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Quantitative Measurement of Pneumothorax Using Artificial Intelligence Management Model and Clinical Application

Artificial intelligence (AI) techniques can be a solution for delayed or misdiagnosed pneumothorax. This study developed, a deep-learning-based AI model to estimate the pneumothorax amount on a chest radiograph and applied it to a treatment algorithm developed by experienced thoracic surgeons. U-net...

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Autores principales: Kim, Dohun, Lee, Jae-Hyeok, Kim, Si-Wook, Hong, Jong-Myeon, Kim, Sung-Jin, Song, Minji, Choi, Jong-Mun, Lee, Sun-Yeop, Yoon, Hongjun, Yoo, Jin-Young
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9406694/
https://www.ncbi.nlm.nih.gov/pubmed/36010174
http://dx.doi.org/10.3390/diagnostics12081823
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author Kim, Dohun
Lee, Jae-Hyeok
Kim, Si-Wook
Hong, Jong-Myeon
Kim, Sung-Jin
Song, Minji
Choi, Jong-Mun
Lee, Sun-Yeop
Yoon, Hongjun
Yoo, Jin-Young
author_facet Kim, Dohun
Lee, Jae-Hyeok
Kim, Si-Wook
Hong, Jong-Myeon
Kim, Sung-Jin
Song, Minji
Choi, Jong-Mun
Lee, Sun-Yeop
Yoon, Hongjun
Yoo, Jin-Young
author_sort Kim, Dohun
collection PubMed
description Artificial intelligence (AI) techniques can be a solution for delayed or misdiagnosed pneumothorax. This study developed, a deep-learning-based AI model to estimate the pneumothorax amount on a chest radiograph and applied it to a treatment algorithm developed by experienced thoracic surgeons. U-net performed semantic segmentation and classification of pneumothorax and non-pneumothorax areas. The pneumothorax amount was measured using chest computed tomography (volume ratio, gold standard) and chest radiographs (area ratio, true label) and calculated using the AI model (area ratio, predicted label). Each value was compared and analyzed based on clinical outcomes. The study included 96 patients, of which 67 comprised the training set and the others the test set. The AI model showed an accuracy of 97.8%, sensitivity of 69.2%, a negative predictive value of 99.1%, and a dice similarity coefficient of 61.8%. In the test set, the average amount of pneumothorax was 15%, 16%, and 13% in the gold standard, predicted, and true labels, respectively. The predicted label was not significantly different from the gold standard (p = 0.11) but inferior to the true label (difference in MAE: 3.03%). The amount of pneumothorax in thoracostomy patients was 21.6% in predicted cases and 18.5% in true cases.
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spelling pubmed-94066942022-08-26 Quantitative Measurement of Pneumothorax Using Artificial Intelligence Management Model and Clinical Application Kim, Dohun Lee, Jae-Hyeok Kim, Si-Wook Hong, Jong-Myeon Kim, Sung-Jin Song, Minji Choi, Jong-Mun Lee, Sun-Yeop Yoon, Hongjun Yoo, Jin-Young Diagnostics (Basel) Article Artificial intelligence (AI) techniques can be a solution for delayed or misdiagnosed pneumothorax. This study developed, a deep-learning-based AI model to estimate the pneumothorax amount on a chest radiograph and applied it to a treatment algorithm developed by experienced thoracic surgeons. U-net performed semantic segmentation and classification of pneumothorax and non-pneumothorax areas. The pneumothorax amount was measured using chest computed tomography (volume ratio, gold standard) and chest radiographs (area ratio, true label) and calculated using the AI model (area ratio, predicted label). Each value was compared and analyzed based on clinical outcomes. The study included 96 patients, of which 67 comprised the training set and the others the test set. The AI model showed an accuracy of 97.8%, sensitivity of 69.2%, a negative predictive value of 99.1%, and a dice similarity coefficient of 61.8%. In the test set, the average amount of pneumothorax was 15%, 16%, and 13% in the gold standard, predicted, and true labels, respectively. The predicted label was not significantly different from the gold standard (p = 0.11) but inferior to the true label (difference in MAE: 3.03%). The amount of pneumothorax in thoracostomy patients was 21.6% in predicted cases and 18.5% in true cases. MDPI 2022-07-29 /pmc/articles/PMC9406694/ /pubmed/36010174 http://dx.doi.org/10.3390/diagnostics12081823 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
Kim, Dohun
Lee, Jae-Hyeok
Kim, Si-Wook
Hong, Jong-Myeon
Kim, Sung-Jin
Song, Minji
Choi, Jong-Mun
Lee, Sun-Yeop
Yoon, Hongjun
Yoo, Jin-Young
Quantitative Measurement of Pneumothorax Using Artificial Intelligence Management Model and Clinical Application
title Quantitative Measurement of Pneumothorax Using Artificial Intelligence Management Model and Clinical Application
title_full Quantitative Measurement of Pneumothorax Using Artificial Intelligence Management Model and Clinical Application
title_fullStr Quantitative Measurement of Pneumothorax Using Artificial Intelligence Management Model and Clinical Application
title_full_unstemmed Quantitative Measurement of Pneumothorax Using Artificial Intelligence Management Model and Clinical Application
title_short Quantitative Measurement of Pneumothorax Using Artificial Intelligence Management Model and Clinical Application
title_sort quantitative measurement of pneumothorax using artificial intelligence management model and clinical application
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9406694/
https://www.ncbi.nlm.nih.gov/pubmed/36010174
http://dx.doi.org/10.3390/diagnostics12081823
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