Machine learning augmented echocardiography for diastolic function assessment
<jats:p>Cardiac diastolic dysfunction is prevalent and is a diagnostic criterion for heart failure with preserved ejection fraction—a burgeoning global health issue. As gold-standard invasive haemodynamic assessment of diastolic function is not routinely performed, clinical...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
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Frontiers Media
2021
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| _version_ | 1826302079302369280 |
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| author | Fletcher, A Leeson, P Lapidaire, W |
| author_facet | Fletcher, A Leeson, P Lapidaire, W |
| author_sort | Fletcher, A |
| collection | OXFORD |
| description | <jats:p>Cardiac diastolic dysfunction is prevalent and is a diagnostic criterion for heart failure with preserved ejection fraction—a burgeoning global health issue. As gold-standard invasive haemodynamic assessment of diastolic function is not routinely performed, clinical guidelines advise using echocardiography measures to determine the grade of diastolic function. However, the current process has suboptimal accuracy, regular indeterminate classifications and is susceptible to confounding from comorbidities. Advances in artificial intelligence in recent years have created revolutionary ways to evaluate and integrate large quantities of cardiology data. Imaging is an area of particular strength for the sub-field of machine-learning, with evidence that trained algorithms can accurately discern cardiac structures, reliably estimate chamber volumes, and output systolic function metrics from echocardiographic images. In this review, we present the emerging field of machine-learning based echocardiographic diastolic function assessment. We summarise how machine-learning has made use of diastolic parameters to accurately differentiate pathology, to identify novel phenotypes within diastolic disease, and to grade diastolic function. Perspectives are given about how these innovations could be used to augment clinical practice, whilst areas for future investigation are identified.</jats:p> |
| first_indexed | 2024-03-07T05:42:04Z |
| format | Journal article |
| id | oxford-uuid:e5ea7f5a-cb09-4733-9747-344193f7294f |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T05:42:04Z |
| publishDate | 2021 |
| publisher | Frontiers Media |
| record_format | dspace |
| spelling | oxford-uuid:e5ea7f5a-cb09-4733-9747-344193f7294f2022-03-27T10:27:30ZMachine learning augmented echocardiography for diastolic function assessmentJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:e5ea7f5a-cb09-4733-9747-344193f7294fEnglishSymplectic ElementsFrontiers Media2021Fletcher, ALeeson, PLapidaire, W<jats:p>Cardiac diastolic dysfunction is prevalent and is a diagnostic criterion for heart failure with preserved ejection fraction—a burgeoning global health issue. As gold-standard invasive haemodynamic assessment of diastolic function is not routinely performed, clinical guidelines advise using echocardiography measures to determine the grade of diastolic function. However, the current process has suboptimal accuracy, regular indeterminate classifications and is susceptible to confounding from comorbidities. Advances in artificial intelligence in recent years have created revolutionary ways to evaluate and integrate large quantities of cardiology data. Imaging is an area of particular strength for the sub-field of machine-learning, with evidence that trained algorithms can accurately discern cardiac structures, reliably estimate chamber volumes, and output systolic function metrics from echocardiographic images. In this review, we present the emerging field of machine-learning based echocardiographic diastolic function assessment. We summarise how machine-learning has made use of diastolic parameters to accurately differentiate pathology, to identify novel phenotypes within diastolic disease, and to grade diastolic function. Perspectives are given about how these innovations could be used to augment clinical practice, whilst areas for future investigation are identified.</jats:p> |
| spellingShingle | Fletcher, A Leeson, P Lapidaire, W Machine learning augmented echocardiography for diastolic function assessment |
| title | Machine learning augmented echocardiography for diastolic function assessment |
| title_full | Machine learning augmented echocardiography for diastolic function assessment |
| title_fullStr | Machine learning augmented echocardiography for diastolic function assessment |
| title_full_unstemmed | Machine learning augmented echocardiography for diastolic function assessment |
| title_short | Machine learning augmented echocardiography for diastolic function assessment |
| title_sort | machine learning augmented echocardiography for diastolic function assessment |
| work_keys_str_mv | AT fletchera machinelearningaugmentedechocardiographyfordiastolicfunctionassessment AT leesonp machinelearningaugmentedechocardiographyfordiastolicfunctionassessment AT lapidairew machinelearningaugmentedechocardiographyfordiastolicfunctionassessment |