Application of artificial intelligence algorithms within the medical context for non-specialized users: the CARTIER-IA platform

• Francisco José García-Peñalvo ^[1] ; Andrea Vázquez-Ingelmo ^[1] ; Alicia García-Holgado ^[1] ; Jesús Sampedro Gómez ^[2] ; Antonio Sánchez-Puente ^[2] ; Víctor Vicente-Palacios ^[3] ; P. Ignacio Dorado-Díaz ^[2] ; Pedro L. Sánchez ^[2]
  1. [1] Universidad de Salamanca

     Universidad de Salamanca

     Salamanca, España

     
  2. [2] Hospital Universitario de Salamanca

     Hospital Universitario de Salamanca

     Salamanca, España

     
  3. [3] Philips Healthcare

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• Localización: IJIMAI, ISSN-e 1989-1660, Vol. 6, Nº. 6, 2021, págs. 46-53
• Idioma: inglés
• DOI: 10.9781/ijimai.2021.05.005
• Enlaces
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• Dialnet Métricas: 1 Cita
• Resumen

  • The use of advanced algorithms and models such as Machine Learning, Deep Learning and other related approaches of Artificial Intelligence have grown in their use given their benefits in different contexts. One of these contexts is the medical domain, as these algorithms can support disease detection, image segmentation and other multiple tasks. However, it is necessary to organize and arrange the different data resources involved in these scenarios and tackle the heterogeneity of data sources. This work presents the CARTIER-IA platform:

    a platform for the management of medical data and imaging. The goal of this project focuses on providing a friendly and usable interface to organize structured data, to visualize and edit medical images, and to apply Artificial Intelligence algorithms on the stored resources. One of the challenges of the platform design is to ease these complex tasks in a way that non-AI-specialized users could benefit from the application of AI algorithms without further training. Two use cases of AI application within the platform are provided, as well as a heuristic evaluation to assess the usability of the first version of CARTIER-IA.

• Referencias bibliográficas
  • A. Rajkomar, J. Dean, and I. Kohane, “Machine Learning in Medicine,” (in eng), N Engl J Med, vol. 380, no. 14, pp. 1347-1358, 04 2019, doi:...
  • G. Litjens et al., “A survey on deep learning in medical image analysis,” (in eng), Med Image Anal, vol. 42, pp. 60-88, Dec 2017, doi: 10.1016/j....
  • S. González Izard, R. Sánchez Torres, Ó. Alonso Plaza, J. A. Juanes Méndez, and F. J. García-Peñalvo, “Nextmed: Automatic Imaging Segmentation,...
  • S. G. Izard, J. A. Juanes, F. J. García Peñalvo, J. M. G. Estella, M. J. S. Ledesma, and P. Ruisoto, “Virtual Reality as an Educational and...
  • X. Liu et al., “A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a...
  • A. Vázquez-Ingelmo, F. J. García-Peñalvo, and R. Therón, “Information Dashboards and Tailoring Capabilities - A Systematic Literature Review,”...
  • A. Vázquez-Ingelmo, F. J. García-Peñalvo, R. Therón, D. A. Filvà, and D. F. Escudero, “Connecting domain-specific features to source code:...
  • A. Sarikaya, M. Correll, L. Bartram, M. Tory, and D. Fisher, “What Do We Talk About When We Talk About Dashboards?,” IEEE Transactions on...
  • Y. Zhonggen, “Visualizing Artificial Intelligence Used in Education Over Two Decades,” Journal of Information Technology Research (JITR),...
  • J. C. Sánchez-Prieto, J. Cruz-Benito, R. Therón Sánchez, and F. J. García Peñalvo, “Assessed by Machines: Development of a TAM-Based Tool...
  • O. Bernard et al., “Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?,”...
  • P. Mildenberger, M. Eichelberg, and E. Martin, “Introduction to the DICOM standard,” European radiology, vol. 12, no. 4, pp. 920-927, 2002.
  • D. S. Marcus, T. R. Olsen, M. Ramaratnam, and R. L. Buckner, “The Extensible Neuroimaging Archive Toolkit: an informatics platform for managing,...
  • E. Gibson et al., “NiftyNet: a deep-learning platform for medical imaging,” (in eng), Comput Methods Programs Biomed, vol. 158, pp. 113-122,...
  • A. García-Holgado and F. J. García-Peñalvo, “Preliminary validation of the metamodel for developing learning ecosystems,” in Fifth International...
  • A. García-Holgado and F. J. García-Peñalvo, “Validation of the learning ecosystem metamodel using transformation rules,” Future Generation...
  • Django Software Foundation. “Django Web Framework.” https://www. djangoproject.com/ (accessed 15/03/2015.
  • J. Nielsen, “Heuristic evaluation,” in Usability inspection methods, vol. 17, J. Nielsen and R. L. Mack Eds., no. 1): John Wiley & Sons,...
  • A. Tarrell, L. Grabenbauer, J. McClay, J. Windle, and A. L. Fruhling, “Toward improved heuristic evaluation of EHRs,” Health Systems, vol....
  • D. Armijo, C. McDonnell, and K. Werner, Electronic health record usability: Evaluation and use case framework. AHRQ Publication No. 09(10)-0091-1-...
  • J. Nielsen, “Finding usability problems through heuristic evaluation,” in CHI ‘92: Proceedings of the SIGCHI Conference on Human Factors in...
  • P. A. Harris, R. Taylor, R. Thielke, J. Payne, N. Gonzalez, and J. G. Conde, “Research electronic data capture (REDCap)—a metadata-driven...
  • R. Khajouei, A. Ameri, and Y. Jahani, “Evaluating the agreement of users with usability problems identified by heuristic evaluation,” International...