  Despite much promising research in the area of artificial intelligence for
medical image diagnosis, there has been no large-scale validation study done in
Thailand to confirm the accuracy and utility of such algorithms when applied to
local datasets. Here we present a wide-reaching development and testing of a
deep learning algorithm for automated thoracic disease detection, utilizing
421,859 local chest radiographs. Our study shows that convolutional neural
networks can achieve remarkable performance in detecting 13 common abnormality
conditions on chest X-ray, and the incorporation of local images into the
training set is key to the model's success. This paper presents a
state-of-the-art model for CXR abnormality detection, reaching an average AUROC
of 0.91. This model, if integrated to the workflow, can result in up to 55.6%
work reduction for medical practitioners in the CXR analysis process. Our work
emphasizes the importance of investing in local research of medical diagnosis
algorithms to ensure safe and efficient usage within the intended region.
