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Atri-U: assisted image analysis in routine cardiovascular magnetic resonance volumetry of the left atrium
BACKGROUND: Artificial intelligence can assist in cardiac image interpretation. Here, we achieved a substantial reduction in time required to read a cardiovascular magnetic resonance (CMR) study to estimate left atrial volume without compromising accuracy or reliability. Rather than deploying a full...
Autores principales: | , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8582149/ https://www.ncbi.nlm.nih.gov/pubmed/34758821 http://dx.doi.org/10.1186/s12968-021-00791-8 |
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author | Anastasopoulos, Constantin Yang, Shan Pradella, Maurice Akinci D’Antonoli, Tugba Knecht, Sven Cyriac, Joshy Reisert, Marco Kellner, Elias Achermann, Rita Haaf, Philip Stieltjes, Bram Sauter, Alexander W. Bremerich, Jens Sommer, Gregor Abdulkadir, Ahmed |
author_facet | Anastasopoulos, Constantin Yang, Shan Pradella, Maurice Akinci D’Antonoli, Tugba Knecht, Sven Cyriac, Joshy Reisert, Marco Kellner, Elias Achermann, Rita Haaf, Philip Stieltjes, Bram Sauter, Alexander W. Bremerich, Jens Sommer, Gregor Abdulkadir, Ahmed |
author_sort | Anastasopoulos, Constantin |
collection | PubMed |
description | BACKGROUND: Artificial intelligence can assist in cardiac image interpretation. Here, we achieved a substantial reduction in time required to read a cardiovascular magnetic resonance (CMR) study to estimate left atrial volume without compromising accuracy or reliability. Rather than deploying a fully automatic black-box, we propose to incorporate the automated LA volumetry into a human-centric interactive image-analysis process. METHODS AND RESULTS: Atri-U, an automated data analysis pipeline for long-axis cardiac cine images, computes the atrial volume by: (i) detecting the end-systolic frame, (ii) outlining the endocardial borders of the LA, (iii) localizing the mitral annular hinge points and constructing the longitudinal atrial diameters, equivalent to the usual workup done by clinicians. In every step human interaction is possible, such that the results provided by the algorithm can be accepted, corrected, or re-done from scratch. Atri-U was trained and evaluated retrospectively on a sample of 300 patients and then applied to a consecutive clinical sample of 150 patients with various heart conditions. The agreement of the indexed LA volume between Atri-U and two experts was similar to the inter-rater agreement between clinicians (average overestimation of 0.8 mL/m(2) with upper and lower limits of agreement of − 7.5 and 5.8 mL/m(2), respectively). An expert cardiologist blinded to the origin of the annotations rated the outputs produced by Atri-U as acceptable in 97% of cases for step (i), 94% for step (ii) and 95% for step (iii), which was slightly lower than the acceptance rate of the outputs produced by a human expert radiologist in the same cases (92%, 100% and 100%, respectively). The assistance of Atri-U lead to an expected reduction in reading time of 66%—from 105 to 34 s, in our in-house clinical setting. CONCLUSIONS: Our proposal enables automated calculation of the maximum LA volume approaching human accuracy and precision. The optional user interaction is possible at each processing step. As such, the assisted process sped up the routine CMR workflow by providing accurate, precise, and validated measurement results. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12968-021-00791-8. |
format | Online Article Text |
id | pubmed-8582149 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-85821492021-11-15 Atri-U: assisted image analysis in routine cardiovascular magnetic resonance volumetry of the left atrium Anastasopoulos, Constantin Yang, Shan Pradella, Maurice Akinci D’Antonoli, Tugba Knecht, Sven Cyriac, Joshy Reisert, Marco Kellner, Elias Achermann, Rita Haaf, Philip Stieltjes, Bram Sauter, Alexander W. Bremerich, Jens Sommer, Gregor Abdulkadir, Ahmed J Cardiovasc Magn Reson Technical Notes BACKGROUND: Artificial intelligence can assist in cardiac image interpretation. Here, we achieved a substantial reduction in time required to read a cardiovascular magnetic resonance (CMR) study to estimate left atrial volume without compromising accuracy or reliability. Rather than deploying a fully automatic black-box, we propose to incorporate the automated LA volumetry into a human-centric interactive image-analysis process. METHODS AND RESULTS: Atri-U, an automated data analysis pipeline for long-axis cardiac cine images, computes the atrial volume by: (i) detecting the end-systolic frame, (ii) outlining the endocardial borders of the LA, (iii) localizing the mitral annular hinge points and constructing the longitudinal atrial diameters, equivalent to the usual workup done by clinicians. In every step human interaction is possible, such that the results provided by the algorithm can be accepted, corrected, or re-done from scratch. Atri-U was trained and evaluated retrospectively on a sample of 300 patients and then applied to a consecutive clinical sample of 150 patients with various heart conditions. The agreement of the indexed LA volume between Atri-U and two experts was similar to the inter-rater agreement between clinicians (average overestimation of 0.8 mL/m(2) with upper and lower limits of agreement of − 7.5 and 5.8 mL/m(2), respectively). An expert cardiologist blinded to the origin of the annotations rated the outputs produced by Atri-U as acceptable in 97% of cases for step (i), 94% for step (ii) and 95% for step (iii), which was slightly lower than the acceptance rate of the outputs produced by a human expert radiologist in the same cases (92%, 100% and 100%, respectively). The assistance of Atri-U lead to an expected reduction in reading time of 66%—from 105 to 34 s, in our in-house clinical setting. CONCLUSIONS: Our proposal enables automated calculation of the maximum LA volume approaching human accuracy and precision. The optional user interaction is possible at each processing step. As such, the assisted process sped up the routine CMR workflow by providing accurate, precise, and validated measurement results. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12968-021-00791-8. BioMed Central 2021-11-11 /pmc/articles/PMC8582149/ /pubmed/34758821 http://dx.doi.org/10.1186/s12968-021-00791-8 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/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/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://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 | Technical Notes Anastasopoulos, Constantin Yang, Shan Pradella, Maurice Akinci D’Antonoli, Tugba Knecht, Sven Cyriac, Joshy Reisert, Marco Kellner, Elias Achermann, Rita Haaf, Philip Stieltjes, Bram Sauter, Alexander W. Bremerich, Jens Sommer, Gregor Abdulkadir, Ahmed Atri-U: assisted image analysis in routine cardiovascular magnetic resonance volumetry of the left atrium |
title | Atri-U: assisted image analysis in routine cardiovascular magnetic resonance volumetry of the left atrium |
title_full | Atri-U: assisted image analysis in routine cardiovascular magnetic resonance volumetry of the left atrium |
title_fullStr | Atri-U: assisted image analysis in routine cardiovascular magnetic resonance volumetry of the left atrium |
title_full_unstemmed | Atri-U: assisted image analysis in routine cardiovascular magnetic resonance volumetry of the left atrium |
title_short | Atri-U: assisted image analysis in routine cardiovascular magnetic resonance volumetry of the left atrium |
title_sort | atri-u: assisted image analysis in routine cardiovascular magnetic resonance volumetry of the left atrium |
topic | Technical Notes |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8582149/ https://www.ncbi.nlm.nih.gov/pubmed/34758821 http://dx.doi.org/10.1186/s12968-021-00791-8 |
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