High-Performance Decision Support System to Diagnose Uncharacterized Eye Diseases

Starting date
March 9, 2018
Duration (months)
Computer Science, Department of Engineering for Innovation Medicine
Managers or local contacts
Bombieri Nicola

The project aims to design a high-performance multi-target decision support system (DSS) for the diagnosis of eye diseases based on system-level biology data at the molecular level combined with phenotypic data through bioinformatics analysis. The system, called DSSNEST, aims to characterize the phenotype of patients and their genetic ocular disease and / or rare ocular disease. This group of diseases is defined heterogeneous both clinically and genetically, probably one of the most heterogeneous of any group of neurodegenerative or genetic diseases and one of the major causes of blindness for the working age population, to date with no treatment or effective treatment.
The starting point of the project must be an existing platform, vEyesRNP, which currently implements the HW and SW system to collect patient personal data, medical data, and their interfacing with the centers of care and research. DSSNEST will extend this platform with a high-throughput data repository, a bioinformatics pipeline for data analysis, and a decision support system (DSS). The extension will allow automatic analysis of data integration, with which user profiles will be defined and used to efficiently provide a personalized diagnosis, disease-based prediction models or population for large-scale management. Given the large size of the data and the complexity of this analysis, the system will be implemented to exploit high performance parallel architectures, for which algorithms and parallel applications will be developed to ensure efficiency, scalability and accuracy of data analysis. The project aims to overcome the current limitations in transmitting genetic diagnoses in clinical practice for ocular diseases. DSSNEST will create a cooperation between doctors and patients, where the latter will be the main beneficiaries of the project result. Finally, the project aims to promote the use of Quality of Life (QoL) and Patient Reported Outcomes (PROs) indicators.


Funds: assigned and managed by an external body

Project participants

Nicola Bombieri
Full Professor
Rosalba Giugno
Full Professor

Collaboratori esterni

Roberta Amato
Neurovisual Science Technology (NEST)
Caterina Gagliano
Neurovisual Science Technology (NEST)
Massimiliano Salfi
Research areas involved in the project
Sistemi ciberfisici