Validation of American Society of Echocardiography Guideline-Recommended Parameters of Right Ventricular Dysfunction Using Artificial Intelligence Compared With Cardiac Magnetic Resonance Imaging
Copyright © 2023 American Society of Echocardiography. Published by Elsevier Inc. All rights reserved..
BACKGROUND: Right ventricular (RV) function is important in the evaluation of cardiac function, but its assessment using standard transthoracic echocardiography (TTE) remains challenging. Cardiac magnetic resonance imaging (CMR) is considered the gold standard. The American Society of Echocardiography recommends surrogate measures of RV function and RV ejection fraction (RVEF) by TTE, including fractional area change (FAC), free wall strain (FWS), and tricuspid annular planar systolic excursion (TAPSE), but they require technical expertise in acquisition and quantification.
METHODS: The aim of this study was to evaluate the sensitivity, specificity, and positive and negative predictive values of FAC, FWS, and TAPSE derived using a rapid, novel artificial intelligence (AI) software (LVivoRV) from a single-plane transthoracic echocardiographic apical four-chamber, RV-focused view without ultrasound-enhancing agents for detecting abnormal RV function compared with CMR-derived RVEF. RV dysfunction was defined as RVEF < 50% and RVEF < 40% on CMR.
RESULTS: TTE and CMR were performed within a median of 10 days (interquartile range, 2-32 days) of each other in 225 consecutive patients without interval procedural or pharmacologic intervention. The sensitivity and negative predictive value to detect CMR-defined RV dysfunction when all three AI-derived parameters (FAC, FWS, and TAPSE) were abnormal were 91% and 96%, while those of expert physician reads were 91% and 97%. Specificity and positive predictive value were lower (50% and 32%) compared with expert physician-read echocardiograms (82% and 56%).
CONCLUSIONS: AI-derived measurements of FAC, FWS, and TAPSE had excellent sensitivity and negative predictive value for ruling out significant RV dysfunction (CMR RVEF < 40%), comparable with that of expert physician readers, but lower specificity. Thus AI, using American Society of Echocardiography guidelines, may serve as a useful screening tool for rapid bedside assessment to exclude significant RV dysfunction.
| 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), 9 vom: 24. Sept., Seite 967-977 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Hsia, Brian C [VerfasserIn] |
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| Links: |
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| Themen: |
Artificial intelligence |
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| Anmerkungen: |
Date Completed 05.09.2023 Date Revised 07.09.2023 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.echo.2023.05.015 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM358331609 |
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| 100 | 1 | |a Hsia, Brian C |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Validation of American Society of Echocardiography Guideline-Recommended Parameters of Right Ventricular Dysfunction Using Artificial Intelligence Compared With Cardiac Magnetic Resonance Imaging |
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| 500 | |a Date Revised 07.09.2023 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2023 American Society of Echocardiography. Published by Elsevier Inc. All rights reserved. | ||
| 520 | |a BACKGROUND: Right ventricular (RV) function is important in the evaluation of cardiac function, but its assessment using standard transthoracic echocardiography (TTE) remains challenging. Cardiac magnetic resonance imaging (CMR) is considered the gold standard. The American Society of Echocardiography recommends surrogate measures of RV function and RV ejection fraction (RVEF) by TTE, including fractional area change (FAC), free wall strain (FWS), and tricuspid annular planar systolic excursion (TAPSE), but they require technical expertise in acquisition and quantification | ||
| 520 | |a METHODS: The aim of this study was to evaluate the sensitivity, specificity, and positive and negative predictive values of FAC, FWS, and TAPSE derived using a rapid, novel artificial intelligence (AI) software (LVivoRV) from a single-plane transthoracic echocardiographic apical four-chamber, RV-focused view without ultrasound-enhancing agents for detecting abnormal RV function compared with CMR-derived RVEF. RV dysfunction was defined as RVEF < 50% and RVEF < 40% on CMR | ||
| 520 | |a RESULTS: TTE and CMR were performed within a median of 10 days (interquartile range, 2-32 days) of each other in 225 consecutive patients without interval procedural or pharmacologic intervention. The sensitivity and negative predictive value to detect CMR-defined RV dysfunction when all three AI-derived parameters (FAC, FWS, and TAPSE) were abnormal were 91% and 96%, while those of expert physician reads were 91% and 97%. Specificity and positive predictive value were lower (50% and 32%) compared with expert physician-read echocardiograms (82% and 56%) | ||
| 520 | |a CONCLUSIONS: AI-derived measurements of FAC, FWS, and TAPSE had excellent sensitivity and negative predictive value for ruling out significant RV dysfunction (CMR RVEF < 40%), comparable with that of expert physician readers, but lower specificity. Thus AI, using American Society of Echocardiography guidelines, may serve as a useful screening tool for rapid bedside assessment to exclude significant RV dysfunction | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Cardiac MRI | |
| 650 | 4 | |a Fractional area change | |
| 650 | 4 | |a Free wall strain | |
| 650 | 4 | |a Right ventricle | |
| 650 | 4 | |a TAPSE | |
| 650 | 4 | |a Transthoracic echocardiography | |
| 700 | 1 | |a Lai, Ashton |e verfasserin |4 aut | |
| 700 | 1 | |a Singh, Supreet |e verfasserin |4 aut | |
| 700 | 1 | |a Samtani, Rajeev |e verfasserin |4 aut | |
| 700 | 1 | |a Bienstock, Solomon |e verfasserin |4 aut | |
| 700 | 1 | |a Liao, Steve |e verfasserin |4 aut | |
| 700 | 1 | |a Stern, Eric |e verfasserin |4 aut | |
| 700 | 1 | |a LaRocca, Gina |e verfasserin |4 aut | |
| 700 | 1 | |a Sanz, Javier |e verfasserin |4 aut | |
| 700 | 1 | |a Lerakis, Stamatios |e verfasserin |4 aut | |
| 700 | 1 | |a Croft, Lori |e verfasserin |4 aut | |
| 700 | 1 | |a Carrasso, Shemy |e verfasserin |4 aut | |
| 700 | 1 | |a Rosenmann, David |e verfasserin |4 aut | |
| 700 | 1 | |a DeMaria, Anthony |e verfasserin |4 aut | |
| 700 | 1 | |a Stone, Gregg W |e verfasserin |4 aut | |
| 700 | 1 | |a Goldman, Martin E |e verfasserin |4 aut | |
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