Research and Development



  • Criteria for acquired images quality evaluation
  • Development of AI based expert system that will apply deep learning techniques to deduce a confident result
  • Formalized evaluation framework has been incorporated, capable of incorporating qualitative criteria based on biometrics and or other factors, in order to optimize the needed iterations and provide a trusted evaluation of the reconstruction quality.
  • New perspectives are created by using a global cloud service that could serve as an online quality evaluation metric for medical images scanners and other similar systems.

Knowledge Management

Knowledge Silos

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Medical Applications

Medical Imaging and BigData

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Decision Making

Metrics Aggregation with Fuzzy Inference Engine

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Ongoing effort and future developments

  •  Due to the nature of the effort required, a global collaborative effort from multiple teams would be ideal in order to build a valuable shareable knowledge, combined with the feedback of acceptance/assessment of medicine physicians that will allow for immediate quality evaluation of medical machines
  • Collaborative community: bright perspectives for a shared knowledge community and associated services in a DaaS approach