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Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs

IMPORTANCE: Early detection of pneumothorax, most often via chest radiography, can help determine need for emergent clinical intervention. The ability to accurately detect and rapidly triage pneumothorax with an artificial intelligence (AI) model could assist with earlier identification and improve...

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Autores principales: Hillis, James M., Bizzo, Bernardo C., Mercaldo, Sarah, Chin, John K., Newbury-Chaet, Isabella, Digumarthy, Subba R., Gilman, Matthew D., Muse, Victorine V., Bottrell, Georgie, Seah, Jarrel C.Y., Jones, Catherine M., Kalra, Mannudeep K., Dreyer, Keith J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Medical Association 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9856508/
https://www.ncbi.nlm.nih.gov/pubmed/36520432
http://dx.doi.org/10.1001/jamanetworkopen.2022.47172
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author Hillis, James M.
Bizzo, Bernardo C.
Mercaldo, Sarah
Chin, John K.
Newbury-Chaet, Isabella
Digumarthy, Subba R.
Gilman, Matthew D.
Muse, Victorine V.
Bottrell, Georgie
Seah, Jarrel C.Y.
Jones, Catherine M.
Kalra, Mannudeep K.
Dreyer, Keith J.
author_facet Hillis, James M.
Bizzo, Bernardo C.
Mercaldo, Sarah
Chin, John K.
Newbury-Chaet, Isabella
Digumarthy, Subba R.
Gilman, Matthew D.
Muse, Victorine V.
Bottrell, Georgie
Seah, Jarrel C.Y.
Jones, Catherine M.
Kalra, Mannudeep K.
Dreyer, Keith J.
author_sort Hillis, James M.
collection PubMed
description IMPORTANCE: Early detection of pneumothorax, most often via chest radiography, can help determine need for emergent clinical intervention. The ability to accurately detect and rapidly triage pneumothorax with an artificial intelligence (AI) model could assist with earlier identification and improve care. OBJECTIVE: To compare the accuracy of an AI model vs consensus thoracic radiologist interpretations in detecting any pneumothorax (incorporating both nontension and tension pneumothorax) and tension pneumothorax. DESIGN, SETTING, AND PARTICIPANTS: This diagnostic study was a retrospective standalone performance assessment using a data set of 1000 chest radiographs captured between June 1, 2015, and May 31, 2021. The radiographs were obtained from patients aged at least 18 years at 4 hospitals in the Mass General Brigham hospital network in the United States. Included radiographs were selected using 2 strategies from all chest radiography performed at the hospitals, including inpatient and outpatient. The first strategy identified consecutive radiographs with pneumothorax through a manual review of radiology reports, and the second strategy identified consecutive radiographs with tension pneumothorax using natural language processing. For both strategies, negative radiographs were selected by taking the next negative radiograph acquired from the same radiography machine as each positive radiograph. The final data set was an amalgamation of these processes. Each radiograph was interpreted independently by up to 3 radiologists to establish consensus ground-truth interpretations. Each radiograph was then interpreted by the AI model for the presence of pneumothorax and tension pneumothorax. This study was conducted between July and October 2021, with the primary analysis performed between October and November 2021. MAIN OUTCOMES AND MEASURES: The primary end points were the areas under the receiver operating characteristic curves (AUCs) for the detection of pneumothorax and tension pneumothorax. The secondary end points were the sensitivities and specificities for the detection of pneumothorax and tension pneumothorax. RESULTS: The final analysis included radiographs from 985 patients (mean [SD] age, 60.8 [19.0] years; 436 [44.3%] female patients), including 307 patients with nontension pneumothorax, 128 patients with tension pneumothorax, and 550 patients without pneumothorax. The AI model detected any pneumothorax with an AUC of 0.979 (95% CI, 0.970-0.987), sensitivity of 94.3% (95% CI, 92.0%-96.3%), and specificity of 92.0% (95% CI, 89.6%-94.2%) and tension pneumothorax with an AUC of 0.987 (95% CI, 0.980-0.992), sensitivity of 94.5% (95% CI, 90.6%-97.7%), and specificity of 95.3% (95% CI, 93.9%-96.6%). CONCLUSIONS AND RELEVANCE: These findings suggest that the assessed AI model accurately detected pneumothorax and tension pneumothorax in this chest radiograph data set. The model’s use in the clinical workflow could lead to earlier identification and improved care for patients with pneumothorax.
