Cargando…

A machine learning algorithm supports ultrasound-naïve novices in the acquisition of diagnostic echocardiography loops and provides accurate estimation of LVEF

Left ventricular ejection fraction (LVEF) is the most important parameter in the assessment of cardiac function. A machine-learning algorithm was trained to guide ultrasound-novices to acquire diagnostic echocardiography images. The artificial intelligence (AI) algorithm then estimates LVEF from the...

Descripción completa

Detalles Bibliográficos
Autores principales: Schneider, Matthias, Bartko, Philipp, Geller, Welf, Dannenberg, Varius, König, Andreas, Binder, Christina, Goliasch, Georg, Hengstenberg, Christian, Binder, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Netherlands 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7541096/
https://www.ncbi.nlm.nih.gov/pubmed/33029699
http://dx.doi.org/10.1007/s10554-020-02046-6
_version_ 1783591334579273728
author Schneider, Matthias
Bartko, Philipp
Geller, Welf
Dannenberg, Varius
König, Andreas
Binder, Christina
Goliasch, Georg
Hengstenberg, Christian
Binder, Thomas
author_facet Schneider, Matthias
Bartko, Philipp
Geller, Welf
Dannenberg, Varius
König, Andreas
Binder, Christina
Goliasch, Georg
Hengstenberg, Christian
Binder, Thomas
author_sort Schneider, Matthias
collection PubMed
description Left ventricular ejection fraction (LVEF) is the most important parameter in the assessment of cardiac function. A machine-learning algorithm was trained to guide ultrasound-novices to acquire diagnostic echocardiography images. The artificial intelligence (AI) algorithm then estimates LVEF from the captured apical-4-chamber (AP4), apical-2-chamber (AP2), and parasternal-long-axis (PLAX) loops. We sought to test this algorithm by having first-year medical students without previous ultrasound knowledge scan real patients. Nineteen echo-naïve first-year medical students were trained in the basics of echocardiography by a 2.5 h online video tutorial. Each student then scanned three patients with the help of the AI. Image quality was graded according to the American College of Emergency Physicians scale. If rated as diagnostic quality, the AI calculated LVEF from the acquired loops (monoplane and also a “best-LVEF” considering all views acquired in the particular patient). These LVEF calculations were compared to images of the same patients captured and read by three experts (ground-truth LVEF [GT-EF]). The novices acquired diagnostic-quality images in 33/57 (58%), 49/57 (86%), and 39/57 (68%) patients in the PLAX, AP4, and AP2, respectively. At least one of the three views was obtained in 91% of the attempts. We found an excellent agreement between the machine’s LVEF calculations from images acquired by the novices with the GT-EF (bias of 3.5% ± 5.6 and r = 0.92, p < 0.001 in the “best-LVEF” algorithm). This pilot study shows first evidence that a machine-learning algorithm can guide ultrasound-novices to acquire diagnostic echo loops and provide an automated LVEF calculation that is in agreement with a human expert.
format Online
Article
Text
id pubmed-7541096
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher Springer Netherlands
record_format MEDLINE/PubMed
spelling pubmed-75410962020-10-08 A machine learning algorithm supports ultrasound-naïve novices in the acquisition of diagnostic echocardiography loops and provides accurate estimation of LVEF Schneider, Matthias Bartko, Philipp Geller, Welf Dannenberg, Varius König, Andreas Binder, Christina Goliasch, Georg Hengstenberg, Christian Binder, Thomas Int J Cardiovasc Imaging Original Paper Left ventricular ejection fraction (LVEF) is the most important parameter in the assessment of cardiac function. A machine-learning algorithm was trained to guide ultrasound-novices to acquire diagnostic echocardiography images. The artificial intelligence (AI) algorithm then estimates LVEF from the captured apical-4-chamber (AP4), apical-2-chamber (AP2), and parasternal-long-axis (PLAX) loops. We sought to test this algorithm by having first-year medical students without previous ultrasound knowledge scan real patients. Nineteen echo-naïve first-year medical students were trained in the basics of echocardiography by a 2.5 h online video tutorial. Each student then scanned three patients with the help of the AI. Image quality was graded according to the American College of Emergency Physicians scale. If rated as diagnostic quality, the AI calculated LVEF from the acquired loops (monoplane and also a “best-LVEF” considering all views acquired in the particular patient). These LVEF calculations were compared to images of the same patients captured and read by three experts (ground-truth LVEF [GT-EF]). The novices acquired diagnostic-quality images in 33/57 (58%), 49/57 (86%), and 39/57 (68%) patients in the PLAX, AP4, and AP2, respectively. At least one of the three views was obtained in 91% of the attempts. We found an excellent agreement between the machine’s LVEF calculations from images acquired by the novices with the GT-EF (bias of 3.5% ± 5.6 and r = 0.92, p < 0.001 in the “best-LVEF” algorithm). This pilot study shows first evidence that a machine-learning algorithm can guide ultrasound-novices to acquire diagnostic echo loops and provide an automated LVEF calculation that is in agreement with a human expert. Springer Netherlands 2020-10-08 2021 /pmc/articles/PMC7541096/ /pubmed/33029699 http://dx.doi.org/10.1007/s10554-020-02046-6 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Original Paper
