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Recent artificial intelligence (AI) algorithms have achievedradiologist-level performance on various medical classification tasks. However,only a few studies addressed the localization of abnormal findings from CXRscans, which is essential in explaining the image-level classification toradiologists. We introduce in this paper an explainable deep learning systemcalled VinDr-CXR that can classify a CXR scan into multiple thoracic diseasesand, at the same time, localize most types of critical findings on the image.VinDr-CXR was trained on 51,485 CXR scans with radiologist-provided boundingbox annotations. It demonstrated a comparable performance to experiencedradiologists in classifying 6 common thoracic diseases on a retrospectivevalidation set of 3,000 CXR scans, with a mean area under the receiveroperating characteristic curve (AUROC) of 0.967 (95% confidence interval [CI]:0.958-0.975). The VinDr-CXR was also externally validated in independentpatient cohorts and showed its robustness. For the localization task with 14types of lesions, our free-response receiver operating characteristic (FROC)analysis showed that the VinDr-CXR achieved a sensitivity of 80.2% at the rateof 1.0 false-positive lesion identified per scan. A prospective study was alsoconducted to measure the clinical impact of the VinDr-CXR in assisting sixexperienced radiologists. The results indicated that the proposed system, whenused as a diagnosis supporting tool, significantly improved the agreementbetween radiologists themselves with an increase of 1.5% in mean Fleiss' Kappa.We also observed that, after the radiologists consulted VinDr-CXR'ssuggestions, the agreement between each of them and the system was remarkablyincreased by 3.3% in mean Cohen's Kappa.

An Accurate and Explainable Deep Learning System Improves Interobserver Agreement in the Interpretation of Chest Radiograph