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Developing an Echocardiography-Based, Automatic Deep Learning Framework for the Differentiation of Increased Left Ventricular Wall Thickness Etiologies
Aims:Increased left ventricular (LV) wall thickness is frequently encountered in transthoracic echocardiography (TTE). While accurate and early diagnosis is clinically important, given the differences in available therapeutic options and prognosis, an extensive workup is often required to establish...
Autores principales: | , , , , , , , , , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9964852/ https://www.ncbi.nlm.nih.gov/pubmed/36826967 http://dx.doi.org/10.3390/jimaging9020048 |
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author | Li, James Chao, Chieh-Ju Jeong, Jiwoong Jason Farina, Juan Maria Seri, Amith R. Barry, Timothy Newman, Hana Campany, Megan Abdou, Merna O’Shea, Michael Smith, Sean Abraham, Bishoy Hosseini, Seyedeh Maryam Wang, Yuxiang Lester, Steven Alsidawi, Said Wilansky, Susan Steidley, Eric Rosenthal, Julie Ayoub, Chadi Appleton, Christopher P. Shen, Win-Kuang Grogan, Martha Kane, Garvan C. Oh, Jae K. Patel, Bhavik N. Arsanjani, Reza Banerjee, Imon |
author_facet | Li, James Chao, Chieh-Ju Jeong, Jiwoong Jason Farina, Juan Maria Seri, Amith R. Barry, Timothy Newman, Hana Campany, Megan Abdou, Merna O’Shea, Michael Smith, Sean Abraham, Bishoy Hosseini, Seyedeh Maryam Wang, Yuxiang Lester, Steven Alsidawi, Said Wilansky, Susan Steidley, Eric Rosenthal, Julie Ayoub, Chadi Appleton, Christopher P. Shen, Win-Kuang Grogan, Martha Kane, Garvan C. Oh, Jae K. Patel, Bhavik N. Arsanjani, Reza Banerjee, Imon |
author_sort | Li, James |
collection | PubMed |
description | Aims:Increased left ventricular (LV) wall thickness is frequently encountered in transthoracic echocardiography (TTE). While accurate and early diagnosis is clinically important, given the differences in available therapeutic options and prognosis, an extensive workup is often required to establish the diagnosis. We propose the first echo-based, automated deep learning model with a fusion architecture to facilitate the evaluation and diagnosis of increased left ventricular (LV) wall thickness. Methods and Results: Patients with an established diagnosis of increased LV wall thickness (hypertrophic cardiomyopathy (HCM), cardiac amyloidosis (CA), and hypertensive heart disease (HTN)/others) between 1/2015 and 11/2019 at Mayo Clinic Arizona were identified. The cohort was divided into 80%/10%/10% for training, validation, and testing sets, respectively. Six baseline TTE views were used to optimize a pre-trained InceptionResnetV2 model. Each model output was used to train a meta-learner under a fusion architecture. Model performance was assessed by multiclass area under the receiver operating characteristic curve (AUROC). A total of 586 patients were used for the final analysis (194 HCM, 201 CA, and 191 HTN/others). The mean age was 55.0 years, and 57.8% were male. Among the individual view-dependent models, the apical 4-chamber model had the best performance (AUROC: HCM: 0.94, CA: 0.73, and HTN/other: 0.87). The final fusion model outperformed all the view-dependent models (AUROC: HCM: 0.93, CA: 0.90, and HTN/other: 0.92). Conclusion: The echo-based InceptionResnetV2 fusion model can accurately classify the main etiologies of increased LV wall thickness and can facilitate the process of diagnosis and workup. |
format | Online Article Text |
