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Artificial intelligence assisted compositional analyses of human abdominal aortic aneurysms ex vivo

Quantification of histological information from excised human abdominal aortic aneurysm (AAA) specimens may provide essential information on the degree of infiltration of inflammatory cells in different regions of the AAA. Such information will support mechanistic insight in AAA pathology and can be...

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Autores principales: Thorsted, Bjarne, Bjerregaard, Lisette, Jensen, Pia S., Rasmussen, Lars M., Lindholt, Jes S., Bloksgaard, Maria
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9441486/
https://www.ncbi.nlm.nih.gov/pubmed/36072852
http://dx.doi.org/10.3389/fphys.2022.840965
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author Thorsted, Bjarne
Bjerregaard, Lisette
Jensen, Pia S.
Rasmussen, Lars M.
Lindholt, Jes S.
Bloksgaard, Maria
author_facet Thorsted, Bjarne
Bjerregaard, Lisette
Jensen, Pia S.
Rasmussen, Lars M.
Lindholt, Jes S.
Bloksgaard, Maria
author_sort Thorsted, Bjarne
collection PubMed
description Quantification of histological information from excised human abdominal aortic aneurysm (AAA) specimens may provide essential information on the degree of infiltration of inflammatory cells in different regions of the AAA. Such information will support mechanistic insight in AAA pathology and can be linked to clinical measures for further development of AAA treatment regimens. We hypothesize that artificial intelligence can support high throughput analyses of histological sections of excised human AAA. We present an analysis framework based on supervised machine learning. We used TensorFlow and QuPath to determine the overall architecture of the AAA: thrombus, arterial wall, and adventitial loose connective tissue. Within the wall and adventitial zones, the content of collagen, elastin, and specific inflammatory cells was quantified. A deep neural network (DNN) was trained on manually annotated, Weigert stained, tissue sections (14 patients) and validated on images from two other patients. Finally, we applied the method on 95 new patient samples. The DNN was able to segment the sections according to the overall wall architecture with Jaccard coefficients after 65 epocs of 92% for the training and 88% for the validation data set, respectively. Precision and recall both reached 92%. The zone areas were highly variable between patients, as were the outputs on total cell count and elastin/collagen fiber content. The number of specific cells or stained area per zone was deterministically determined. However, combining the masks based on the Weigert stainings, with images of immunostained serial sections requires addition of landmark recognition to the analysis path. The combination of digital pathology, the DNN we developed, and landmark registration will provide a strong tool for future analyses of the histology of excised human AAA. In combination with biomechanical testing and microstructurally motivated mathematical models of AAA remodeling, the method has the potential to be a strong tool to provide mechanistic insight in the disease. In combination with each patients’ demographic and clinical profile, the method can be an interesting tool to in supportof a better treatment regime for the patients.
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spelling pubmed-94414862022-09-06 Artificial intelligence assisted compositional analyses of human abdominal aortic aneurysms ex vivo Thorsted, Bjarne Bjerregaard, Lisette Jensen, Pia S. Rasmussen, Lars M. Lindholt, Jes S. Bloksgaard, Maria Front Physiol Physiology Quantification of histological information from excised human abdominal aortic aneurysm (AAA) specimens may provide essential information on the degree of infiltration of inflammatory cells in different regions of the AAA. Such information will support mechanistic insight in AAA pathology and can be linked to clinical measures for further development of AAA treatment regimens. We hypothesize that artificial intelligence can support high throughput analyses of histological sections of excised human AAA. We present an analysis framework based on supervised machine learning. We used TensorFlow and QuPath to determine the overall architecture of the AAA: thrombus, arterial wall, and adventitial loose connective tissue. Within the wall and adventitial zones, the content of collagen, elastin, and specific inflammatory cells was quantified. A deep neural network (DNN) was trained on manually annotated, Weigert stained, tissue sections (14 patients) and validated on images from two other patients. Finally, we applied the method on 95 new patient samples. The DNN was able to segment the sections according to the overall wall architecture with Jaccard coefficients after 65 epocs of 92% for the training and 88% for the validation data set, respectively. Precision and recall both reached 92%. The zone areas were highly variable between patients, as were the outputs on total cell count and elastin/collagen fiber content. The number of specific cells or stained area per zone was deterministically determined. However, combining the masks based on the Weigert stainings, with images of immunostained serial sections requires addition of landmark recognition to the analysis path. The combination of digital pathology, the DNN we developed, and landmark registration will provide a strong tool for future analyses of the histology of excised human AAA. In combination with biomechanical testing and microstructurally motivated mathematical models of AAA remodeling, the method has the potential to be a strong tool to provide mechanistic insight in the disease. In combination with each patients’ demographic and clinical profile, the method can be an interesting tool to in supportof a better treatment regime for the patients. Frontiers Media S.A. 2022-08-22 /pmc/articles/PMC9441486/ /pubmed/36072852 http://dx.doi.org/10.3389/fphys.2022.840965 Text en Copyright © 2022 Thorsted, Bjerregaard, Jensen, Rasmussen, Lindholt and Bloksgaard. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Physiology
Thorsted, Bjarne
Bjerregaard, Lisette
Jensen, Pia S.
Rasmussen, Lars M.
Lindholt, Jes S.
Bloksgaard, Maria
Artificial intelligence assisted compositional analyses of human abdominal aortic aneurysms ex vivo
title Artificial intelligence assisted compositional analyses of human abdominal aortic aneurysms ex vivo
title_full Artificial intelligence assisted compositional analyses of human abdominal aortic aneurysms ex vivo
title_fullStr Artificial intelligence assisted compositional analyses of human abdominal aortic aneurysms ex vivo
title_full_unstemmed Artificial intelligence assisted compositional analyses of human abdominal aortic aneurysms ex vivo
title_short Artificial intelligence assisted compositional analyses of human abdominal aortic aneurysms ex vivo
title_sort artificial intelligence assisted compositional analyses of human abdominal aortic aneurysms ex vivo
topic Physiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9441486/
https://www.ncbi.nlm.nih.gov/pubmed/36072852
http://dx.doi.org/10.3389/fphys.2022.840965
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