Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings: A Multicenter Study

Version 1 : Received: 5 August 2022 / Approved: 10 August 2022 / Online: 10 August 2022 (04:17:32 CEST)

A peer-reviewed article of this Preprint also exists.

Kaviani, P.; Digumarthy, S.R.; Bizzo, B.C.; Reddy, B.; Tadepalli, M.; Putha, P.; Jagirdar, A.; Ebrahimian, S.; Kalra, M.K.; Dreyer, K.J. Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings: A Multicenter Study. Diagnostics 2022, 12, 2086. Kaviani, P.; Digumarthy, S.R.; Bizzo, B.C.; Reddy, B.; Tadepalli, M.; Putha, P.; Jagirdar, A.; Ebrahimian, S.; Kalra, M.K.; Dreyer, K.J. Performance of a Chest Radiography AI Algorithm for Detection of Missed or Mislabeled Findings: A Multicenter Study. Diagnostics 2022, 12, 2086.

Journal reference: Diagnostics 2022, 12, 2086
DOI: 10.3390/diagnostics12092086

Abstract

Purpose: We assessed if a CXR AI algorithm can detect missed or mislabeled chest radiographs (CXRs) findings in radiology reports. Methods: We queried multi-institutional radiology reports search database of 13-million reports to identify all CXRs reports with addendums from 1999-2021. Of the 3469 CXR reports with an addendum, a thoracic radiologist excluded reports where addendum was created for typographic errors, wrong report template, missing sections, or uninterpreted signoffs. The remaining reports with addendum (279 patients) with errors related to side-discrepancy 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 AI to detect missed or mislabeled findings. Results: AI had high sensitivity (96%), specificity (100%), and accuracy (96%) for detecting all missed and mislabeled CXR findings. The corresponding finding-specific statistics for 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%). Conclusion: 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.

Keywords

chest-X ray; sddendum; missed finding; radiology

Subject

MEDICINE & PHARMACOLOGY, Other

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