  Catheters are the second most common abnormal finding on radiographs. The
position of catheters must be assessed on all radiographs, as serious
complications can arise if catheters are malpositioned. However, due to the
large number of radiographs performed each day, there can be substantial delays
between the time a radiograph is performed and when it is interpreted by a
radiologist. Computer-aided approaches hold the potential to assist in
prioritizing radiographs with potentially malpositioned catheters for
interpretation and automatically insert text indicating the placement of
catheters in radiology reports, thereby improving radiologists' efficiency.
After 50 years of research in computer-aided diagnosis, there is still a
paucity of study in this area. With the development of deep learning
approaches, the problem of catheter assessment is far more solvable. Therefore,
we have performed a review of current algorithms and identified key challenges
in building a reliable computer-aided diagnosis system for assessment of
catheters on radiographs. This review may serve to further the development of
machine learning approaches for this important use case.
