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The Subgroup Imperative: Chest Radiograph Classifier Generalization Gaps in Patient, Setting, and Pathology

Monish Ahluwalia1, Mohamed Abdalla1, James Sanayei1

  • 1From the Kingston Health Sciences Centre, Queen's University, Kingston, Ontario, Canada (M. Ahluwalia); Faculty of Medicine (M. Ahluwalia, J.S.), Institute of Health Policy, Management and Evaluation (M. Ahluwalia), Department of Computer Science (M. Abdalla, L.S.K.), and Department of Medical Imaging (B.F.), University of Toronto, Toronto, Ontario, Canada; Vector Institute for Artificial Intelligence, Toronto, Canada (M. Abdalla, B.F.); Institute for Better Health (M. Abdalla, A.A., B.F.) and Department of Diagnostic Imaging (A.A., B.F.), Trillium Health Partners, 100 Queensway West, Clinical Administrative Building, 6th Floor, Mississauga, ON, Canada L5B 1B8; Department of Medicine, Royal University Hospital, Saskatoon, Saskatchewan, Canada (J.S.); Department of Electrical Engineering and Computer Science, York University, Toronto, Ontario, Canada (L.S.K.); and Techie Maestro, Waterloo, Ontario, Canada (M.H.).

Radiology. Artificial Intelligence
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Summary
This summary is machine-generated.

Four deep learning chest radiograph classifiers were tested on a large dataset. Performance varied significantly across patient, setting, and pathology subgroups, highlighting the need for subgroup analysis in AI implementation.

Keywords:
Conventional RadiographyConvolutional Neural Network (CNN)EthicsMachine Learning AlgorithmsSupervised LearningThorax

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Healthcare
  • Radiology Informatics

Background:

  • Deep learning classifiers are increasingly used for chest radiograph interpretation.
  • External validation on diverse, real-world datasets is crucial for assessing generalizability.
  • Subgroup analysis is essential to identify performance disparities in AI algorithms.

Purpose of the Study:

  • To externally evaluate the performance of four chest radiograph classifiers.
  • To conduct a robust subgroup analysis based on patient, setting, and pathology factors.
  • To assess the real-world applicability and potential biases of AI in radiology.

Main Methods:

  • Retrospective analysis of 197,540 adult posteroanterior chest radiographs and reports (2016-2020).
  • Ground truth labels generated using a validated open-source natural language processing tool.
  • Performance metrics (accuracy, sensitivity, specificity, etc.) calculated for overall dataset and subgroups.

Main Results:

  • Classifiers achieved 68%-77% accuracy, 64%-75% sensitivity, and 82%-94% specificity.
  • Reduced sensitivity observed for solitary findings, younger patients (<40 years), and emergency department cases.
  • Decreased specificity noted for normal radiographs with support devices; sex and ancestry impacted performance.

Conclusions:

  • Chest radiograph classifier performance is influenced by patient, setting, and pathology variables.
  • Subgroup analysis is critical for informing AI implementation and ensuring equitable performance.
  • Ongoing monitoring is necessary to maintain quality, safety, and fairness in AI-assisted radiology.