Clinical data warehouses (CDW) are centralised repositories that store and organise a wide range of clinical data within a healthcare organisation. CDWs offer fantastic opportunities for research due to their unique characteristics: i) they constitute very large scale datasets gathering up to millions of patients; ii) they integrate very diverse data types, including electronic health records, laboratory tests and medical imaging; iii) they allow longitudinal studies since they store historical patient data over time; iv) they provide a unique source of real-world data. However, harnessing CDWs for research raises major challenges, such as that related to data quality and heterogeneity. Indeed, the heterogeneity of data quality can be a source of bias in statistical analysis that can lead to erroneous conclusions, in particular when the quality of the data is correlated to the outcome of interest.
Main publications
Quality control
- Bottani, S., Burgos, N., Maire, A., Wild, A., Ströer, S., Dormont, D., and Colliot, O.: Automatic Quality Control of Brain T1-Weighted Magnetic Resonance Images for a Clinical Data Warehouse. Medical Image Analysis, 75:102219, 2022. doi:10.1016/j.media.2021.102219 Available on HAL
- Loizillon, S., Bottani, S., Maire, A., Ströer, S., Dormont, D., Colliot, O., and Burgos, N.: Transfer Learning from Synthetic to Routine Clinical Data for Motion Artefact Detection in Brain T1-Weighted MRI. In SPIE Medical Imaging 2023: Image Processing, 12464:343–349, SPIE, 2023. doi:10.1117/12.2648201 Available on HAL
- Loizillon, S., Bottani, S., Maire, A., Ströer, S., Dormont, D., Colliot, O., and Burgos, N.: Automatic Motion Artefact Detection in Brain T1-Weighted Magnetic Resonance Images from a Clinical Data Warehouse Using Synthetic Data. Preprint, 2022. Available on HAL
- Loizillon, S., Colliot, O., Chougar, L., Stroer, S., Jacob, Y., Maire, A., Dormont, D., and Burgos, N.: Semi-Supervised Domain Adaptation for Automatic Quality Control of FLAIR MRIs in a Clinical Data Warehouse. In Domain Adaptation and Representation Transfer, LNCS, 14293: 84–93, 2024. doi:10.1007/978-3-031-45857-6_9
Dataset homogenisation
- Bottani, S., Thibeau-Sutre, E., Maire, A., Ströer, S., Dormont, D., Colliot, O., and Burgos, N.: Homogenization of Brain MRI from a Clinical Data Warehouse Using Contrast-Enhanced to Non-Contrast-Enhanced Image Translation with U-Net Derived Models. In SPIE Medical Imaging 2022: Image Processing, 12032:576–582, SPIE, 2022. doi:10.1117/12.2608565 Available on HAL
Computer-aided diagnosis of dementia
- Bottani, S., Burgos, N., Maire, A., Saracino, D., Ströer, S., Dormont, D., and Colliot, O.: Evaluation of MRI-Based Machine Learning Approaches for Computer-Aided Diagnosis of Dementia in a Clinical Data Warehouse. Medical Image Analysis, 89: 102903, 2023. doi:10.1016/j.media.2023.102903 Available on HAL