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.

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. - In: JOURNAL OF CLINICAL MEDICINE. - ISSN 2077-0383. - 10:6(2021), pp. 1-10. [10.3390/jcm10061279]

Echocardiographic Left Ventricular Mass Assessment: Correlation between 2D-Derived Linear Dimensions and 3-Dimensional Automated, Machine Learning-Based Methods in Unselected Patients

Boriani, Giuseppe
2021

Abstract

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.
2021
10
6
1
10
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. - In: JOURNAL OF CLINICAL MEDICINE. - ISSN 2077-0383. - 10:6(2021), pp. 1-10. [10.3390/jcm10061279]
Barbieri, Andrea; Bursi, Francesca; Camaioni, Giovanni; Maisano, Anna; Imberti, Jacopo Francesco; Albini, Alessandro; De Mitri, Gerardo; Mantovani, Fr...espandi
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11380/1251500
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