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Clinicians' Agreement on Extrapulmonary Radiographic Findings in Chest X-Rays Using a Diagnostic Labelling Scheme.

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Summary
This summary is machine-generated.

Clinician annotations for extrapulmonary findings on chest X-rays (CXRs) are reliable across experience levels. A standardized labeling scheme ensures consistent interpretation, aiding clinical decisions and AI model training.

Keywords:
annotationartificial intelligenceconsistencyextrapulmonary findings

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Area of Science:

  • Radiology
  • Medical Imaging Analysis
  • Clinical Decision Support

Background:

  • Accurate interpretation of chest X-rays (CXRs) is crucial for patient care and artificial intelligence (AI) development.
  • Existing research primarily focuses on pulmonary findings, with less attention paid to extrapulmonary abnormalities.
  • Evaluating the consistency of extrapulmonary finding annotations is vital for reliable diagnostic processes.

Purpose of the Study:

  • To assess the reliability and consistency of clinician annotations for extrapulmonary findings on CXRs.
  • To compare annotation agreement across different clinician experience levels (novice, intermediate, experienced).
  • To evaluate the impact of a standardized diagnostic labeling scheme on annotation consistency.

Main Methods:

  • Six clinicians annotated 100 CXRs for extrapulmonary findings across two rounds using a diagnostic labeling scheme.
  • Annotation consistency was measured using Randolph's kappa (RK), PABAK, PPA, and PNA.
  • Inter-reader and intra-reader agreement were analyzed using pairwise comparisons and McNemar's test.

Main Results:

  • High overall grouped labeling agreement (PABAK) was observed across all experience levels (0.86-0.91).
  • Strong agreement was found for negative findings (PNA), while positive findings showed moderate-to-low consistency (PPA).
  • No significant differences in RK were found across experience levels, and annotation stability was confirmed between rounds.

Conclusions:

  • Clinician annotations of extrapulmonary CXR findings are consistent and reliable when using a defined diagnostic labeling scheme.
  • The findings support the use of standardized schemes to improve diagnostic accuracy and AI model training.
  • This study provides insights into optimizing training strategies for both human interpreters and AI systems in radiology.