  X-ray imaging in DICOM format is the most commonly used imaging modality in
clinical practice, resulting in vast, non-normalized databases. This leads to
an obstacle in deploying AI solutions for analyzing medical images, which often
requires identifying the right body part before feeding the image into a
specified AI model. This challenge raises the need for an automated and
efficient approach to classifying body parts from X-ray scans. Unfortunately,
to the best of our knowledge, there is no open tool or framework for this task
to date. To fill this lack, we introduce a DICOM Imaging Router that deploys
deep CNNs for categorizing unknown DICOM X-ray images into five anatomical
groups: abdominal, adult chest, pediatric chest, spine, and others. To this
end, a large-scale X-ray dataset consisting of 16,093 images has been collected
and manually classified. We then trained a set of state-of-the-art deep CNNs
using a training set of 11,263 images. These networks were then evaluated on an
independent test set of 2,419 images and showed superior performance in
classifying the body parts. Specifically, our best performing model achieved a
recall of 0.982 (95% CI, 0.977-0.988), a precision of 0.985 (95% CI,
0.975-0.989) and a F1-score of 0.981 (95% CI, 0.976-0.987), whilst requiring
less computation for inference (0.0295 second per image). Our external validity
on 1,000 X-ray images shows the robustness of the proposed approach across
hospitals. These remarkable performances indicate that deep CNNs can accurately
and effectively differentiate human body parts from X-ray scans, thereby
providing potential benefits for a wide range of applications in clinical
settings. The dataset, codes, and trained deep learning models from this study
will be made publicly available on our project website at https://vindr.ai/.
