Single-Site Experience with an Automated Artificial Intelligence Application for Left Ventricular Ejection Fraction Measurement in Echocardiography
Left ventricular ejection fraction (LVEF) is a key parameter in evaluating left ventricular (LV) function using echocardiography (Echo), but its manual measurement by the modified biplane Simpson (MBS) method is time consuming and operator dependent. We investigated the feasibility of a server-based...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-03-01
|
| Series: | Diagnostics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2075-4418/13/7/1298 |
| _version_ | 1827747000490131456 |
|---|---|
| author | Krunoslav Michael Sveric Roxana Botan Zouhir Dindane Anna Winkler Thomas Nowack Christoph Heitmann Leonhard Schleußner Axel Linke |
| author_facet | Krunoslav Michael Sveric Roxana Botan Zouhir Dindane Anna Winkler Thomas Nowack Christoph Heitmann Leonhard Schleußner Axel Linke |
| author_sort | Krunoslav Michael Sveric |
| collection | DOAJ |
| description | Left ventricular ejection fraction (LVEF) is a key parameter in evaluating left ventricular (LV) function using echocardiography (Echo), but its manual measurement by the modified biplane Simpson (MBS) method is time consuming and operator dependent. We investigated the feasibility of a server-based, commercially available and ready-to use-artificial intelligence (AI) application based on convolutional neural network methods that integrate fully automatic view selection and measurement of LVEF from an entire Echo exam into a single workflow. We prospectively enrolled 1083 consecutive patients who had been referred to Echo for diagnostic or therapeutic purposes. LVEF was measured independently using MBS and AI. Test–retest variability was assessed in 40 patients. The reliability, repeatability, and time efficiency of LVEF measurements were compared between the two methods. Overall, 889 Echos were analyzed by cardiologists with the MBS method and by the AI. Over the study period of 10 weeks, the feasibility of both automatic view classification and seamlessly measured LVEF rose to 81% without user involvement. LVEF, LV end-diastolic and end-systolic volumes correlated strongly between MBS and AI (R = 0.87, 0.89 and 0.93, <i>p</i> < 0.001 for all) with a mean bias of +4.5% EF, −12 mL and −11 mL, respectively, due to impaired image quality and the extent of LV function. Repeatability and reliability of LVEF measurement (<i>n</i> = 40, test–retest) by AI was excellent compared to MBS (coefficient of variation: 3.2% vs. 5.9%), although the median analysis time of the AI was longer than that of the operator-dependent MBS method (258 s vs. 171 s). This AI has succeeded in identifying apical LV views and measuring EF in one workflow with comparable results to the MBS method and shows excellent reproducibility. It offers realistic perspectives for fully automated AI-based measurement of LVEF in routine clinical settings. |
| first_indexed | 2024-03-11T05:39:57Z |
| format | Article |
| id | doaj.art-85a5c1423ca84360883728d749f8ef72 |
| institution | Directory Open Access Journal |
| issn | 2075-4418 |
| language | English |
| last_indexed | 2024-03-11T05:39:57Z |
| publishDate | 2023-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Diagnostics |
| spelling | doaj.art-85a5c1423ca84360883728d749f8ef722023-11-17T16:30:36ZengMDPI AGDiagnostics2075-44182023-03-01137129810.3390/diagnostics13071298Single-Site Experience with an Automated Artificial Intelligence Application for Left Ventricular Ejection Fraction Measurement in EchocardiographyKrunoslav Michael Sveric0Roxana Botan1Zouhir Dindane2Anna Winkler3Thomas Nowack4Christoph Heitmann5Leonhard Schleußner6Axel Linke7Department of Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Fetscherstr. 76, 01307 Dresden, GermanyDepartment of Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Fetscherstr. 76, 01307 Dresden, GermanyDepartment of Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Fetscherstr. 76, 01307 Dresden, GermanyDepartment of Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Fetscherstr. 76, 01307 Dresden, GermanyDepartment of Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Fetscherstr. 76, 01307 Dresden, GermanyDepartment of Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Fetscherstr. 76, 01307 Dresden, GermanyDepartment of Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Fetscherstr. 76, 01307 Dresden, GermanyDepartment of Internal Medicine and Cardiology, Herzzentrum Dresden, Technische Universität Dresden, Fetscherstr. 