In order to better align existing and future ICT implementations in the health domain with the strategic options defined by the National Plan for Health Development, the Ministry of Health (MoH) of Burundi initiated in 2014 the development of a national e-health enterprise architecture based on the TOGAF methodology. A first part of the development cycle consisted of a detailed analysis of regulatory documents and strategic plans related to the Burundian health system. In a second part, semi-structured interviews were organized with a representative sample of relevant MoH health structures. The study demonstrated the donor driven unequal distribution of hardware equipment over health administration components and health facilities. Internet connectivity remains problematic and few health oriented business applications found their way to the Burundian health system. Paper based instruments remain predominant in Burundi's health administration. The study also identified a series of problems introduced by the uncoordinated development of health ICT in Burundi such as the lack of standardization, data security risks, varying data quality, inadequate ICT infrastructures, an unregulated e-health sector and insufficient human capacity. The results confirm the challenging situation of the Burundian health information system but they also expose a number of bright spots that provide hope for the future: a political will to reclaim MoH leadership in the health information management domain, the readiness to develop e-health education and training programs and the opportunity to capitalize the experiences with DHIS2 deployment, results based financing monitoring and hospital information management systems implementation.

Purpose: Using 3-dimensional (3D) printers, the creation of patient-specific models is possible before and after a therapeutic intervention. There are many articles about replicas for training and simulation of aneurysm clipping. However, no paper has focused on 3D replicas obtained from 3-tesla 3D time of flight (3D-TOF) MR angiography for intrasaccular flow diverter (WEB device) embolization of the cerebral aneurysms. In this paper, we aimed to investigate the feasibility of 3D printing models obtained from 3-tesla 3D-TOF data in the management and training of WEB-assisted embolization procedures. Case presentation:We presented a longitudinal case report with several 3D-TOF MRA prints over time. Three-tesla 3D-TOF data were converted into STL and G-code files using an open-source (3D-Slicer) program. We built patient-specific realistic 3D models of a patient with a middle cerebral artery trifurcation aneurysm, which were able to demonstrate the entire WEB device treatment procedure in the pre-intervention and post-intervention periods. The aneurysmatic segment was well displayed on the STL files and the 3D replicas. They allowed visualization of the aneurysmatic segment and changes within a 6-year follow-up period. We successfully showed the possibility of fast, cheap, and easy production of replicas for demonstration of the aneurysm, the parent vessels, and post-intervention changes in a simple way using an affordable 3D printer.Conclusions: 3D printing is useful for training the endovascular team and the patients, understanding the aneurysm/ parent vessels, and choosing the optimal embolization technique/device. 3D printing will potentially lead to greater interventionalist confidence, decreased radiation dose, and improvements in patient safety.

Phase Contrast Magnetic Resonance Image (PC-MRI) is an emerging noninvasive technique that contains pulsatile information by measuring the parameters of cerebrospinal fluid (CSF) flow. As CSF flow quantities are measured from the selected region on the images, the accuracy in the identification of the interested region is the most essential, and the examination requires a lot of time and experience to analyze and for accurate CSF flow assessment. In this study, a three-dimensional (3D)-Unet architecture, including pulsatile flow data as the third dimension, is proposed to address the issue. The dataset contains 2176 phase and rephase images from 57 slabs of 39 3-tesla PC-MRI subjects collected from the lower thoracic levels of control and Idiopathic Scoliosis (IS) patients. The procedure starts with labeling the CSF containing spaces in the spinal canal. In the preprocessing step, unequal cardiac cycle images (i.e., frame) and the numbers of MRIs in cases are adjusted by interpolation to align the temporal dimension of the dataset to an equal size. The five-fold cross-validation procedure is used to evaluate the 3D Attention-U-Net model after training and achieved an average weighted performance of 97% precision, 95% recall, 98% F1 score, and 95% area under curve. The success of the model is also measured using the CSF flow waveform quantities as well. The mean flow rates through the labeled and predicted CSF lumens have a significant correlation coefficient of 0.96, and the peak CSF flow rates have a coefficient of 0.65. To our knowledge, this is the first fully automatic 3D deep learning architecture implementation to segment spinal CSF-containing spaces that utilizes both spatial and pulsatile information in PC-MRI data. We expect that our work will attract future research on the use of PC-MRI temporal information for training deep models.

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