Putting machine learning into motion: applications in cardiovascular imaging

O’Regan, Declan P

doi:10.1016/j.crad.2019.04.008

Cited by 20 publications

(14 citation statements)

References 35 publications

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“…Machine learning (ML) uses algorithms to recognise patterns in example data to make predictive decisions in unprecedented data [86]. ML can classify the data based on the differentiating patterns it has learnt [87,88].…”

Section: Machine Learningmentioning

confidence: 99%

Cardiac Magnetic Resonance in Pulmonary Hypertension—an Update

Alabed

Garg

Johns

et al. 2020

Curr Cardiovasc Imaging Rep

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Purpose of Review This article reviews advances over the past 3 years in cardiac magnetic resonance (CMR) imaging in pulmonary hypertension (PH). We aim to bring the reader up-to-date with CMR applications in diagnosis, prognosis, 4D flow, strain analysis, T1 mapping, machine learning and ongoing research. Recent Findings CMR volumetric and functional metrics are now established as valuable prognostic markers in PH. This imaging modality is increasingly used to assess treatment response and improves risk stratification when incorporated into PH risk scores. Emerging techniques such as myocardial T1 mapping may play a role in the follow-up of selected patients. Myocardial strain may be used as an early marker for right and left ventricular dysfunction and a predictor for mortality. Machine learning has offered a glimpse into future possibilities. Ongoing research of new PH therapies is increasingly using CMR as a clinical endpoint. Summary The last 3 years have seen several large studies establishing CMR as a valuable diagnostic and prognostic tool in patients with PH, with CMR increasingly considered as an endpoint in clinical trials of PH therapies. Machine learning approaches to improve automation and accuracy of CMR metrics and identify imaging features of PH is an area of active research interest with promising clinical utility.

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Section: Machine Learningmentioning

confidence: 99%

Cardiac Magnetic Resonance in Pulmonary Hypertension—an Update

Alabed

Garg

Johns

et al. 2020

Curr Cardiovasc Imaging Rep

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“…Recent years have seen an increase in the volume of artificial intelligence (AI) research in the field of cancer imaging, prompting calls for appropriately rigorous design and appraisal standards [1][2][3][4][5][6]. Evaluation of AI research requires a skillset which is distinct from those of classical medical statistics and epidemiology.…”

Section: Introductionmentioning

confidence: 99%

Systematic review of research design and reporting of imaging studies applying convolutional neural networks for radiological cancer diagnosis

2021

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Objectives To perform a systematic review of design and reporting of imaging studies applying convolutional neural network models for radiological cancer diagnosis. Methods A comprehensive search of PUBMED, EMBASE, MEDLINE and SCOPUS was performed for published studies applying convolutional neural network models to radiological cancer diagnosis from January 1, 2016, to August 1, 2020. Two independent reviewers measured compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Compliance was defined as the proportion of applicable CLAIM items satisfied. Results One hundred eighty-six of 655 screened studies were included. Many studies did not meet the criteria for current design and reporting guidelines. Twenty-seven percent of studies documented eligibility criteria for their data (50/186, 95% CI 21–34%), 31% reported demographics for their study population (58/186, 95% CI 25–39%) and 49% of studies assessed model performance on test data partitions (91/186, 95% CI 42–57%). Median CLAIM compliance was 0.40 (IQR 0.33–0.49). Compliance correlated positively with publication year (ρ = 0.15, p = .04) and journal H-index (ρ = 0.27, p < .001). Clinical journals demonstrated higher mean compliance than technical journals (0.44 vs. 0.37, p < .001). Conclusions Our findings highlight opportunities for improved design and reporting of convolutional neural network research for radiological cancer diagnosis. Key Points • Imaging studies applying convolutional neural networks (CNNs) for cancer diagnosis frequently omit key clinical information including eligibility criteria and population demographics. • Fewer than half of imaging studies assessed model performance on explicitly unobserved test data partitions. • Design and reporting standards have improved in CNN research for radiological cancer diagnosis, though many opportunities remain for further progress.

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“…The application of machine learning in medical imaging can be briefly summarized into three types: supervised, unsupervised, and semisupervised learning (31). Supervised learning intends to recognize the relationship between characteristics relevant to the learning objectives and to cardiovascular imaging are supervised and unsupervised learning (32).…”

Section: Machine Learningmentioning

confidence: 99%

Magnetic Resonance Texture Analysis in Myocardial Infarction

Peng

Zheng

Tang

et al. 2021

Front. Cardiovasc. Med.

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Texture analysis (TA) is a newly arisen field that can detect the invisible MRI signal changes among image pixels. Myocardial infarction (MI) is cardiomyocyte necrosis caused by myocardial ischemia and hypoxia, becoming the primary cause of death and disability worldwide. In recent years, various TA studies have been performed in patients with MI and show a good clinical application prospect. This review briefly presents the main pathogenesis and pathophysiology of MI, introduces the overview and workflow of TA, and summarizes multiple magnetic resonance TA (MRTA) clinical applications in MI. We also discuss the facing challenges currently for clinical utilization and propose the prospect.

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Putting machine learning into motion: applications in cardiovascular imaging

Cited by 20 publications

References 35 publications

Cardiac Magnetic Resonance in Pulmonary Hypertension—an Update

Cardiac Magnetic Resonance in Pulmonary Hypertension—an Update

Systematic review of research design and reporting of imaging studies applying convolutional neural networks for radiological cancer diagnosis

Magnetic Resonance Texture Analysis in Myocardial Infarction

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