Fully Automated Artificial Intelligence Assessment of Aortic Stenosis by Echocardiography
Published by Elsevier Inc..
BACKGROUND: Aortic stenosis (AS) is a common form of valvular heart disease, present in over 12% of the population age 75 years and above. Transthoracic echocardiography (TTE) is the first line of imaging in the adjudication of AS severity but is time-consuming and requires expert sonographic and interpretation capabilities to yield accurate results. Artificial intelligence (AI) technology has emerged as a useful tool to address these limitations but has not yet been applied in a fully hands-off manner to evaluate AS. Here, we correlate artificial neural network measurements of key hemodynamic AS parameters to experienced human reader assessment.
METHODS: Two-dimensional and Doppler echocardiographic images from patients with normal aortic valves and all degrees of AS were analyzed by an artificial neural network (Us2.ai) with no human input to measure key variables in AS assessment. Trained echocardiographers blinded to AI data performed manual measurements of these variables, and correlation analyses were performed.
RESULTS: Our cohort included 256 patients with an average age of 67.6 ± 9.5 years. Across all AS severities, AI closely matched human measurement of aortic valve peak velocity (r = 0.97, P < .001), mean pressure gradient (r = 0.94, P < .001), aortic valve area by continuity equation (r = 0.88, P < .001), stroke volume index (r = 0.79, P < .001), left ventricular outflow tract velocity-time integral (r = 0.89, P < .001), aortic valve velocity-time integral (r = 0.96, P < .001), and left ventricular outflow tract diameter (r = 0.76, P < .001).
CONCLUSIONS: Artificial neural networks have the capacity to closely mimic human measurement of all relevant parameters in the adjudication of AS severity. Application of this AI technology may minimize interscan variability, improve interpretation and diagnosis of AS, and allow for precise and reproducible identification and management of patients with AS.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2023 |
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| Erschienen: |
2023 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:36 |
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| Enthalten in: |
Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography - 36(2023), 7 vom: 14. Juli, Seite 769-777 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Krishna, Hema [VerfasserIn] |
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| Links: |
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| Themen: |
Aortic stenosis |
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| Anmerkungen: |
Date Completed 10.07.2023 Date Revised 15.04.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.echo.2023.03.008 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM35463867X |
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| 245 | 1 | 0 | |a Fully Automated Artificial Intelligence Assessment of Aortic Stenosis by Echocardiography |
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| 500 | |a Date Revised 15.04.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Published by Elsevier Inc. | ||
| 520 | |a BACKGROUND: Aortic stenosis (AS) is a common form of valvular heart disease, present in over 12% of the population age 75 years and above. Transthoracic echocardiography (TTE) is the first line of imaging in the adjudication of AS severity but is time-consuming and requires expert sonographic and interpretation capabilities to yield accurate results. Artificial intelligence (AI) technology has emerged as a useful tool to address these limitations but has not yet been applied in a fully hands-off manner to evaluate AS. Here, we correlate artificial neural network measurements of key hemodynamic AS parameters to experienced human reader assessment | ||
| 520 | |a METHODS: Two-dimensional and Doppler echocardiographic images from patients with normal aortic valves and all degrees of AS were analyzed by an artificial neural network (Us2.ai) with no human input to measure key variables in AS assessment. Trained echocardiographers blinded to AI data performed manual measurements of these variables, and correlation analyses were performed | ||
| 520 | |a RESULTS: Our cohort included 256 patients with an average age of 67.6 ± 9.5 years. Across all AS severities, AI closely matched human measurement of aortic valve peak velocity (r = 0.97, P < .001), mean pressure gradient (r = 0.94, P < .001), aortic valve area by continuity equation (r = 0.88, P < .001), stroke volume index (r = 0.79, P < .001), left ventricular outflow tract velocity-time integral (r = 0.89, P < .001), aortic valve velocity-time integral (r = 0.96, P < .001), and left ventricular outflow tract diameter (r = 0.76, P < .001) | ||
| 520 | |a CONCLUSIONS: Artificial neural networks have the capacity to closely mimic human measurement of all relevant parameters in the adjudication of AS severity. Application of this AI technology may minimize interscan variability, improve interpretation and diagnosis of AS, and allow for precise and reproducible identification and management of patients with AS | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Aortic stenosis | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Doppler | |
| 650 | 4 | |a Echocardiography | |
| 650 | 4 | |a Machine learning | |
| 700 | 1 | |a Desai, Kevin |e verfasserin |4 aut | |
| 700 | 1 | |a Slostad, Brody |e verfasserin |4 aut | |
| 700 | 1 | |a Bhayani, Siddharth |e verfasserin |4 aut | |
| 700 | 1 | |a Arnold, Joshua H |e verfasserin |4 aut | |
| 700 | 1 | |a Ouwerkerk, Wouter |e verfasserin |4 aut | |
| 700 | 1 | |a Hummel, Yoran |e verfasserin |4 aut | |
| 700 | 1 | |a Lam, Carolyn S P |e verfasserin |4 aut | |
| 700 | 1 | |a Ezekowitz, Justin |e verfasserin |4 aut | |
| 700 | 1 | |a Frost, Matthew |e verfasserin |4 aut | |
| 700 | 1 | |a Jiang, Zhubo |e verfasserin |4 aut | |
| 700 | 1 | |a Equilbec, Cyril |e verfasserin |4 aut | |
| 700 | 1 | |a Twing, Aamir |e verfasserin |4 aut | |
| 700 | 1 | |a Pellikka, Patricia A |e verfasserin |4 aut | |
| 700 | 1 | |a Frazin, Leon |e verfasserin |4 aut | |
| 700 | 1 | |a Kansal, Mayank |e verfasserin |4 aut | |
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