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Radiographs are used as the most important imaging tool for identifying spineanomalies in clinical practice. The evaluation of spinal bone lesions, however,is a challenging task for radiologists. This work aims at developing andevaluating a deep learning-based framework, named VinDr-SpineXR, for theclassification and localization of abnormalities from spine X-rays. First, webuild a large dataset, comprising 10,468 spine X-ray images from 5,000 studies,each of which is manually annotated by an experienced radiologist with boundingboxes around abnormal findings in 13 categories. Using this dataset, we thentrain a deep learning classifier to determine whether a spine scan is abnormaland a detector to localize 7 crucial findings amongst the total 13. TheVinDr-SpineXR is evaluated on a test set of 2,078 images from 1,000 studies,which is kept separate from the training set. It demonstrates an area under thereceiver operating characteristic curve (AUROC) of 88.61% (95% CI 87.19%,90.02%) for the image-level classification task and a mean average precision(mAP@0.5) of 33.56% for the lesion-level localization task. These results serveas a proof of concept and set a baseline for future research in this direction.To encourage advances, the dataset, codes, and trained deep learning models aremade publicly available.

VinDr-SpineXR: A deep learning framework for spinal lesions detection and classification from radiographs