BRAINTEASER project launch.

Harmonisation of models’ technical specifications and requirements for their integration into the web-based, cloud-based platform InSilicoTrials.com, compliant with regulatory standards.

Conceptualisation of the architecture and development of the services infrastructure for data acquisition. Back-end services support for the interactive tools/apps for patients and clinical health professionals.

First release of app questionnaires and sensors system (wearable and environmental) for continuous monitoring of patients. Release of the clinical tool for data collection in the clinical settings.

First Version of the BRAINTEASER Ontology (BTO 1.0).

Data FAIRification and integration with European Open Science Cloud services.

iDPP@CLEF 2022.

First release of AI models for alert detection, patients’ stratification, disease progression.

Initial version of the dashboard-based Tool for Data Management, Monitoring and Exploration.

Preliminary Exploitation plan.

Second Version of the BRAINTEASER Ontology (BTO 2.0).

Community of Practice launch.

Release of the AI-based prognostic models and implementation on the clinical tool.

Data FAIRification and integration with European Open Science Cloud services.

iDPP@CLEF 2023.

Third Version of the BRAINTEASER Ontology (BTO 3.0).

Second release of AI models for alert detection, patients’ stratification, disease progression.

Release of updated version of our AI software, which includes predictive models and tools to explain their results.

Deployment of alpha-version of cloud-based applications, aimed to industry, integrating AI models for patients’ stratification and disease progression.

Data FAIRification and integration with European Open Science Cloud services.

Clinical study recruitment finalised.

Deployment of beta-version of cloud-based applications, aimed to industry, integrating AI models for patients’ stratification and disease progression. 

iDPP@CLEF 2024.

Exloitation workshop.

Fully operational web applications and mobile apps for patients, along with a clinician dashboard offering personalized options for ALS and MS care.

Release of a dataset combining prospective data, environmental exposure information, and sensor data from patients.

Improved understanding of complex cases through advanced statistical techniques, identifying challenging predictions and enhancing model reliability and accuracy.

Improved disease tracking by integrating progression modeling with classification techniques, enhancing prediction accuracy for complex conditions like multiple sclerosis.

Deployment of pre-release version of cloud-based applications, aimed to industry, integrating AI models for patients’ stratification and disease progression. This new version includes document repository and validation datasets.