\section{Discussion}
The aim of this paper was to develop a framework for automatic quality assessment and to evaluate how well it performs.
We were able to show that the accuracies of the predicted quality (93.0\% \textit{anterior posterior}, 95.1\% \textit{lateral}) are better than those made by radiologists (92.6\% \textit{anterior posterior}, 90.1\% \textit{lateral}).
The results of the individual steps included in the framework are summarized in \tableref{tab:experiments-and-results:tasks-results}.
With this framework it is now possible for radiographers to immediately get a first feedback on the same level of expertise as they would get from a radiologist.
These results support our view that anatomical features can be learned and are therefore suitable for the automatic assessment of diagnostic quality.
In order to achieve these results, an initial separation of the radiographs into \textit{lateral} and \textit{anterior posterior} was necessary. This task could be achieved with an accuracy of 99.5\%.

If our framework had been already in place when capturing the 950 radiographs of our dataset, 80.0\% of the non-diagnostic radiographs would have been immediately and correctly recognized as such. Since 12.9\% of the dataset are non-diagnostic radiographs, for every 100 radiographs the number of additional needed appointments for examinations could have been decreased from 13 to only 3.

Regarding scalability, our experiments show that about 500 labeled radiographs per radiographic view are sufficient to train a network to the accuracy level of an expert.
We assume that the framework can be transferred to radiographs of other body parts.
In addition to its use in day-to-day operations, the framework can potentially help to comply with quality standards and optimize the clinical routine.
