Chest X-Ray Abnormality Detection Using Artificial Intelligence: Retrospective Validation of Deep Learning Model Performance in Preclinical Practice

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.

Abstract:

Chest X-ray (CXR) is one of the most common radiological examination for both non-emergent and emergent clinical indications, but human error or lack of prioritization of patients can hinder timely interpretation. Deep learning (DL) algorithms have been proven to be useful in the assessment of various abnormalities including tuberculosis, lung parenchymal lesions or pneumothorax. 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 75 differently experienced radiologists. On the assessed dataset (n=127) collected from municipal hospital in the Czech Republic, DLAD achieved 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.

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