id | pubmed-9964852 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99648522023-02-26 Developing an Echocardiography-Based, Automatic Deep Learning Framework for the Differentiation of Increased Left Ventricular Wall Thickness Etiologies Li, James Chao, Chieh-Ju Jeong, Jiwoong Jason Farina, Juan Maria Seri, Amith R. Barry, Timothy Newman, Hana Campany, Megan Abdou, Merna O’Shea, Michael Smith, Sean Abraham, Bishoy Hosseini, Seyedeh Maryam Wang, Yuxiang Lester, Steven Alsidawi, Said Wilansky, Susan Steidley, Eric Rosenthal, Julie Ayoub, Chadi Appleton, Christopher P. Shen, Win-Kuang Grogan, Martha Kane, Garvan C. Oh, Jae K. Patel, Bhavik N. Arsanjani, Reza Banerjee, Imon J Imaging Article Aims:Increased left ventricular (LV) wall thickness is frequently encountered in transthoracic echocardiography (TTE). While accurate and early diagnosis is clinically important, given the differences in available therapeutic options and prognosis, an extensive workup is often required to establish the diagnosis. We propose the first echo-based, automated deep learning model with a fusion architecture to facilitate the evaluation and diagnosis of increased left ventricular (LV) wall thickness. Methods and Results: Patients with an established diagnosis of increased LV wall thickness (hypertrophic cardiomyopathy (HCM), cardiac amyloidosis (CA), and hypertensive heart disease (HTN)/others) between 1/2015 and 11/2019 at Mayo Clinic Arizona were identified. The cohort was divided into 80%/10%/10% for training, validation, and testing sets, respectively. Six baseline TTE views were used to optimize a pre-trained InceptionResnetV2 model. Each model output was used to train a meta-learner under a fusion architecture. Model performance was assessed by multiclass area under the receiver operating characteristic curve (AUROC). A total of 586 patients were used for the final analysis (194 HCM, 201 CA, and 191 HTN/others). The mean age was 55.0 years, and 57.8% were male. Among the individual view-dependent models, the apical 4-chamber model had the best performance (AUROC: HCM: 0.94, CA: 0.73, and HTN/other: 0.87). The final fusion model outperformed all the view-dependent models (AUROC: HCM: 0.93, CA: 0.90, and HTN/other: 0.92). Conclusion: The echo-based InceptionResnetV2 fusion model can accurately classify the main etiologies of increased LV wall thickness and can facilitate the process of diagnosis and workup. MDPI 2023-02-18 /pmc/articles/PMC9964852/ /pubmed/36826967 http://dx.doi.org/10.3390/jimaging9020048 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/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 (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Li, James Chao, Chieh-Ju Jeong, Jiwoong Jason Farina, Juan Maria Seri, Amith R. Barry, Timothy Newman, Hana Campany, Megan Abdou, Merna O’Shea, Michael Smith, Sean Abraham, Bishoy Hosseini, Seyedeh Maryam Wang, Yuxiang Lester, Steven Alsidawi, Said Wilansky, Susan Steidley, Eric Rosenthal, Julie Ayoub, Chadi Appleton, Christopher P. Shen, Win-Kuang Grogan, Martha Kane, Garvan C. Oh, Jae K. Patel, Bhavik N. Arsanjani, Reza Banerjee, Imon Developing an Echocardiography-Based, Automatic Deep Learning Framework for the Differentiation of Increased Left Ventricular Wall Thickness Etiologies |
title | Developing an Echocardiography-Based, Automatic Deep Learning Framework for the Differentiation of Increased Left Ventricular Wall Thickness Etiologies |
title_full | Developing an Echocardiography-Based, Automatic Deep Learning Framework for the Differentiation of Increased Left Ventricular Wall Thickness Etiologies |
title_fullStr | Developing an Echocardiography-Based, Automatic Deep Learning Framework for the Differentiation of Increased Left Ventricular Wall Thickness Etiologies |
title_full_unstemmed | Developing an Echocardiography-Based, Automatic Deep Learning Framework for the Differentiation of Increased Left Ventricular Wall Thickness Etiologies |
title_short | Developing an Echocardiography-Based, Automatic Deep Learning Framework for the Differentiation of Increased Left Ventricular Wall Thickness Etiologies |
title_sort | developing an echocardiography-based, automatic deep learning framework for the differentiation of increased left ventricular wall thickness etiologies |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9964852/ https://www.ncbi.nlm.nih.gov/pubmed/36826967 http://dx.doi.org/10.3390/jimaging9020048 |
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