  Purpose: Identifying intravenous (IV) contrast use within CT scans is a key
component of data curation for model development and testing. Currently, IV
contrast is poorly documented in imaging metadata and necessitates manual
correction and annotation by clinician experts, presenting a major barrier to
imaging analyses and algorithm deployment. We sought to develop and validate a
convolutional neural network (CNN)-based deep learning (DL) platform to
identify IV contrast within CT scans. Methods: For model development and
evaluation, we used independent datasets of CT scans of head, neck (HN) and
lung cancer patients, totaling 133,480 axial 2D scan slices from 1,979 CT scans
manually annotated for contrast presence by clinical experts. Five different DL
models were adopted and trained in HN training datasets for slice-level
contrast detection. Model performances were evaluated on a hold-out set and on
an independent validation set from another institution. DL models was then
fine-tuned on chest CT data and externally validated on a separate chest CT
dataset. Results: Initial DICOM metadata tags for IV contrast were missing or
erroneous in 1,496 scans (75.6%). The EfficientNetB4-based model showed the
best overall detection performance. For HN scans, AUC was 0.996 in the internal
validation set (n = 216) and 1.0 in the external validation set (n = 595). The
fine-tuned model on chest CTs yielded an AUC: 1.0 for the internal validation
set (n = 53), and AUC: 0.980 for the external validation set (n = 402).
Conclusion: The DL model could accurately detect IV contrast in both HN and
chest CT scans with near-perfect performance.