76, 01307 Dresden, GermanyLeft ventricular ejection fraction (LVEF) is a key parameter in evaluating left ventricular (LV) function using echocardiography (Echo), but its manual measurement by the modified biplane Simpson (MBS) method is time consuming and operator dependent. We investigated the feasibility of a server-based, commercially available and ready-to use-artificial intelligence (AI) application based on convolutional neural network methods that integrate fully automatic view selection and measurement of LVEF from an entire Echo exam into a single workflow. We prospectively enrolled 1083 consecutive patients who had been referred to Echo for diagnostic or therapeutic purposes. LVEF was measured independently using MBS and AI. Test–retest variability was assessed in 40 patients. The reliability, repeatability, and time efficiency of LVEF measurements were compared between the two methods. Overall, 889 Echos were analyzed by cardiologists with the MBS method and by the AI. Over the study period of 10 weeks, the feasibility of both automatic view classification and seamlessly measured LVEF rose to 81% without user involvement. LVEF, LV end-diastolic and end-systolic volumes correlated strongly between MBS and AI (R = 0.87, 0.89 and 0.93, <i>p</i> < 0.001 for all) with a mean bias of +4.5% EF, −12 mL and −11 mL, respectively, due to impaired image quality and the extent of LV function. Repeatability and reliability of LVEF measurement (<i>n</i> = 40, test–retest) by AI was excellent compared to MBS (coefficient of variation: 3.2% vs. 5.9%), although the median analysis time of the AI was longer than that of the operator-dependent MBS method (258 s vs. 171 s). This AI has succeeded in identifying apical LV views and measuring EF in one workflow with comparable results to the MBS method and shows excellent reproducibility. It offers realistic perspectives for fully automated AI-based measurement of LVEF in routine clinical settings.https://www.mdpi.com/2075-4418/13/7/1298seamlessartificial intelligenceleft ventricular ejection fractionechocardiography |
| spellingShingle | Krunoslav Michael Sveric Roxana Botan Zouhir Dindane Anna Winkler Thomas Nowack Christoph Heitmann Leonhard Schleußner Axel Linke Single-Site Experience with an Automated Artificial Intelligence Application for Left Ventricular Ejection Fraction Measurement in Echocardiography Diagnostics seamless artificial intelligence left ventricular ejection fraction echocardiography |
| title | Single-Site Experience with an Automated Artificial Intelligence Application for Left Ventricular Ejection Fraction Measurement in Echocardiography |
| title_full | Single-Site Experience with an Automated Artificial Intelligence Application for Left Ventricular Ejection Fraction Measurement in Echocardiography |
| title_fullStr | Single-Site Experience with an Automated Artificial Intelligence Application for Left Ventricular Ejection Fraction Measurement in Echocardiography |
| title_full_unstemmed | Single-Site Experience with an Automated Artificial Intelligence Application for Left Ventricular Ejection Fraction Measurement in Echocardiography |
| title_short | Single-Site Experience with an Automated Artificial Intelligence Application for Left Ventricular Ejection Fraction Measurement in Echocardiography |
| title_sort | single site experience with an automated artificial intelligence application for left ventricular ejection fraction measurement in echocardiography |
| topic | seamless artificial intelligence left ventricular ejection fraction echocardiography |
| url | https://www.mdpi.com/2075-4418/13/7/1298 |
| work_keys_str_mv | AT krunoslavmichaelsveric singlesiteexperiencewithanautomatedartificialintelligenceapplicationforleftventricularejectionfractionmeasurementinechocardiography AT roxanabotan singlesiteexperiencewithanautomatedartificialintelligenceapplicationforleftventricularejectionfractionmeasurementinechocardiography AT zouhirdindane singlesiteexperiencewithanautomatedartificialintelligenceapplicationforleftventricularejectionfractionmeasurementinechocardiography AT annawinkler singlesiteexperiencewithanautomatedartificialintelligenceapplicationforleftventricularejectionfractionmeasurementinechocardiography AT thomasnowack singlesiteexperiencewithanautomatedartificialintelligenceapplicationforleftventricularejectionfractionmeasurementinechocardiography AT christophheitmann singlesiteexperiencewithanautomatedartificialintelligenceapplicationforleftventricularejectionfractionmeasurementinechocardiography AT leonhardschleußner singlesiteexperiencewithanautomatedartificialintelligenceapplicationforleftventricularejectionfractionmeasurementinechocardiography AT axellinke singlesiteexperiencewithanautomatedartificialintelligenceapplicationforleftventricularejectionfractionmeasurementinechocardiography |