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Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings: A Multicenter Study
Purpose: We assessed whether a CXR AI algorithm was able to detect missed or mislabeled chest radiograph (CXR) findings in radiology reports. Methods: We queried a multi-institutional radiology reports search database of 13 million reports to identify all CXR reports with addendums from 1999–2021. O...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9497851/ https://www.ncbi.nlm.nih.gov/pubmed/36140488 http://dx.doi.org/10.3390/diagnostics12092086 |
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author | Kaviani, Parisa Digumarthy, Subba R. Bizzo, Bernardo C. Reddy, Bhargava Tadepalli, Manoj Putha, Preetham Jagirdar, Ammar Ebrahimian, Shadi Kalra, Mannudeep K. Dreyer, Keith J. |
author_facet | Kaviani, Parisa Digumarthy, Subba R. Bizzo, Bernardo C. Reddy, Bhargava Tadepalli, Manoj Putha, Preetham Jagirdar, Ammar Ebrahimian, Shadi Kalra, Mannudeep K. Dreyer, Keith J. |
author_sort | Kaviani, Parisa |
collection | PubMed |
description | Purpose: We assessed whether a CXR AI algorithm was able to detect missed or mislabeled chest radiograph (CXR) findings in radiology reports. Methods: We queried a multi-institutional radiology reports search database of 13 million reports to identify all CXR reports with addendums from 1999–2021. Of the 3469 CXR reports with an addendum, a thoracic radiologist excluded reports where addenda were created for typographic errors, wrong report template, missing sections, or uninterpreted signoffs. The remaining reports contained addenda (279 patients) with errors related to side-discrepancies or missed findings such as pulmonary nodules, consolidation, pleural effusions, pneumothorax, and rib fractures. All CXRs were processed with an AI algorithm. Descriptive statistics were performed to determine the sensitivity, specificity, and accuracy of the AI in detecting missed or mislabeled findings. Results: The AI had high sensitivity (96%), specificity (100%), and accuracy (96%) for detecting all missed and mislabeled CXR findings. The corresponding finding-specific statistics for the AI were nodules (96%, 100%, 96%), pneumothorax (84%, 100%, 85%), pleural effusion (100%, 17%, 67%), consolidation (98%, 100%, 98%), and rib fractures (87%, 100%, 94%). Conclusions: The CXR AI could accurately detect mislabeled and missed findings. Clinical Relevance: The CXR AI can reduce the frequency of errors in detection and side-labeling of radiographic findings. |
format | Online Article Text |
id | pubmed-9497851 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94978512022-09-23 Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings: A Multicenter Study Kaviani, Parisa Digumarthy, Subba R. Bizzo, Bernardo C. Reddy, Bhargava Tadepalli, Manoj Putha, Preetham Jagirdar, Ammar Ebrahimian, Shadi Kalra, Mannudeep K. Dreyer, Keith J. Diagnostics (Basel) Article Purpose: We assessed whether a CXR AI algorithm was able to detect missed or mislabeled chest radiograph (CXR) findings in radiology reports. Methods: We queried a multi-institutional radiology reports search database of 13 million reports to identify all CXR reports with addendums from 1999–2021. Of the 3469 CXR reports with an addendum, a thoracic radiologist excluded reports where addenda were created for typographic errors, wrong report template, missing sections, or uninterpreted signoffs. The remaining reports contained addenda (279 patients) with errors related to side-discrepancies or missed findings such as pulmonary nodules, consolidation, pleural effusions, pneumothorax, and rib fractures. All CXRs were processed with an AI algorithm. Descriptive statistics were performed to determine the sensitivity, specificity, and accuracy of the AI in detecting missed or mislabeled findings. Results: The AI had high sensitivity (96%), specificity (100%), and accuracy (96%) for detecting all missed and mislabeled CXR findings. The corresponding finding-specific statistics for the AI were nodules (96%, 100%, 96%), pneumothorax (84%, 100%, 85%), pleural effusion (100%, 17%, 67%), consolidation (98%, 100%, 98%), and rib fractures (87%, 100%, 94%). Conclusions: The CXR AI could accurately detect mislabeled and missed findings. Clinical Relevance: The CXR AI can reduce the frequency of errors in detection and side-labeling of radiographic findings. MDPI 2022-08-28 /pmc/articles/PMC9497851/ /pubmed/36140488 http://dx.doi.org/10.3390/diagnostics12092086 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 Kaviani, Parisa Digumarthy, Subba R. Bizzo, Bernardo C. Reddy, Bhargava Tadepalli, Manoj Putha, Preetham Jagirdar, Ammar Ebrahimian, Shadi Kalra, Mannudeep K. Dreyer, Keith J. Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings: A Multicenter Study |
title | Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings: A Multicenter Study |
title_full | Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings: A Multicenter Study |
title_fullStr | Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings: A Multicenter Study |
title_full_unstemmed | Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings: A Multicenter Study |
title_short | Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings: A Multicenter Study |
title_sort | performance of a chest radiography ai algorithm for detection of missed or mislabeled findings: a multicenter study |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9497851/ https://www.ncbi.nlm.nih.gov/pubmed/36140488 http://dx.doi.org/10.3390/diagnostics12092086 |
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