Schneider, Matthias
Bartko, Philipp
Geller, Welf
Dannenberg, Varius
König, Andreas
Binder, Christina
Goliasch, Georg
Hengstenberg, Christian
Binder, Thomas
A machine learning algorithm supports ultrasound-naïve novices in the acquisition of diagnostic echocardiography loops and provides accurate estimation of LVEF
title A machine learning algorithm supports ultrasound-naïve novices in the acquisition of diagnostic echocardiography loops and provides accurate estimation of LVEF
title_full A machine learning algorithm supports ultrasound-naïve novices in the acquisition of diagnostic echocardiography loops and provides accurate estimation of LVEF
title_fullStr A machine learning algorithm supports ultrasound-naïve novices in the acquisition of diagnostic echocardiography loops and provides accurate estimation of LVEF
title_full_unstemmed A machine learning algorithm supports ultrasound-naïve novices in the acquisition of diagnostic echocardiography loops and provides accurate estimation of LVEF
title_short A machine learning algorithm supports ultrasound-naïve novices in the acquisition of diagnostic echocardiography loops and provides accurate estimation of LVEF
title_sort machine learning algorithm supports ultrasound-naïve novices in the acquisition of diagnostic echocardiography loops and provides accurate estimation of lvef
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7541096/
https://www.ncbi.nlm.nih.gov/pubmed/33029699
http://dx.doi.org/10.1007/s10554-020-02046-6
work_keys_str_mv AT schneidermatthias amachinelearningalgorithmsupportsultrasoundnaivenovicesintheacquisitionofdiagnosticechocardiographyloopsandprovidesaccurateestimationoflvef
AT bartkophilipp amachinelearningalgorithmsupportsultrasoundnaivenovicesintheacquisitionofdiagnosticechocardiographyloopsandprovidesaccurateestimationoflvef
AT gellerwelf amachinelearningalgorithmsupportsultrasoundnaivenovicesintheacquisitionofdiagnosticechocardiographyloopsandprovidesaccurateestimationoflvef
AT dannenbergvarius amachinelearningalgorithmsupportsultrasoundnaivenovicesintheacquisitionofdiagnosticechocardiographyloopsandprovidesaccurateestimationoflvef
AT konigandreas amachinelearningalgorithmsupportsultrasoundnaivenovicesintheacquisitionofdiagnosticechocardiographyloopsandprovidesaccurateestimationoflvef
AT binderchristina amachinelearningalgorithmsupportsultrasoundnaivenovicesintheacquisitionofdiagnosticechocardiographyloopsandprovidesaccurateestimationoflvef
AT goliaschgeorg amachinelearningalgorithmsupportsultrasoundnaivenovicesintheacquisitionofdiagnosticechocardiographyloopsandprovidesaccurateestimationoflvef
AT hengstenbergchristian amachinelearningalgorithmsupportsultrasoundnaivenovicesintheacquisitionofdiagnosticechocardiographyloopsandprovidesaccurateestimationoflvef
AT binderthomas amachinelearningalgorithmsupportsultrasoundnaivenovicesintheacquisitionofdiagnosticechocardiographyloopsandprovidesaccurateestimationoflvef
AT schneidermatthias machinelearningalgorithmsupportsultrasoundnaivenovicesintheacquisitionofdiagnosticechocardiographyloopsandprovidesaccurateestimationoflvef
AT bartkophilipp machinelearningalgorithmsupportsultrasoundnaivenovicesintheacquisitionofdiagnosticechocardiographyloopsandprovidesaccurateestimationoflvef
AT gellerwelf machinelearningalgorithmsupportsultrasoundnaivenovicesintheacquisitionofdiagnosticechocardiographyloopsandprovidesaccurateestimationoflvef
AT dannenbergvarius machinelearningalgorithmsupportsultrasoundnaivenovicesintheacquisitionofdiagnosticechocardiographyloopsandprovidesaccurateestimationoflvef
AT konigandreas machinelearningalgorithmsupportsultrasoundnaivenovicesintheacquisitionofdiagnosticechocardiographyloopsandprovidesaccurateestimationoflvef
AT binderchristina machinelearningalgorithmsupportsultrasoundnaivenovicesintheacquisitionofdiagnosticechocardiographyloopsandprovidesaccurateestimationoflvef
AT goliaschgeorg machinelearningalgorithmsupportsultrasoundnaivenovicesintheacquisitionofdiagnosticechocardiographyloopsandprovidesaccurateestimationoflvef
AT hengstenbergchristian machinelearningalgorithmsupportsultrasoundnaivenovicesintheacquisitionofdiagnosticechocardiographyloopsandprovidesaccurateestimationoflvef
AT binderthomas machinelearningalgorithmsupportsultrasoundnaivenovicesintheacquisitionofdiagnosticechocardiographyloopsandprovidesaccurateestimationoflvef