Università degli Studi di Urbino Carlo Bo / Portale Web di Ateneo

Shortening the path to Rare Disease diagnosis by using newborn genetic screening and digital technologies


Specific Challenge:

Approximately 5,000-8,000 distinct rare diseases (RD) affect 6-8% of the EU population i.e. between 27 and 36 million people; 263-446 million people are affected globall. Despite scientific advances, in Europe, the fact remains that fewer than 10% of RD patients receive treatment and only 1% are managed using an approved treatment. Delivering effective treatments to RD patients where the prevalence is low has been described as one of the major global health challenges of the 21st century. 

The overall objective of this call topic is to shorten the path to RD diagnosis by using newborn / paediatric (infants during their first weeks of life) genetic screening; and, via application of advanced digital technologies that enable rare disease diagnosis / identification. The latter might require consolidation of existing fragmented efforts.

The specific objectives are:

  • Assessment and development of a comprehensive, strategic overview of existing converging RD resources e.g. databases, registries (such as the EU RD platform), natural history projects, platforms, reference networks, rare disease academic centers of excellence (e.g. European Reference Networks (ERNs)), and initiatives for evaluation / identification of potential collaboration and synergies;
  • Federation of available RD databases into a RD metadata repository amenable to machine learning or other advanced digital tools;
  • Co-creating a sustainable strategy for newborn genetic screening and pilot it. This could start directly after achieving objective 1;
  • Based on the output of objectives 1 & 2:
    a) Repurposing of pre-existing diagnosis AI algorithm to identify early onset RD patients in electronic health records (EHRs). This will include at least 3 pilots in better-known rare diseases (with the understanding that solutions and algorithms developed or adapted should be amenable or made amenable to be emulated for larger sets of better-known RDs) where more robust data is available to train and test the AI algorithm(s), and / or;
    b) Design and development of new AI algorithm(s) to achieve the above goal.
  • Based on insights generated by objectives 1, 2 & 4, either repurposing or development of a broad AI RD diagnosis “symptom checker” to help undiagnosed RD patients going from one health care provider (HCP) to another. In addition, exploration of further viable options to implement the symptom checker in actionable solutions for HCPs and patients.

  • Data pubblicazione: 2/7/2020
    Scadenza presentazione domande: 29/9/2020

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