A primer on artificial intelligence for the paediatric cardiologist

Gearhart, Addison; Gaffar, Sharib; Chang, Anthony

doi:10.1017/s1047951120001493

Cited by 14 publications

(13 citation statements)

References 61 publications

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“…Neural networks have been widely used in cardiovascular disease to quantify the complex relationships between the given data and have shown superior comparative ability in many areas such as diagnosis, response to treatment, and prognosis for patients suffering from various diseases [ 13 ]. Gearhart et al used neural networks and cardiopulmonary function tests to compare the risk of cardiovascular-related mortality in patients who had heart failure with a left cardiopulmonary function test were followed up, and the results of the obtained tests and patient survival were composed into a sample set, which was randomly divided into a training set and a test set to compare cardiovascular-related mortality [ 14 ]. Olier et al applied a cardiac hemodynamic monitor to measure left atrial volume measurements and pulmonary vein parameters by the cardiac impedance method in patients with coronary artery disease at different times to observe the effects of coronary artery disease on patients' left atrial volume measurements and pulmonary veins to further guide clinical diagnosis [ 15 ].…”

Section: Related Workmentioning

confidence: 99%

Artificial Intelligence-Based Echocardiographic Left Atrial Volume Measurement with Pulmonary Vein Comparison

Zhu

Fan

Liu

et al. 2021

Journal of Healthcare Engineering

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This paper combines echocardiographic signal processing and artificial intelligence technology to propose a deep neural network model adapted to echocardiographic signals to achieve left atrial volume measurement and automatic assessment of pulmonary veins efficiently and quickly. Based on the echocardiographic signal generation mechanism and detection method, an experimental scheme for the echocardiographic signal acquisition was designed. The echocardiographic signal data of healthy subjects were measured in four different experimental states, and a database of left atrial volume measurements and pulmonary veins was constructed. Combining the correspondence between ECG signals and echocardiographic signals in the time domain, a series of preprocessing such as denoising, feature point localization, and segmentation of the cardiac cycle was realized by wavelet transform and threshold method to complete the data collection. This paper proposes a comparative model based on artificial intelligence, adapts to the characteristics of one-dimensional time-series echocardiographic signals, automatically extracts the deep features of echocardiographic signals, effectively reduces the subjective influence of manual feature selection, and realizes the automatic classification and evaluation of human left atrial volume measurement and pulmonary veins under different states. The experimental results show that the proposed BP neural network model has good adaptability and classification performance in the tasks of LV volume measurement and pulmonary vein automatic classification evaluation and achieves an average test accuracy of over 96.58%. The average root-mean-square error percentage of signal compression is only 0.65% by extracting the coding features of the original echocardiographic signal through the convolutional autoencoder, which completes the signal compression with low loss. Comparing the training time and classification accuracy of the LSTM network with the original signal and encoded features, the experimental results show that the AI model can greatly reduce the model training time cost and achieve an average accuracy of 97.97% in the test set and increase the real-time performance of the left atrial volume measurement and pulmonary vein evaluation as well as the security of the data transmission process, which is very important for the comparison of left atrial volume measurement and pulmonary vein. It is of great practical importance to compare left atrial volume measurements with pulmonary veins.

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Section: Related Workmentioning

confidence: 99%

Artificial Intelligence-Based Echocardiographic Left Atrial Volume Measurement with Pulmonary Vein Comparison

Zhu

Fan

Liu

et al. 2021

Journal of Healthcare Engineering

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“…65,66 This issue is directly associated with how AI and its data are monetized, 66 as there are controversies about who should profit from the collected data and for how long these institutions or individuals can and should retain patient health information. 67…”

Section: Resultsmentioning

confidence: 99%

Barriers and Facilitators to Trustworthy and Ethical AI-enabled Medical Care From Patient’s and Healthcare Provider’s Perspectives: A Literature Review

