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Deep learning to diagnose cardiac amyloidosis from cardiovascular magnetic resonance
BACKGROUND: Cardiovascular magnetic resonance (CMR) is part of the diagnostic work-up for cardiac amyloidosis (CA). Deep learning (DL) is an application of artificial intelligence that may allow to automatically analyze CMR findings and establish the likelihood of CA. METHODS: 1.5 T CMR was performe...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7720569/ https://www.ncbi.nlm.nih.gov/pubmed/33287829 http://dx.doi.org/10.1186/s12968-020-00690-4 |
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author | Martini, Nicola Aimo, Alberto Barison, Andrea Della Latta, Daniele Vergaro, Giuseppe Aquaro, Giovanni Donato Ripoli, Andrea Emdin, Michele Chiappino, Dante |
author_facet | Martini, Nicola Aimo, Alberto Barison, Andrea Della Latta, Daniele Vergaro, Giuseppe Aquaro, Giovanni Donato Ripoli, Andrea Emdin, Michele Chiappino, Dante |
author_sort | Martini, Nicola |
collection | PubMed |
description | BACKGROUND: Cardiovascular magnetic resonance (CMR) is part of the diagnostic work-up for cardiac amyloidosis (CA). Deep learning (DL) is an application of artificial intelligence that may allow to automatically analyze CMR findings and establish the likelihood of CA. METHODS: 1.5 T CMR was performed in 206 subjects with suspected CA (n = 100, 49% with unexplained left ventricular (LV) hypertrophy; n = 106, 51% with blood dyscrasia and suspected light-chain amyloidosis). Patients were randomly assigned to the training (n = 134, 65%), validation (n = 30, 15%), and testing subgroups (n = 42, 20%). Short axis, 2-chamber, 4-chamber late gadolinium enhancement (LGE) images were evaluated by 3 networks (DL algorithms). The tags “amyloidosis present” or “absent” were attributed when the average probability of CA from the 3 networks was ≥ 50% or < 50%, respectively. The DL strategy was compared to a machine learning (ML) algorithm considering all manually extracted features (LV volumes, mass and function, LGE pattern, early blood-pool darkening, pericardial and pleural effusion, etc.), to reproduce exam reading by an experienced operator. RESULTS: The DL strategy displayed good diagnostic accuracy (88%), with an area under the curve (AUC) of 0.982. The precision (positive predictive value), recall score (sensitivity), and F1 score (a measure of test accuracy) were 83%, 95%, and 89% respectively. A ML algorithm considering all CMR features had a similar diagnostic yield to DL strategy (AUC 0.952 vs. 0.982; p = 0.39). CONCLUSIONS: A DL approach evaluating LGE acquisitions displayed a similar diagnostic performance for CA to a ML-based approach, which simulates CMR reading by experienced operators. |
format | Online Article Text |
id | pubmed-7720569 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-77205692020-12-07 Deep learning to diagnose cardiac amyloidosis from cardiovascular magnetic resonance Martini, Nicola Aimo, Alberto Barison, Andrea Della Latta, Daniele Vergaro, Giuseppe Aquaro, Giovanni Donato Ripoli, Andrea Emdin, Michele Chiappino, Dante J Cardiovasc Magn Reson Research BACKGROUND: Cardiovascular magnetic resonance (CMR) is part of the diagnostic work-up for cardiac amyloidosis (CA). Deep learning (DL) is an application of artificial intelligence that may allow to automatically analyze CMR findings and establish the likelihood of CA. METHODS: 1.5 T CMR was performed in 206 subjects with suspected CA (n = 100, 49% with unexplained left ventricular (LV) hypertrophy; n = 106, 51% with blood dyscrasia and suspected light-chain amyloidosis). Patients were randomly assigned to the training (n = 134, 65%), validation (n = 30, 15%), and testing subgroups (n = 42, 20%). Short axis, 2-chamber, 4-chamber late gadolinium enhancement (LGE) images were evaluated by 3 networks (DL algorithms). The tags “amyloidosis present” or “absent” were attributed when the average probability of CA from the 3 networks was ≥ 50% or < 50%, respectively. The DL strategy was compared to a machine learning (ML) algorithm considering all manually extracted features (LV volumes, mass and function, LGE pattern, early blood-pool darkening, pericardial and pleural effusion, etc.), to reproduce exam reading by an experienced operator. RESULTS: The DL strategy displayed good diagnostic accuracy (88%), with an area under the curve (AUC) of 0.982. The precision (positive predictive value), recall score (sensitivity), and F1 score (a measure of test accuracy) were 83%, 95%, and 89% respectively. A ML algorithm considering all CMR features had a similar diagnostic yield to DL strategy (AUC 0.952 vs. 0.982; p = 0.39). CONCLUSIONS: A DL approach evaluating LGE acquisitions displayed a similar diagnostic performance for CA to a ML-based approach, which simulates CMR reading by experienced operators. BioMed Central 2020-12-07 /pmc/articles/PMC7720569/ /pubmed/33287829 http://dx.doi.org/10.1186/s12968-020-00690-4 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/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Martini, Nicola Aimo, Alberto Barison, Andrea Della Latta, Daniele Vergaro, Giuseppe Aquaro, Giovanni Donato Ripoli, Andrea Emdin, Michele Chiappino, Dante Deep learning to diagnose cardiac amyloidosis from cardiovascular magnetic resonance |
title | Deep learning to diagnose cardiac amyloidosis from cardiovascular magnetic resonance |
title_full | Deep learning to diagnose cardiac amyloidosis from cardiovascular magnetic resonance |
title_fullStr | Deep learning to diagnose cardiac amyloidosis from cardiovascular magnetic resonance |
title_full_unstemmed | Deep learning to diagnose cardiac amyloidosis from cardiovascular magnetic resonance |
title_short | Deep learning to diagnose cardiac amyloidosis from cardiovascular magnetic resonance |
title_sort | deep learning to diagnose cardiac amyloidosis from cardiovascular magnetic resonance |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7720569/ https://www.ncbi.nlm.nih.gov/pubmed/33287829 http://dx.doi.org/10.1186/s12968-020-00690-4 |
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