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Article

Chest X-ray Abnormality Detection by Using Artificial Intelligence: A Single-Site Retrospective Study of Deep Learning Model Performance

by
Daniel Kvak
1,*,
Anna Chromcová
1,
Marek Biroš
1,2,
Robert Hrubý
1,3,
Karolína Kvaková
1,
Marija Pajdaković
1,4 and
Petra Ovesná
5
1
Carebot, Ltd., 128 00 Prague, Czech Republic
2
Faculty of Mathematics and Physics, Charles University, 121 16 Prague, Czech Republic
3
Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University, 115 19 Prague, Czech Republic
4
Faculty of Electrical Engineering, Czech Technical University, 166 36 Prague, Czech Republic
5
Institute of Biostatistics and Analysis, Ltd., 602 00 Brno, Czech Republic
*
Author to whom correspondence should be addressed.
BioMedInformatics 2023, 3(1), 82-101; https://doi.org/10.3390/biomedinformatics3010006
Submission received: 23 November 2022 / Revised: 23 December 2022 / Accepted: 11 January 2023 / Published: 13 January 2023
(This article belongs to the Section Imaging Informatics)
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Abstract

Chest X-ray (CXR) is one of the most common radiological examinations for both nonemergent and emergent clinical indications, but human error or lack of prioritization of patients can hinder timely interpretation. Deep learning (DL) algorithms have proven to be useful in the assessment of various abnormalities including tuberculosis, lung parenchymal lesions, or pneumothorax. The deep learning–based automatic detection algorithm (DLAD) was developed to detect visual patterns on CXR for 12 preselected findings. To evaluate the proposed system, we designed a single-site retrospective study comparing the DL algorithm with the performance of five differently experienced radiologists. On the assessed dataset (n = 127) collected from the municipal hospital in the Czech Republic, DLAD achieved a sensitivity (Se) of 0.925 and specificity (Sp) of 0.644, compared to bootstrapped radiologists’ Se of 0.661 and Sp of 0.803, respectively, with statistically significant difference. The negative likelihood ratio (NLR) of the proposed software (0.12 (0.04–0.32)) was significantly lower than radiologists’ assessment (0.42 (0.4–0.43), p < 0.0001). No critical findings were missed by the software.
Keywords: artificial intelligence; computer-aided detection; deep learning; chest X-ray; patient prioritization artificial intelligence; computer-aided detection; deep learning; chest X-ray; patient prioritization

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MDPI and ACS Style

Kvak, D.; Chromcová, A.; Biroš, M.; Hrubý, R.; Kvaková, K.; Pajdaković, M.; Ovesná, P. Chest X-ray Abnormality Detection by Using Artificial Intelligence: A Single-Site Retrospective Study of Deep Learning Model Performance. BioMedInformatics 2023, 3, 82-101. https://doi.org/10.3390/biomedinformatics3010006

AMA Style

Kvak D, Chromcová A, Biroš M, Hrubý R, Kvaková K, Pajdaković M, Ovesná P. Chest X-ray Abnormality Detection by Using Artificial Intelligence: A Single-Site Retrospective Study of Deep Learning Model Performance. BioMedInformatics. 2023; 3(1):82-101. https://doi.org/10.3390/biomedinformatics3010006

Chicago/Turabian Style

Kvak, Daniel, Anna Chromcová, Marek Biroš, Robert Hrubý, Karolína Kvaková, Marija Pajdaković, and Petra Ovesná. 2023. "Chest X-ray Abnormality Detection by Using Artificial Intelligence: A Single-Site Retrospective Study of Deep Learning Model Performance" BioMedInformatics 3, no. 1: 82-101. https://doi.org/10.3390/biomedinformatics3010006

APA Style

Kvak, D., Chromcová, A., Biroš, M., Hrubý, R., Kvaková, K., Pajdaković, M., & Ovesná, P. (2023). Chest X-ray Abnormality Detection by Using Artificial Intelligence: A Single-Site Retrospective Study of Deep Learning Model Performance. BioMedInformatics, 3(1), 82-101. https://doi.org/10.3390/biomedinformatics3010006

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