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Superiority of Supervised Machine Learning on Reading Chest X-Rays in Intensive Care Units
Purpose: Portable chest radiographs are diagnostically indispensable in intensive care units (ICU). This study aimed to determine if the proposed machine learning technique increased in accuracy as the number of radiograph readings increased and if it was accurate in a clinical setting. Methods: Two...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8554032/ https://www.ncbi.nlm.nih.gov/pubmed/34722558 http://dx.doi.org/10.3389/fmed.2021.676277 |
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author | Tanaka, Kumiko Nakada, Taka-aki Takahashi, Nozomi Dozono, Takahiro Yoshimura, Yuichiro Yokota, Hajime Horikoshi, Takuro Nakaguchi, Toshiya Shinozaki, Koichiro |
author_facet | Tanaka, Kumiko Nakada, Taka-aki Takahashi, Nozomi Dozono, Takahiro Yoshimura, Yuichiro Yokota, Hajime Horikoshi, Takuro Nakaguchi, Toshiya Shinozaki, Koichiro |
author_sort | Tanaka, Kumiko |
collection | PubMed |
description | Purpose: Portable chest radiographs are diagnostically indispensable in intensive care units (ICU). This study aimed to determine if the proposed machine learning technique increased in accuracy as the number of radiograph readings increased and if it was accurate in a clinical setting. Methods: Two independent data sets of portable chest radiographs (n = 380, a single Japanese hospital; n = 1,720, The National Institution of Health [NIH] ChestX-ray8 dataset) were analyzed. Each data set was divided training data and study data. Images were classified as atelectasis, pleural effusion, pneumonia, or no emergency. DenseNet-121, as a pre-trained deep convolutional neural network was used and ensemble learning was performed on the best-performing algorithms. Diagnostic accuracy and processing time were compared to those of ICU physicians. Results: In the single Japanese hospital data, the area under the curve (AUC) of diagnostic accuracy was 0.768. The area under the curve (AUC) of diagnostic accuracy significantly improved as the number of radiograph readings increased from 25 to 100% in the NIH data set. The AUC was higher than 0.9 for all categories toward the end of training with a large sample size. The time to complete 53 radiographs by machine learning was 70 times faster than the time taken by ICU physicians (9.66 s vs. 12 min). The diagnostic accuracy was higher by machine learning than by ICU physicians in most categories (atelectasis, AUC 0.744 vs. 0.555, P < 0.05; pleural effusion, 0.856 vs. 0.706, P < 0.01; pneumonia, 0.720 vs. 0.744, P = 0.88; no emergency, 0.751 vs. 0.698, P = 0.47). Conclusions: We developed an automatic detection system for portable chest radiographs in ICU setting; its performance was superior and quite faster than ICU physicians. |
format | Online Article Text |
id | pubmed-8554032 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-85540322021-10-30 Superiority of Supervised Machine Learning on Reading Chest X-Rays in Intensive Care Units Tanaka, Kumiko Nakada, Taka-aki Takahashi, Nozomi Dozono, Takahiro Yoshimura, Yuichiro Yokota, Hajime Horikoshi, Takuro Nakaguchi, Toshiya Shinozaki, Koichiro Front Med (Lausanne) Medicine Purpose: Portable chest radiographs are diagnostically indispensable in intensive care units (ICU). This study aimed to determine if the proposed machine learning technique increased in accuracy as the number of radiograph readings increased and if it was accurate in a clinical setting. Methods: Two independent data sets of portable chest radiographs (n = 380, a single Japanese hospital; n = 1,720, The National Institution of Health [NIH] ChestX-ray8 dataset) were analyzed. Each data set was divided training data and study data. Images were classified as atelectasis, pleural effusion, pneumonia, or no emergency. DenseNet-121, as a pre-trained deep convolutional neural network was used and ensemble learning was performed on the best-performing algorithms. Diagnostic accuracy and processing time were compared to those of ICU physicians. Results: In the single Japanese hospital data, the area under the curve (AUC) of diagnostic accuracy was 0.768. The area under the curve (AUC) of diagnostic accuracy significantly improved as the number of radiograph readings increased from 25 to 100% in the NIH data set. The AUC was higher than 0.9 for all categories toward the end of training with a large sample size. The time to complete 53 radiographs by machine learning was 70 times faster than the time taken by ICU physicians (9.66 s vs. 12 min). The diagnostic accuracy was higher by machine learning than by ICU physicians in most categories (atelectasis, AUC 0.744 vs. 0.555, P < 0.05; pleural effusion, 0.856 vs. 0.706, P < 0.01; pneumonia, 0.720 vs. 0.744, P = 0.88; no emergency, 0.751 vs. 0.698, P = 0.47). Conclusions: We developed an automatic detection system for portable chest radiographs in ICU setting; its performance was superior and quite faster than ICU physicians. Frontiers Media S.A. 2021-10-15 /pmc/articles/PMC8554032/ /pubmed/34722558 http://dx.doi.org/10.3389/fmed.2021.676277 Text en Copyright © 2021 Tanaka, Nakada, Takahashi, Dozono, Yoshimura, Yokota, Horikoshi, Nakaguchi and Shinozaki. 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 | Medicine Tanaka, Kumiko Nakada, Taka-aki Takahashi, Nozomi Dozono, Takahiro Yoshimura, Yuichiro Yokota, Hajime Horikoshi, Takuro Nakaguchi, Toshiya Shinozaki, Koichiro Superiority of Supervised Machine Learning on Reading Chest X-Rays in Intensive Care Units |
title | Superiority of Supervised Machine Learning on Reading Chest X-Rays in Intensive Care Units |
title_full | Superiority of Supervised Machine Learning on Reading Chest X-Rays in Intensive Care Units |
title_fullStr | Superiority of Supervised Machine Learning on Reading Chest X-Rays in Intensive Care Units |
title_full_unstemmed | Superiority of Supervised Machine Learning on Reading Chest X-Rays in Intensive Care Units |
title_short | Superiority of Supervised Machine Learning on Reading Chest X-Rays in Intensive Care Units |
title_sort | superiority of supervised machine learning on reading chest x-rays in intensive care units |
topic | Medicine |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8554032/ https://www.ncbi.nlm.nih.gov/pubmed/34722558 http://dx.doi.org/10.3389/fmed.2021.676277 |
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