  Many studies on machine learning (ML) for computer-aided diagnosis have so
far been mostly restricted to high-quality research data. Clinical data
warehouses, gathering routine examinations from hospitals, offer great promises
for training and validation of ML models in a realistic setting. However, the
use of such clinical data warehouses requires quality control (QC) tools.
Visual QC by experts is time-consuming and does not scale to large datasets. In
this paper, we propose a convolutional neural network (CNN) for the automatic
QC of 3D T1-weighted brain MRI for a large heterogeneous clinical data
warehouse. To that purpose, we used the data warehouse of the hospitals of the
Greater Paris area (Assistance Publique-H\^opitaux de Paris [AP-HP]).
Specifically, the objectives were: 1) to identify images which are not proper
T1-weighted brain MRIs; 2) to identify acquisitions for which gadolinium was
injected; 3) to rate the overall image quality. We used 5000 images for
training and validation and a separate set of 500 images for testing. In order
to train/validate the CNN, the data were annotated by two trained raters
according to a visual QC protocol that we specifically designed for application
in the setting of a data warehouse. For objectives 1 and 2, our approach
achieved excellent accuracy (balanced accuracy and F1-score \textgreater 90\%),
similar to the human raters. For objective 3, the performance was good but
substantially lower than that of human raters. Nevertheless, the automatic
approach accurately identified (balanced accuracy and F1-score \textgreater
80\%) low quality images, which would typically need to be excluded. Overall,
our approach shall be useful for exploiting hospital data warehouses in medical
image computing.
