CIRCA: comprehensible online system in support of chest X-rays-based screening by COVID-19 example
Year: 2025
Type: article
Abstract: 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.
Source: Scientific Reports
Authors: 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, POLCOVID Study Group, 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
Institutions: Silesian University of Technology, Medical University of Silesia, Wojewódzki Szpital Specjalistyczny we Wrocławiu, Jagiellonian University, Medical University of Warsaw, Nicolaus Copernicus University, Collegium Medicum in Bydgoszcz, Medical University of Białystok, Wroclaw Medical University, Central Clinical Hospital, The Maria Sklodowska-Curie National Research Institute of Oncology, Gdańsk Medical University, Medical Prognosis Institute (Denmark), Uniwersytecki Szpital Kliniczny w Opolu, University of Rzeszów, Yale Cancer Center, Yale University
Language: English
Topic: COVID-19 diagnosis using AI
Subfield: Radiology, Nuclear Medicine and Imaging
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W4416783292