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spelling pubmed-98565082023-02-03 Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs Hillis, James M. Bizzo, Bernardo C. Mercaldo, Sarah Chin, John K. Newbury-Chaet, Isabella Digumarthy, Subba R. Gilman, Matthew D. Muse, Victorine V. Bottrell, Georgie Seah, Jarrel C.Y. Jones, Catherine M. Kalra, Mannudeep K. Dreyer, Keith J. JAMA Netw Open Original Investigation IMPORTANCE: Early detection of pneumothorax, most often via chest radiography, can help determine need for emergent clinical intervention. The ability to accurately detect and rapidly triage pneumothorax with an artificial intelligence (AI) model could assist with earlier identification and improve care. OBJECTIVE: To compare the accuracy of an AI model vs consensus thoracic radiologist interpretations in detecting any pneumothorax (incorporating both nontension and tension pneumothorax) and tension pneumothorax. DESIGN, SETTING, AND PARTICIPANTS: This diagnostic study was a retrospective standalone performance assessment using a data set of 1000 chest radiographs captured between June 1, 2015, and May 31, 2021. The radiographs were obtained from patients aged at least 18 years at 4 hospitals in the Mass General Brigham hospital network in the United States. Included radiographs were selected using 2 strategies from all chest radiography performed at the hospitals, including inpatient and outpatient. The first strategy identified consecutive radiographs with pneumothorax through a manual review of radiology reports, and the second strategy identified consecutive radiographs with tension pneumothorax using natural language processing. For both strategies, negative radiographs were selected by taking the next negative radiograph acquired from the same radiography machine as each positive radiograph. The final data set was an amalgamation of these processes. Each radiograph was interpreted independently by up to 3 radiologists to establish consensus ground-truth interpretations. Each radiograph was then interpreted by the AI model for the presence of pneumothorax and tension pneumothorax. This study was conducted between July and October 2021, with the primary analysis performed between October and November 2021. MAIN OUTCOMES AND MEASURES: The primary end points were the areas under the receiver operating characteristic curves (AUCs) for the detection of pneumothorax and tension pneumothorax. The secondary end points were the sensitivities and specificities for the detection of pneumothorax and tension pneumothorax. RESULTS: The final analysis included radiographs from 985 patients (mean [SD] age, 60.8 [19.0] years; 436 [44.3%] female patients), including 307 patients with nontension pneumothorax, 128 patients with tension pneumothorax, and 550 patients without pneumothorax. The AI model detected any pneumothorax with an AUC of 0.979 (95% CI, 0.970-0.987), sensitivity of 94.3% (95% CI, 92.0%-96.3%), and specificity of 92.0% (95% CI, 89.6%-94.2%) and tension pneumothorax with an AUC of 0.987 (95% CI, 0.980-0.992), sensitivity of 94.5% (95% CI, 90.6%-97.7%), and specificity of 95.3% (95% CI, 93.9%-96.6%). CONCLUSIONS AND RELEVANCE: These findings suggest that the assessed AI model accurately detected pneumothorax and tension pneumothorax in this chest radiograph data set. The model’s use in the clinical workflow could lead to earlier identification and improved care for patients with pneumothorax. American Medical Association 2022-12-15 /pmc/articles/PMC9856508/ /pubmed/36520432 http://dx.doi.org/10.1001/jamanetworkopen.2022.47172 Text en Copyright 2022 Hillis JM et al. JAMA Network Open. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article distributed under the terms of the CC-BY-NC-ND License.
spellingShingle Original Investigation
Hillis, James M.
Bizzo, Bernardo C.
Mercaldo, Sarah
Chin, John K.
Newbury-Chaet, Isabella
Digumarthy, Subba R.
Gilman, Matthew D.
Muse, Victorine V.
Bottrell, Georgie
Seah, Jarrel C.Y.
Jones, Catherine M.
Kalra, Mannudeep K.
Dreyer, Keith J.
Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs
title Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs
title_full Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs
title_fullStr Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs
title_full_unstemmed Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs
title_short Evaluation of an Artificial Intelligence Model for Detection of Pneumothorax and Tension Pneumothorax in Chest Radiographs
title_sort evaluation of an artificial intelligence model for detection of pneumothorax and tension pneumothorax in chest radiographs
topic Original Investigation
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9856508/
https://www.ncbi.nlm.nih.gov/pubmed/36520432
http://dx.doi.org/10.1001/jamanetworkopen.2022.47172
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