# _Applied Sciences_
## 4 Discussion and Conclusions
### Final Conclusions
This method is particularly challenging to apply in personalized medicine since the details of each image are required for a proper assessment, but the entire content is confidential. Furthermore, unlike the techniques discussed above, GAN-based methods do not secure information regarding the target objects or the objective of model training (in our use case, the MLaaS provider, or an interceptor who visualizes the obfuscated images, could tell that they are angiographic frames). The proposed obfuscation algorithm was created with the requirements of a medical use case in mind. Only the computational overhead associated with the obfuscation phase is introduced. Once the data have been secured, training and inference are carried out as if plain data were used. Because the result of the obfuscation is still an image, there is no need for special deep learning libraries or frameworks. Although the privacy-accuracy trade-off must be considered, applying the obfuscation algorithm on medical images successfully hides the sensitive content from human perception and protects it against AI-based reconstruction attacks, while allowing for DL model training with satisfactory performance. Conceptualization, A.B.P. and C.I.N. ; methodology, A.B.P., I.A.T., A.V., C.S. and L.M.I. ; software, A.B.P. and I.A.T. ; validation, A.V., C.I.N., A.S.-U. and L.M.I. ; formal analysis, A.S.-U., C.I.N. and A.V. ; investigation, A.B.P., I.A.T., C.I.N., A.V. and L.M.I. ; resources, L.M.I. and A.S.-U. ; data curation, A.B.P. and I.A.T. ; writing--original draft preparation, A.B.P. and I.A.T. ; writing--review and editing, C.I.N., A.V., C.S. and L.M.I. ; visualization, A.B.P. and I.A.T. ; supervision, C.S. and L.M.I. ; project administration, L.M.I. ; funding acquisition, L.M.I. All authors have read and agreed to the published version of the manuscript. This work was supported by a grant of the Romanian Ministry of Education and Research, CCCDI--UEFISCDI, project number PN-III-P2-2.1-PED-2019-2415, within PNCDI III. This work was also supported by a grant of the Ministry of Education and Research, CNCS/CCCD-- UEFISCDI, project number PN-III-P3-3-6-H2020-2020-0145/2021. This project received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No. 875351. Not applicable. The Medical MNIST dataset is available at [[https://github.com/apolanco3225/Medical-MNIST-Classification](https://github.com/apolanco3225/Medical-MNIST-Classification)]([https://github.com/apolanco3225/Medical-MNIST-Classification](https://github.com/apolanco3225/Medical-MNIST-Classification)) (accessed on 23 March 2022). The coronary angiography data used to support the findings of this study have not been made available because they were acquired in a research grant (heart.unitbv.ro) which does not allow the publication of the data. Conflicts of Interest:[PERSON], [PERSON], [PERSON], and [PERSON] are employees of Siemens SRL, Brasov, Romania. [PERSON] received scholarships from Siemens SRL, Brasov, Romania. The other authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. The following abbreviations are used in this manuscript: \begin{tabular}{l l} DL & Deep Learning \\ VAE & Variational Autoencoder \\ AI & Artificial Intelligence \\ ML & Machine Learning \\ MLaaS & Machine Learning as a Service \\ SSIM & Structural Similarity Index Measure \\ PSNR & Peak Signal-to-Noise-Ratio \\ ReLU & Rectified Linear Unit \\ LCA & Left Coronary Artery \\ RCA & Right Coronary Artery \\ CT & Computed Tomography \\ MRI & Magnetic Resonance Imaging \\ CXR & Chest X-Ray \\ CNN & Convolutional Neural Network \\ \end{tabular}   
Appendix A Multiple examples of angiographic frames and the corresponding obfuscated or reconstructed counterparts are presented in this appendix.