen

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