  Purpose: Federated training is often hindered by heterogeneous datasets due
to divergent data storage options, inconsistent naming schemes, varied
annotation procedures, and disparities in label quality. This is particularly
evident in the emerging multi-modal learning paradigms, where dataset
harmonization including a uniform data representation and filtering options are
of paramount importance.
  Methods: DICOM structured reports enable the standardized linkage of
arbitrary information beyond the imaging domain and can be used within Python
deep learning pipelines with highdicom. Building on this, we developed an open
platform for data integration and interactive filtering capabilities that
simplifies the process of assembling multi-modal datasets.
  Results: In this study, we extend our prior work by showing its applicability
to more and divergent data types, as well as streamlining datasets for
federated training within an established consortium of eight university
hospitals in Germany. We prove its concurrent filtering ability by creating
harmonized multi-modal datasets across all locations for predicting the outcome
after minimally invasive heart valve replacement. The data includes DICOM data
(i.e. computed tomography images, electrocardiography scans) as well as
annotations (i.e. calcification segmentations, pointsets and pacemaker
dependency), and metadata (i.e. prosthesis and diagnoses).
  Conclusion: Structured reports bridge the traditional gap between imaging
systems and information systems. Utilizing the inherent DICOM reference system
arbitrary data types can be queried concurrently to create meaningful cohorts
for clinical studies. The graphical interface as well as example structured
report templates will be made publicly available.
