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Echocardiographic Left Ventricular Mass Assessment: Correlation between 2D-Derived Linear Dimensions and 3-Dimensional Automated, Machine Learning-Based Methods in Unselected Patients
A recently developed algorithm for 3D analysis based on machine learning (ML) principles detects left ventricular (LV) mass without any human interaction. We retrospectively studied the correlation between 2D-derived linear dimensions using the ASE/EACVI-recommended formula and 3D automated, ML-base...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8003438/ https://www.ncbi.nlm.nih.gov/pubmed/33808707 http://dx.doi.org/10.3390/jcm10061279 |
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author | Barbieri, Andrea Bursi, Francesca Camaioni, Giovanni Maisano, Anna Imberti, Jacopo Francesco Albini, Alessandro De Mitri, Gerardo Mantovani, Francesca Boriani, Giuseppe |
author_facet | Barbieri, Andrea Bursi, Francesca Camaioni, Giovanni Maisano, Anna Imberti, Jacopo Francesco Albini, Alessandro De Mitri, Gerardo Mantovani, Francesca Boriani, Giuseppe |
author_sort | Barbieri, Andrea |
collection | PubMed |
description | A recently developed algorithm for 3D analysis based on machine learning (ML) principles detects left ventricular (LV) mass without any human interaction. We retrospectively studied the correlation between 2D-derived linear dimensions using the ASE/EACVI-recommended formula and 3D automated, ML-based methods (Philips HeartModel) regarding LV mass quantification in unselected patients undergoing echocardiography. We included 130 patients (mean age 60 ± 18 years; 45% women). There was only discrete agreement between 2D and 3D measurements of LV mass (r = 0.662, r(2) = 0.348, p < 0.001). The automated algorithm yielded an overestimation of LV mass compared to the linear method (Bland–Altman positive bias of 13.1 g with 95% limits of the agreement at 4.5 to 21.6 g, p = 0.003, ICC 0.78 (95%CI 0.68−8.4). There was a significant proportional bias (Beta −0.22, t = −2.9) p = 0.005, the variance of the difference varied across the range of LV mass. When the published cut-offs for LV mass abnormality were used, the observed proportion of overall agreement was 77% (kappa = 0.32, p < 0.001). In consecutive patients undergoing echocardiography for any indications, LV mass assessment by 3D analysis using a novel ML-based algorithm showed systematic differences and wide limits of agreements compared with quantification by ASE/EACVI- recommended formula when the current cut-offs and partition values were applied. |
format | Online Article Text |
id | pubmed-8003438 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-80034382021-03-28 Echocardiographic Left Ventricular Mass Assessment: Correlation between 2D-Derived Linear Dimensions and 3-Dimensional Automated, Machine Learning-Based Methods in Unselected Patients Barbieri, Andrea Bursi, Francesca Camaioni, Giovanni Maisano, Anna Imberti, Jacopo Francesco Albini, Alessandro De Mitri, Gerardo Mantovani, Francesca Boriani, Giuseppe J Clin Med Article A recently developed algorithm for 3D analysis based on machine learning (ML) principles detects left ventricular (LV) mass without any human interaction. We retrospectively studied the correlation between 2D-derived linear dimensions using the ASE/EACVI-recommended formula and 3D automated, ML-based methods (Philips HeartModel) regarding LV mass quantification in unselected patients undergoing echocardiography. We included 130 patients (mean age 60 ± 18 years; 45% women). There was only discrete agreement between 2D and 3D measurements of LV mass (r = 0.662, r(2) = 0.348, p < 0.001). The automated algorithm yielded an overestimation of LV mass compared to the linear method (Bland–Altman positive bias of 13.1 g with 95% limits of the agreement at 4.5 to 21.6 g, p = 0.003, ICC 0.78 (95%CI 0.68−8.4). There was a significant proportional bias (Beta −0.22, t = −2.9) p = 0.005, the variance of the difference varied across the range of LV mass. When the published cut-offs for LV mass abnormality were used, the observed proportion of overall agreement was 77% (kappa = 0.32, p < 0.001). In consecutive patients undergoing echocardiography for any indications, LV mass assessment by 3D analysis using a novel ML-based algorithm showed systematic differences and wide limits of agreements compared with quantification by ASE/EACVI- recommended formula when the current cut-offs and partition values were applied. MDPI 2021-03-19 /pmc/articles/PMC8003438/ /pubmed/33808707 http://dx.doi.org/10.3390/jcm10061279 Text en © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Barbieri, Andrea Bursi, Francesca Camaioni, Giovanni Maisano, Anna Imberti, Jacopo Francesco Albini, Alessandro De Mitri, Gerardo Mantovani, Francesca Boriani, Giuseppe Echocardiographic Left Ventricular Mass Assessment: Correlation between 2D-Derived Linear Dimensions and 3-Dimensional Automated, Machine Learning-Based Methods in Unselected Patients |
title | Echocardiographic Left Ventricular Mass Assessment: Correlation between 2D-Derived Linear Dimensions and 3-Dimensional Automated, Machine Learning-Based Methods in Unselected Patients |
title_full | Echocardiographic Left Ventricular Mass Assessment: Correlation between 2D-Derived Linear Dimensions and 3-Dimensional Automated, Machine Learning-Based Methods in Unselected Patients |
title_fullStr | Echocardiographic Left Ventricular Mass Assessment: Correlation between 2D-Derived Linear Dimensions and 3-Dimensional Automated, Machine Learning-Based Methods in Unselected Patients |
title_full_unstemmed | Echocardiographic Left Ventricular Mass Assessment: Correlation between 2D-Derived Linear Dimensions and 3-Dimensional Automated, Machine Learning-Based Methods in Unselected Patients |
title_short | Echocardiographic Left Ventricular Mass Assessment: Correlation between 2D-Derived Linear Dimensions and 3-Dimensional Automated, Machine Learning-Based Methods in Unselected Patients |
title_sort | echocardiographic left ventricular mass assessment: correlation between 2d-derived linear dimensions and 3-dimensional automated, machine learning-based methods in unselected patients |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8003438/ https://www.ncbi.nlm.nih.gov/pubmed/33808707 http://dx.doi.org/10.3390/jcm10061279 |
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