Mooghali,

Stroud,

Yoo

et al. 2023

Preprint

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BackgroundArtificial intelligence (AI) and machine learning (ML) are increasingly used for prevention, diagnosis, monitoring, and treatment of cardiovascular diseases. Despite the potential for AI/ML to improve care, ethical concerns and mistrust in AI-enabled health care exist among the public and medical community. To inform practice guidelines and regulatory policies that facilitate ethical and trustworthy use of AI in medicine, we conducted a literature review to identify key ethical and trust barriers and facilitators from patients’ and healthcare providers’ perspectives when using AI in cardiovascular care.MethodsIn this rapid literature review, we searched six bibliographic databases to identify publications discussing transparency, trust, or ethical concerns (outcomes of interest) associated with AI/ML-based medical devices (interventions of interest) in the context of cardiovascular care from patients’, caregivers’, or healthcare providers’ perspectives. The search was completed on May 24, 2022 and was not limited by date or study design.ResultsAfter reviewing 7,925 papers from six databases and 3,603 papers identified through citation chasing, 145 articles were included. Key ethical concerns included privacy, security, or confidentiality issues; risk of healthcare inequity or disparity; risk of patient harm; accountability and responsibility concerns; problematic informed consent and potential loss of patient autonomy; and issues related to data ownership. Major trust barriers included data privacy and security concerns, potential risk of patient harm, perceived lack of transparency about AI-enabled medical devices, concerns about AI replacing human aspects of care, concerns about prioritizing profits over patients’ interests, and lack of robust evidence related to the accuracy and limitations of AI-based medical devices. Ethical and trust facilitators included ensuring data privacy and data validation, conducting clinical trials in diverse cohorts, providing appropriate training and resources to patients and healthcare providers and improving their engagement in different phases of AI implementation, and establishing further regulatory oversights.ConclusionThis review revealed key ethical concerns and barriers and facilitators of trust in AI-enabled medical devices from patients’ and healthcare providers’ perspectives. Mitigation strategies, including enhancing regulatory oversight on the use of patient data and promoting AI safety and transparency are needed for effective implementation of AI in cardiovascular care.

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“…The authors of [ 23 ] present a comprehensive review that delves into the history of artificial intelligence in medicine, exploring its contemporary and future applications in adult and pediatric cardiology, with a focus on selected concentrations. The review also addresses the existing barriers to implementing these advanced technologies.…”

Section: State Of the Artmentioning

confidence: 99%

“…Globally, there is a concerted effort to maximize the advantages of artificial intelligence in medicine [ 23 ], aiming to assist physicians in achieving better performance and enhance patients’ experiences during hospitalization. However, we found a gap in the post-diagnosis phase.…”

Section: State Of the Artmentioning

confidence: 99%

AI-Driven Decision Support for Early Detection of Cardiac Events: Unveiling Patterns and Predicting Myocardial Ischemia

Elvas,

Nunes,

Ferreira

et al. 2023

JPM

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Cardiovascular diseases (CVDs) account for a significant portion of global mortality, emphasizing the need for effective strategies. This study focuses on myocardial infarction, pulmonary thromboembolism, and aortic stenosis, aiming to empower medical practitioners with tools for informed decision making and timely interventions. Drawing from data at Hospital Santa Maria, our approach combines exploratory data analysis (EDA) and predictive machine learning (ML) models, guided by the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology. EDA reveals intricate patterns and relationships specific to cardiovascular diseases. ML models achieve accuracies above 80%, providing a 13 min window to predict myocardial ischemia incidents and intervene proactively. This paper presents a Proof of Concept for real-time data and predictive capabilities in enhancing medical strategies.

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A primer on artificial intelligence for the paediatric cardiologist

Cited by 14 publications

References 61 publications

Artificial Intelligence-Based Echocardiographic Left Atrial Volume Measurement with Pulmonary Vein Comparison

Artificial Intelligence-Based Echocardiographic Left Atrial Volume Measurement with Pulmonary Vein Comparison

Barriers and Facilitators to Trustworthy and Ethical AI-enabled Medical Care From Patient’s and Healthcare Provider’s Perspectives: A Literature Review

AI-Driven Decision Support for Early Detection of Cardiac Events: Unveiling Patterns and Predicting Myocardial Ischemia

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