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Please use this identifier to cite or link to this item: https://libeldoc.bsuir.by/handle/123456789/45831
Title: AI-based Retrospective Study for Revealing Diagnostic Errors in Chest X-ray Screening
Authors: Liauchuk, V.
Tarasau, A.
Kovalev, V.
Keywords: материалы конференций;conference proceedings;X-ray CAD;Deep Learning;Retrospective
Issue Date: 2021
Publisher: UIIP NASB
Citation: Liauchuk, V. AI-based Retrospective Study for Revealing Diagnostic Errors in Chest X-ray Screening / Liauchuk V., Tarasau A., Kovalev V. // Pattern Recognition and Information Processing (PRIP'2021) = Распознавание образов и обработка информации (2021) : Proceedings of the 15th International Conference, 21–24 Sept. 2021, Minsk, Belarus / United Institute of Informatics Problems of the National Academy of Sciences of Belarus. – Minsk, 2021. – P. 47–50.
Abstract: In this paper, we explore the ability of an AI-based computer-aided diagnostic system (CAD) to help to reveal the early signs of probable lung diseases in X-ray images. We use a large screening database which contains natively-digital X-ray images acquired between 2001 and 2014 along with the corresponding diagnostic reports provided by the radiologists. We apply a Deep Learning-based CAD system to the cases from the database which were labeled by the radiologist as a norm and compare the CAD prediction results to the radiologists’ diagnostic reports. Our experiments demonstrate the ability of an automated AI-based CAD to reveal discrepancies between the diagnostic reports and the actual state of lungs as conveyed by the X-Ray image. Additionally, in a number of cases the Deep Learning algorithm was able to detect early signs of lung diseases which progressed later according to the patient anamnesis.
URI: https://libeldoc.bsuir.by/handle/123456789/45831
Appears in Collections:Pattern Recognition and Information Processing (PRIP'2021) = Распознавание образов и обработка информации (2021)

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