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Automated Detection, Segmentation, and Classification of Pericardial Effusions on Chest CT Using a Deep Convolutional Neural Network
Pericardial effusions (PEFs) are often missed on Computed Tomography (CT), which particularly affects the outcome of patients presenting with hemodynamic compromise. An automatic PEF detection, segmentation, and classification tool would expedite and improve CT based PEF diagnosis; 258 CTs with (206...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9139725/ https://www.ncbi.nlm.nih.gov/pubmed/35626201 http://dx.doi.org/10.3390/diagnostics12051045 |
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author | Wilder-Smith, Adrian Jonathan Yang, Shan Weikert, Thomas Bremerich, Jens Haaf, Philip Segeroth, Martin Ebert, Lars C. Sauter, Alexander Sexauer, Raphael |
author_facet | Wilder-Smith, Adrian Jonathan Yang, Shan Weikert, Thomas Bremerich, Jens Haaf, Philip Segeroth, Martin Ebert, Lars C. Sauter, Alexander Sexauer, Raphael |
author_sort | Wilder-Smith, Adrian Jonathan |
collection | PubMed |
description | Pericardial effusions (PEFs) are often missed on Computed Tomography (CT), which particularly affects the outcome of patients presenting with hemodynamic compromise. An automatic PEF detection, segmentation, and classification tool would expedite and improve CT based PEF diagnosis; 258 CTs with (206 with simple PEF, 52 with hemopericardium) and without PEF (each 134 with contrast, 124 non-enhanced) were identified using the radiology report (01/2016–01/2021). PEF were manually 3D-segmented. A deep convolutional neural network (nnU-Net) was trained on 316 cases and separately tested on the remaining 200 and 22 external post-mortem CTs. Inter-reader variability was tested on 40 CTs. PEF classification utilized the median Hounsfield unit from each prediction. The sensitivity and specificity for PEF detection was 97% (95% CI 91.48–99.38%) and 100.00% (95% CI 96.38–100.00%) and 89.74% and 83.61% for diagnosing hemopericardium (AUC 0.944, 95% CI 0.904–0.984). Model performance (Dice coefficient: 0.75 ± 0.01) was non-inferior to inter-reader (0.69 ± 0.02) and was unaffected by contrast administration nor alternative chest pathology (p > 0.05). External dataset testing yielded similar results. Our model reliably detects, segments, and classifies PEF on CT in a complex dataset, potentially serving as an alert tool whilst enhancing report quality. The model and corresponding datasets are publicly available. |
format | Online Article Text |
id | pubmed-9139725 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-91397252022-05-28 Automated Detection, Segmentation, and Classification of Pericardial Effusions on Chest CT Using a Deep Convolutional Neural Network Wilder-Smith, Adrian Jonathan Yang, Shan Weikert, Thomas Bremerich, Jens Haaf, Philip Segeroth, Martin Ebert, Lars C. Sauter, Alexander Sexauer, Raphael Diagnostics (Basel) Article Pericardial effusions (PEFs) are often missed on Computed Tomography (CT), which particularly affects the outcome of patients presenting with hemodynamic compromise. An automatic PEF detection, segmentation, and classification tool would expedite and improve CT based PEF diagnosis; 258 CTs with (206 with simple PEF, 52 with hemopericardium) and without PEF (each 134 with contrast, 124 non-enhanced) were identified using the radiology report (01/2016–01/2021). PEF were manually 3D-segmented. A deep convolutional neural network (nnU-Net) was trained on 316 cases and separately tested on the remaining 200 and 22 external post-mortem CTs. Inter-reader variability was tested on 40 CTs. PEF classification utilized the median Hounsfield unit from each prediction. The sensitivity and specificity for PEF detection was 97% (95% CI 91.48–99.38%) and 100.00% (95% CI 96.38–100.00%) and 89.74% and 83.61% for diagnosing hemopericardium (AUC 0.944, 95% CI 0.904–0.984). Model performance (Dice coefficient: 0.75 ± 0.01) was non-inferior to inter-reader (0.69 ± 0.02) and was unaffected by contrast administration nor alternative chest pathology (p > 0.05). External dataset testing yielded similar results. Our model reliably detects, segments, and classifies PEF on CT in a complex dataset, potentially serving as an alert tool whilst enhancing report quality. The model and corresponding datasets are publicly available. MDPI 2022-04-21 /pmc/articles/PMC9139725/ /pubmed/35626201 http://dx.doi.org/10.3390/diagnostics12051045 Text en © 2022 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 Wilder-Smith, Adrian Jonathan Yang, Shan Weikert, Thomas Bremerich, Jens Haaf, Philip Segeroth, Martin Ebert, Lars C. Sauter, Alexander Sexauer, Raphael Automated Detection, Segmentation, and Classification of Pericardial Effusions on Chest CT Using a Deep Convolutional Neural Network |
title | Automated Detection, Segmentation, and Classification of Pericardial Effusions on Chest CT Using a Deep Convolutional Neural Network |
title_full | Automated Detection, Segmentation, and Classification of Pericardial Effusions on Chest CT Using a Deep Convolutional Neural Network |
title_fullStr | Automated Detection, Segmentation, and Classification of Pericardial Effusions on Chest CT Using a Deep Convolutional Neural Network |
title_full_unstemmed | Automated Detection, Segmentation, and Classification of Pericardial Effusions on Chest CT Using a Deep Convolutional Neural Network |
title_short | Automated Detection, Segmentation, and Classification of Pericardial Effusions on Chest CT Using a Deep Convolutional Neural Network |
title_sort | automated detection, segmentation, and classification of pericardial effusions on chest ct using a deep convolutional neural network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9139725/ https://www.ncbi.nlm.nih.gov/pubmed/35626201 http://dx.doi.org/10.3390/diagnostics12051045 |
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