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<p>Chest X-rays (CXRs) are widely used for diagnosing respiratory diseases, including the recent example of COVID-19. Supervised deep learning techniques can help detect cases faster and monitor disease progression. However, they are usually developed using coarser data annotations, which may insufficiently capture the heterogeneous disease portrait. We propose the pipeline called CIRCA (<a href="https://circa.aei.polsl.pl">https://circa.aei.polsl.pl</a>) for a CXR-based screening support system, developed using 6 diverse datasets. Our tool includes lung segmentation, quantitative assessment of data heterogeneity, and a hierarchical three-class decision system using a convolutional network and radiomic features. Lung segmentation showed an accuracy of ~ 94% in the validation and test sets, while classification accuracy was equal 86%, 83%, and 72% for normal, COVID-19, and other pneumonia classes in the independent test set. Three radiomically distinct subtypes were identified per class. In the hold-out set, the classification subtype-specific cross-dataset NPV ranged from 95 to 100%, with PPV from 86 to 100% for all subtypes except N3 (early stage or convalescent) and both C3 and P3 (probable co-occurrence of COVID-19). Using an independent test set gave similar results. The dataset-specific subtype proportions combined with various predictive qualities of subtypes partly explain the widely reported poor generalization of AI-based prediction systems.</p>

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CIRCA: comprehensible online system in support of chest X-rays-based screening by COVID-19 example

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月28日

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  • Wojciech Prazuch,
  • Aleksandra Suwalska,
  • Marek Socha,
  • Joanna Tobiasz,
  • Pawel Foszner,
  • Jerzy Jaroszewicz,
  • Katarzyna Gruszczynska,
  • Magdalena Sliwinska,
  • Mateusz Nowak,
  • Barbara Gizycka,
  • Gabriela Zapolska,
  • Tadeusz Popiela,
  • Grzegorz Przybylski,
  • Piotr Fiedor,
  • Malgorzata Pawlowska,
  • Robert Flisiak,
  • Krzysztof Simon,
  • Jerzy Walecki,
  • Andrzej Cieszanowski,
  • Edyta Szurowska,
  • Agnieszka Oronowicz-Jaskowiak,
  • Bogumil Golebiewski,
  • Mateusz Rataj,
  • Przemyslaw Chmielarz,
  • Adrianna Tur,
  • Grzegorz Drabik,
  • Justyna Kozub,
  • Anna Kozanecka,
  • Sebastian Hildebrandt,
  • Katarzyna Krutul-Walenciej,
  • Jan Baron,
  • Damian Piotrowski,
  • Jerzy Walecki,
  • Piotr Wasilewski,
  • Samuel Mazur,
  • Krzysztof Klaude,
  • Katarzyna Rataj,
  • Piotr Rabiko,
  • Pawel Rajewski,
  • Piotr Blewaska,
  • Katarzyna Sznajder,
  • Robert Plesniak,
  • Michal Marczyk,
  • Joanna Polanska

摘要

Chest X-rays (CXRs) are widely used for diagnosing respiratory diseases, including the recent example of COVID-19. Supervised deep learning techniques can help detect cases faster and monitor disease progression. However, they are usually developed using coarser data annotations, which may insufficiently capture the heterogeneous disease portrait. We propose the pipeline called CIRCA (https://circa.aei.polsl.pl) for a CXR-based screening support system, developed using 6 diverse datasets. Our tool includes lung segmentation, quantitative assessment of data heterogeneity, and a hierarchical three-class decision system using a convolutional network and radiomic features. Lung segmentation showed an accuracy of ~ 94% in the validation and test sets, while classification accuracy was equal 86%, 83%, and 72% for normal, COVID-19, and other pneumonia classes in the independent test set. Three radiomically distinct subtypes were identified per class. In the hold-out set, the classification subtype-specific cross-dataset NPV ranged from 95 to 100%, with PPV from 86 to 100% for all subtypes except N3 (early stage or convalescent) and both C3 and P3 (probable co-occurrence of COVID-19). Using an independent test set gave similar results. The dataset-specific subtype proportions combined with various predictive qualities of subtypes partly explain the widely reported poor generalization of AI-based prediction systems.