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Medical decision support system using weakly-labeled lung CT scans
PURPOSE: Determination and development of an effective set of models leveraging Artificial Intelligence techniques to generate a system able to support clinical practitioners working with COVID-19 patients. It involves a pipeline including classification, lung and lesion segmentation, as well as les...
Autores principales: | , , , , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9554434/ https://www.ncbi.nlm.nih.gov/pubmed/36248019 http://dx.doi.org/10.3389/fmedt.2022.980735 |
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author | Murillo-González, Alejandro González, David Jaramillo, Laura Galeano, Carlos Tavera, Fabby Mejía, Marcia Hernández, Alejandro Rivera, David Restrepo Paniagua, J. G. Ariza-Jiménez, Leandro Garcés Echeverri, José Julián Diaz León, Christian Andrés Serna-Higuita, Diana Lucia Barrios, Wayner Arrázola, Wiston Mejía, Miguel Ángel Arango, Sebastián Marín Ramírez, Daniela Salinas-Miranda, Emmanuel Quintero, O. L. |
author_facet | Murillo-González, Alejandro González, David Jaramillo, Laura Galeano, Carlos Tavera, Fabby Mejía, Marcia Hernández, Alejandro Rivera, David Restrepo Paniagua, J. G. Ariza-Jiménez, Leandro Garcés Echeverri, José Julián Diaz León, Christian Andrés Serna-Higuita, Diana Lucia Barrios, Wayner Arrázola, Wiston Mejía, Miguel Ángel Arango, Sebastián Marín Ramírez, Daniela Salinas-Miranda, Emmanuel Quintero, O. L. |
author_sort | Murillo-González, Alejandro |
collection | PubMed |
description | PURPOSE: Determination and development of an effective set of models leveraging Artificial Intelligence techniques to generate a system able to support clinical practitioners working with COVID-19 patients. It involves a pipeline including classification, lung and lesion segmentation, as well as lesion quantification of axial lung CT studies. APPROACH: A deep neural network architecture based on DenseNet is introduced for the classification of weakly-labeled, variable-sized (and possibly sparse) axial lung CT scans. The models are trained and tested on aggregated, publicly available data sets with over 10 categories. To further assess the models, a data set was collected from multiple medical institutions in Colombia, which includes healthy, COVID-19 and patients with other diseases. It is composed of 1,322 CT studies from a diverse set of CT machines and institutions that make over 550,000 slices. Each CT study was labeled based on a clinical test, and no per-slice annotation took place. This enabled a classification into Normal vs. Abnormal patients, and for those that were considered abnormal, an extra classification step into Abnormal (other diseases) vs. COVID-19. Additionally, the pipeline features a methodology to segment and quantify lesions of COVID-19 patients on the complete CT study, enabling easier localization and progress tracking. Moreover, multiple ablation studies were performed to appropriately assess the elements composing the classification pipeline. RESULTS: The best performing lung CT study classification models achieved 0.83 accuracy, 0.79 sensitivity, 0.87 specificity, 0.82 F1 score and 0.85 precision for the Normal vs. Abnormal task. For the Abnormal vs COVID-19 task, the model obtained 0.86 accuracy, 0.81 sensitivity, 0.91 specificity, 0.84 F1 score and 0.88 precision. The ablation studies showed that using the complete CT study in the pipeline resulted in greater classification performance, restating that relevant COVID-19 patterns cannot be ignored towards the top and bottom of the lung volume. DISCUSSION: The lung CT classification architecture introduced has shown that it can handle weakly-labeled, variable-sized and possibly sparse axial lung studies, reducing the need for expert annotations at a per-slice level. CONCLUSIONS: This work presents a working methodology that can guide the development of decision support systems for clinical reasoning in future interventionist or prospective studies. |
format | Online Article Text |
