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Radiologists can visually predict mortality risk based on the gestalt of chest radiographs comparable to a deep learning network
Deep learning convolutional neural network (CNN) can predict mortality from chest radiographs, yet, it is unknown whether radiologists can perform the same task. Here, we investigate whether radiologists can visually assess image gestalt (defined as deviation from an unremarkable chest radiograph as...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8486799/ https://www.ncbi.nlm.nih.gov/pubmed/34599265 http://dx.doi.org/10.1038/s41598-021-99107-0 |
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author | Weiss, Jakob Taron, Jana Jin, Zexi Mayrhofer, Thomas Aerts, Hugo J. W. L. Lu, Michael T. Hoffmann, Udo |
author_facet | Weiss, Jakob Taron, Jana Jin, Zexi Mayrhofer, Thomas Aerts, Hugo J. W. L. Lu, Michael T. Hoffmann, Udo |
author_sort | Weiss, Jakob |
collection | PubMed |
description | Deep learning convolutional neural network (CNN) can predict mortality from chest radiographs, yet, it is unknown whether radiologists can perform the same task. Here, we investigate whether radiologists can visually assess image gestalt (defined as deviation from an unremarkable chest radiograph associated with the likelihood of 6-year mortality) of a chest radiograph to predict 6-year mortality. The assessment was validated in an independent testing dataset and compared to the performance of a CNN developed for mortality prediction. Results are reported for the testing dataset only (n = 100; age 62.5 ± 5.2; male 55%, event rate 50%). The probability of 6-year mortality based on image gestalt had high accuracy (AUC: 0.68 (95% CI 0.58–0.78), similar to that of the CNN (AUC: 0.67 (95% CI 0.57–0.77); p = 0.90). Patients with high/very high image gestalt ratings were significantly more likely to die when compared to those rated as very low (p ≤ 0.04). Assignment to risk categories was not explained by patient characteristics or traditional risk factors and imaging findings (p ≥ 0.2). In conclusion, assessing image gestalt on chest radiographs by radiologists renders high prognostic accuracy for the probability of mortality, similar to that of a specifically trained CNN. Further studies are warranted to confirm this concept and to determine potential clinical benefits. |
format | Online Article Text |
id | pubmed-8486799 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-84867992021-10-04 Radiologists can visually predict mortality risk based on the gestalt of chest radiographs comparable to a deep learning network Weiss, Jakob Taron, Jana Jin, Zexi Mayrhofer, Thomas Aerts, Hugo J. W. L. Lu, Michael T. Hoffmann, Udo Sci Rep Article Deep learning convolutional neural network (CNN) can predict mortality from chest radiographs, yet, it is unknown whether radiologists can perform the same task. Here, we investigate whether radiologists can visually assess image gestalt (defined as deviation from an unremarkable chest radiograph associated with the likelihood of 6-year mortality) of a chest radiograph to predict 6-year mortality. The assessment was validated in an independent testing dataset and compared to the performance of a CNN developed for mortality prediction. Results are reported for the testing dataset only (n = 100; age 62.5 ± 5.2; male 55%, event rate 50%). The probability of 6-year mortality based on image gestalt had high accuracy (AUC: 0.68 (95% CI 0.58–0.78), similar to that of the CNN (AUC: 0.67 (95% CI 0.57–0.77); p = 0.90). Patients with high/very high image gestalt ratings were significantly more likely to die when compared to those rated as very low (p ≤ 0.04). Assignment to risk categories was not explained by patient characteristics or traditional risk factors and imaging findings (p ≥ 0.2). In conclusion, assessing image gestalt on chest radiographs by radiologists renders high prognostic accuracy for the probability of mortality, similar to that of a specifically trained CNN. Further studies are warranted to confirm this concept and to determine potential clinical benefits. Nature Publishing Group UK 2021-10-01 /pmc/articles/PMC8486799/ /pubmed/34599265 http://dx.doi.org/10.1038/s41598-021-99107-0 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Weiss, Jakob Taron, Jana Jin, Zexi Mayrhofer, Thomas Aerts, Hugo J. W. L. Lu, Michael T. Hoffmann, Udo Radiologists can visually predict mortality risk based on the gestalt of chest radiographs comparable to a deep learning network |
title | Radiologists can visually predict mortality risk based on the gestalt of chest radiographs comparable to a deep learning network |
title_full | Radiologists can visually predict mortality risk based on the gestalt of chest radiographs comparable to a deep learning network |
title_fullStr | Radiologists can visually predict mortality risk based on the gestalt of chest radiographs comparable to a deep learning network |
title_full_unstemmed | Radiologists can visually predict mortality risk based on the gestalt of chest radiographs comparable to a deep learning network |
title_short | Radiologists can visually predict mortality risk based on the gestalt of chest radiographs comparable to a deep learning network |
title_sort | radiologists can visually predict mortality risk based on the gestalt of chest radiographs comparable to a deep learning network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8486799/ https://www.ncbi.nlm.nih.gov/pubmed/34599265 http://dx.doi.org/10.1038/s41598-021-99107-0 |
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