  Radiographs are used as the most important imaging tool for identifying spine
anomalies in clinical practice. The evaluation of spinal bone lesions, however,
is a challenging task for radiologists. This work aims at developing and
evaluating a deep learning-based framework, named VinDr-SpineXR, for the
classification and localization of abnormalities from spine X-rays. First, we
build 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 bounding
boxes around abnormal findings in 13 categories. Using this dataset, we then
train a deep learning classifier to determine whether a spine scan is abnormal
and a detector to localize 7 crucial findings amongst the total 13. The
VinDr-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 the
receiver operating characteristic curve (AUROC) of 88.61% (95% CI 87.19%,
90.02%) for the image-level classification task and a mean average precision
<EMAIL_ADDRESS>of 33.56% for the lesion-level localization task. These results serve
as 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 are
made publicly available.