id | pubmed-9554434 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95544342022-10-13 Medical decision support system using weakly-labeled lung CT scans Murillo-González, Alejandro González, David Jaramillo, Laura Galeano, Carlos Tavera, Fabby Mejía, Marcia Hernández, Alejandro Rivera, David Restrepo Paniagua, J. G. Ariza-Jiménez, Leandro Garcés Echeverri, José Julián Diaz León, Christian Andrés Serna-Higuita, Diana Lucia Barrios, Wayner Arrázola, Wiston Mejía, Miguel Ángel Arango, Sebastián Marín Ramírez, Daniela Salinas-Miranda, Emmanuel Quintero, O. L. Front Med Technol Medical Technology PURPOSE: Determination and development of an effective set of models leveraging Artificial Intelligence techniques to generate a system able to support clinical practitioners working with COVID-19 patients. It involves a pipeline including classification, lung and lesion segmentation, as well as lesion quantification of axial lung CT studies. APPROACH: A deep neural network architecture based on DenseNet is introduced for the classification of weakly-labeled, variable-sized (and possibly sparse) axial lung CT scans. The models are trained and tested on aggregated, publicly available data sets with over 10 categories. To further assess the models, a data set was collected from multiple medical institutions in Colombia, which includes healthy, COVID-19 and patients with other diseases. It is composed of 1,322 CT studies from a diverse set of CT machines and institutions that make over 550,000 slices. Each CT study was labeled based on a clinical test, and no per-slice annotation took place. This enabled a classification into Normal vs. Abnormal patients, and for those that were considered abnormal, an extra classification step into Abnormal (other diseases) vs. COVID-19. Additionally, the pipeline features a methodology to segment and quantify lesions of COVID-19 patients on the complete CT study, enabling easier localization and progress tracking. Moreover, multiple ablation studies were performed to appropriately assess the elements composing the classification pipeline. RESULTS: The best performing lung CT study classification models achieved 0.83 accuracy, 0.79 sensitivity, 0.87 specificity, 0.82 F1 score and 0.85 precision for the Normal vs. Abnormal task. For the Abnormal vs COVID-19 task, the model obtained 0.86 accuracy, 0.81 sensitivity, 0.91 specificity, 0.84 F1 score and 0.88 precision. The ablation studies showed that using the complete CT study in the pipeline resulted in greater classification performance, restating that relevant COVID-19 patterns cannot be ignored towards the top and bottom of the lung volume. DISCUSSION: The lung CT classification architecture introduced has shown that it can handle weakly-labeled, variable-sized and possibly sparse axial lung studies, reducing the need for expert annotations at a per-slice level. CONCLUSIONS: This work presents a working methodology that can guide the development of decision support systems for clinical reasoning in future interventionist or prospective studies. Frontiers Media S.A. 2022-09-28 /pmc/articles/PMC9554434/ /pubmed/36248019 http://dx.doi.org/10.3389/fmedt.2022.980735 Text en © 2022 Murillo-González, González, Jaramillo, Galeano, Tavera, Mejía, Hernández, Rivera, Paniagua, Ariza-Jiménez, Garcés Echeverri, Diaz León, Serna-Higuita, Barrios, Arrázola, Mejía, Arango, Marín Ramírez, Salinas-Miranda and Quintero. 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) (https://creativecommons.org/licenses/by/4.0/) . 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 | Medical Technology Murillo-González, Alejandro González, David Jaramillo, Laura Galeano, Carlos Tavera, Fabby Mejía, Marcia Hernández, Alejandro Rivera, David Restrepo Paniagua, J. G. Ariza-Jiménez, Leandro Garcés Echeverri, José Julián Diaz León, Christian Andrés Serna-Higuita, Diana Lucia Barrios, Wayner Arrázola, Wiston Mejía, Miguel Ángel Arango, Sebastián Marín Ramírez, Daniela Salinas-Miranda, Emmanuel Quintero, O. L. Medical decision support system using weakly-labeled lung CT scans |
title | Medical decision support system using weakly-labeled lung CT scans |
title_full | Medical decision support system using weakly-labeled lung CT scans |
title_fullStr | Medical decision support system using weakly-labeled lung CT scans |
title_full_unstemmed | Medical decision support system using weakly-labeled lung CT scans |
title_short | Medical decision support system using weakly-labeled lung CT scans |
title_sort | medical decision support system using weakly-labeled lung ct scans |
topic | Medical Technology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9554434/ https://www.ncbi.nlm.nih.gov/pubmed/36248019 http://dx.doi.org/10.3389/fmedt.2022.980735 |
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