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D-AI-COM: A DICOM Reception Node to Automate the Application of Artificial Intelligence Scripts to Medical Imaging Data

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Information Systems and Technologies (WorldCIST 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 801))

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Abstract

Artificial Intelligence (AI) has proven to be useful in several fields. The medical domain is one of the fields that benefits from the application of AI methods to automate and ease complex tasks including disease detection, segmentation, assessment of organ functions, etc. However, applying these kinds of methods to the variety of data formats involved in health contexts is not trivial. It is necessary to provide technologies that enable non-expert users to benefit from AI applications. This work presents a platform that acts as a DICOM reception node with the goal of automating the application of AI algorithms to medical imaging data. This platform is set to ease the process applying AI to their DICOM images by making the whole process transparent and straightforward for users without AI-related or programming skills.

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References

  1. Rajkomar, A., Dean, J., Kohane, I.: Machine learning in medicine. N. Engl. J. Med. 380(14), 1347–1358 (2019)

    Article  Google Scholar 

  2. Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., et al.: A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)

    Article  Google Scholar 

  3. Liu, X., Faes, L., Kale, A.U., Wagner, S.K., Fu, D.J., et al.: A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. The Lancet Digital Health 1(6), e271–e297 (2019)

    Article  Google Scholar 

  4. Mildenberger, P., Eichelberg, M., Martin, E.: Introduction to the DICOM standard. Eur. Radiol. 12(4), 920–927 (2002)

    Article  Google Scholar 

  5. García-Peñalvo, F.J., Vázquez-Ingelmo, A., García-Holgado, A., Sampedro-Gómez, J., Sánchez-Puente, A., et al.: Application of artificial intelligence algorithms within the medical context for non-specialized users: the CARTIER-IA platform. Int. J. Interact. Multimedia Artific. Intell. 6(6), 46–53 (2021)

    Google Scholar 

  6. Vázquez-Ingelmo, A., Alonso, J., García-Holgado, A., García-Peñalvo, F.J., Sampedro-Gómez, J., et al.: Usability Study of CARTIER-IA: A Platform for Medical Data and Imaging Management. HCII 2021: Learning and Collaboration Technologies: New Challenges and Learning Experiences, pp. 374–384, Virtual event (2021)

    Google Scholar 

  7. Vázquez-Ingelmo, A., Alonso-Sánchez, J., García-Holgado, A., Peñalvo, F.J.G., Sampedro-Gómez, J., et al.: Bringing machine learning closer to non-experts: proposal of a user-friendly machine learning tool in the healthcare domain. Ninth International Conference on Technological Ecosystems for Enhancing Multiculturality (TEEM 2021), pp. 324–329, Barcelona, Spain (2021 of Conference)

    Google Scholar 

  8. García-Peñalvo, F.J., Vázquez-Ingelmo, A., García-Holgado, A., Sampedro-Gómez, J., Sánchez-Puente, A., et al.: KoopaML: a graphical platform for building machine learning pipelines adapted to health professionals. Int. J. Interact. Multimedia Artific. Intell. (In Press)

    Google Scholar 

  9. García-Holgado, A., Vázquez-Ingelmo, A., Alonso-Sánchez, J., García-Peñalvo, F.J., Therón, R., et al.: User-Centered Design Approach for a Machine Learning Platform for Medical Purpose. HCI-COLLAB 2021, pp. 237–249, Sao Paulo, Brazil (2021)

    Google Scholar 

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Acknowledgments

This research was partially funded by the Ministry of Science and Innovation through the AVisSA project grant number (PID2020-118345RB-I00). This work was also supported by national (PI14/00695, PIE14/00066, PI17/00145, DTS19/00098, PI19/00658, PI19/00656 Institute of Health Carlos III, Spanish Ministry of Economy and Competitiveness and co-funded by ERDF/ESF, “Investing in your future”) and community (GRS 2033/A/19, GRS 2030/A/19, GRS 2031/A/19, GRS 2032/A/19, SACYL, Junta Castilla y León) competitive grants.

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Authors and Affiliations

  1. GRIAL Research Group, Computer Science Department, Universidad de Salamanca, Salamanca, Spain

    Andrea Vázquez-Ingelmo, Alicia García-Holgado & Francisco José García-Peñalvo

  2. Biomedical Research Institute of Salamanca (IBSAL), Salamanca, Spain

    Pablo Pérez-Sánchez & Pedro Ignacio Dorado-Díaz

  3. University Hospital of Salamanca. CIBERCV and Biomedical Research Institute of Salamanca (IBSAL), Salamanca, Spain

    Pedro Luis Sánchez

  4. University Hospital of Salamanca and CIBERCV, Salamanca, Spain

    Antonio Sánchez-Puente

  5. Philips Clinical Science, Amsterdam, The Netherlands

    Víctor Vicente-Palacios

Authors
  1. Andrea Vázquez-Ingelmo
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  2. Alicia García-Holgado
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  3. Francisco José García-Peñalvo
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  4. Pablo Pérez-Sánchez
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  5. Antonio Sánchez-Puente
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  6. Víctor Vicente-Palacios
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  7. Pedro Ignacio Dorado-Díaz
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  8. Pedro Luis Sánchez
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Corresponding author

Correspondence to Andrea Vázquez-Ingelmo .

Editor information

Editors and Affiliations

  1. ISEG, Universidade de Lisboa, Lisbon, Cávado, Portugal

    Alvaro Rocha

  2. College of Engineering, The Ohio State University, Columbus, OH, USA

    Hojjat Adeli

  3. Institute of Data Science and Digital Technologies, Vilnius University, Vilnius, Lithuania

    Gintautas Dzemyda

  4. DCT, Universidade Portucalense, Porto, Portugal

    Fernando Moreira

  5. TeCIP Institute, Scuola Superiore Sant’Anna, Pisa, Italy

    Valentina Colla

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© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Vázquez-Ingelmo, A. et al. (2024). D-AI-COM: A DICOM Reception Node to Automate the Application of Artificial Intelligence Scripts to Medical Imaging Data. In: Rocha, A., Adeli, H., Dzemyda, G., Moreira, F., Colla, V. (eds) Information Systems and Technologies. WorldCIST 2023. Lecture Notes in Networks and Systems, vol 801. Springer, Cham. https://doi.org/10.1007/978-3-031-45648-0_20

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