AI Based CMR Assessment of Biventricular Function

Wang, Shuo; Patel, Hena; Miller, Tamari; Ameyaw, Keith; Narang, Akhil; Chauhan, Daksh; Anand, Simran; Anyanwu, Emeka; Besser, Stephanie; Kawaji, Keigo; Liu, Xingpeng; Lang, Roberto M.; Mor‐Avi, Victor; Patel, Amit R.

doi:10.1016/j.jcmg.2021.08.011

Cited by 20 publications

(12 citation statements)

References 44 publications

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“…However, the limits of agreement between manual assessment and our AI tool for cardiac volumes was similar to the inter-observer variability observed in the analysis of a sample of our own data by three independent clinical experts (see Supplementary material online , Table S5 ) and inter-observer variability values reported in the literature. 7 , 17–19 Note that it is to be expected that our method would not surpass these limits of agreement between observers, as our ground truth data was segmented by a range of different experts from different institutions.…”

Section: Discussionmentioning

confidence: 99%

“… 2–6 A major advantage of AI is the automation of CMR analysis, which could unlock large quantities of new data for clinical research and to inform care. Automated AI-based tools now exist for analysis of clinical CMR at the point of acquisition 4 , 7 as well as retrospectively from highly structured and controlled databases (e.g. our previously developed AI-CMR QC tool 2 ).…”

Section: Introductionmentioning

confidence: 99%

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An artificial intelligence tool for automated analysis of large-scale unstructured clinical cine cardiac magnetic resonance databases

Harana

Asher

Vergani

et al. 2023

European Heart Journal - Digital Health

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Introduction Artificial intelligence (AI) techniques have been proposed for automating analysis of short axis (SAX) cine cardiac magnetic resonance (CMR), but no CMR analysis tool exists to automatically analyse large (unstructured) clinical CMR datasets. We develop and validate a robust AI tool for start-to-end automatic quantification of cardiac function from SAX cine CMR in large clinical databases. Methods Our pipeline for processing and analysing CMR databases includes automated steps to identify the correct data, robust image pre-processing, an AI algorithm for biventricular segmentation of SAX CMR and estimation of functional biomarkers, and automated post-analysis quality control to detect and correct errors. The segmentation algorithm was trained on 2793 CMR scans from two NHS hospitals and validated on additional cases from this dataset (n = 414) and five external datasets (n = 6888), including scans of patients with a range of diseases acquired at 12 different centres using CMR scanners from all major vendors. Results Median absolute errors in cardiac biomarkers were within the range of inter-observer variability: < 8.4 mL (left ventricle volume), < 9.2 mL (right ventricle volume), < 13.3 g (left ventricular mass), and <5.9% (ejection fraction) across all datasets. Stratification of cases according to phenotypes of cardiac disease and scanner vendors showed good performance across all groups. Conclusions We show that our proposed tool, which combines image pre-processing steps, a domain-generalisable AI algorithm trained on a large-scale multi-domain CMR dataset and quality control steps, allows robust analysis of (clinical or research) databases from multiple centres, vendors, and cardiac diseases. This enables translation of our tool for use in fully-automated processing of large multi-centre databases.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An artificial intelligence tool for automated analysis of large-scale unstructured clinical cine cardiac magnetic resonance databases

Harana

Asher

Vergani

et al. 2023

European Heart Journal - Digital Health

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“…17 In MRI, AI tools can detect and mitigate motion artifacts to improve image quality 18 ; accelerate acquisitions, leading to overall shorter examination times 19 ; and automate contouring for evaluation of ventricular volumes and ejection fraction. 20…”

Section: Imagermentioning

confidence: 99%

Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association

Hanneman,

Playford,

Dey

et al. 2024

Circulation

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Multiple applications for machine learning and artificial intelligence (AI) in cardiovascular imaging are being proposed and developed. However, the processes involved in implementing AI in cardiovascular imaging are highly diverse, varying by imaging modality, patient subtype, features to be extracted and analyzed, and clinical application. This article establishes a framework that defines value from an organizational perspective, followed by value chain analysis to identify the activities in which AI might produce the greatest incremental value creation. The various perspectives that should be considered are highlighted, including clinicians, imagers, hospitals, patients, and payers. Integrating the perspectives of all health care stakeholders is critical for creating value and ensuring the successful deployment of AI tools in a real-world setting. Different AI tools are summarized, along with the unique aspects of AI applications to various cardiac imaging modalities, including cardiac computed tomography, magnetic resonance imaging, and positron emission tomography. AI is applicable and has the potential to add value to cardiovascular imaging at every step along the patient journey, from selecting the more appropriate test to optimizing image acquisition and analysis, interpreting the results for classification and diagnosis, and predicting the risk for major adverse cardiac events.

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“…Arterys (Arterys, San Francisco, CA, USA) uses DL and cloud computing, which is storage space available over the Internet, for the automatic analysis of cardiac MRI images. Time-consuming routinely performed analysis can be accelerated and automated [ 19 ]. In addition, advanced methods, such as quantifying the entire delayed enhancement in the left ventricle, enable a more detailed observation and analysis of cardiac MRI sequences.…”

Section: Artificial Intelligence In Cardiovascular Medicinementioning

confidence: 99%

The Heart and Artificial Intelligence—How Can We Improve Medicine Without Causing Harm

Reich

Meder

2023

Curr Heart Fail Rep

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Purpose of Review The introduction of Artificial Intelligence into the healthcare system offers enormous opportunities for biomedical research, the improvement of patient care, and cost reduction in high-end medicine. Digital concepts and workflows are already playing an increasingly important role in cardiology. The fusion of computer science and medicine offers great transformative potential and enables enormous acceleration processes in cardiovascular medicine. Recent Findings As medical data becomes smart, it is also becoming more valuable and vulnerable to malicious actors. In addition, the gap between what is technically possible and what is allowed by privacy legislation is growing. Principles of the General Data Protection Regulation that have been in force since May 2018, such as transparency, purpose limitation, and data minimization, seem to hinder the development and use of Artificial Intelligence. Summary Concepts to secure data integrity and incorporate legal and ethical principles can help to avoid the potential risks of digitization and may result in an European leadership in regard to privacy protection and AI. The following review provides an overview of relevant aspects of Artificial Intelligence and Machine Learning, highlights selected applications in cardiology, and discusses central ethical and legal considerations.

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AI Based CMR Assessment of Biventricular Function

Cited by 20 publications

References 44 publications

An artificial intelligence tool for automated analysis of large-scale unstructured clinical cine cardiac magnetic resonance databases

An artificial intelligence tool for automated analysis of large-scale unstructured clinical cine cardiac magnetic resonance databases

Value Creation Through Artificial Intelligence and Cardiovascular Imaging: A Scientific Statement From the American Heart Association

The Heart and Artificial Intelligence—How Can We Improve Medicine Without Causing Harm

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