An SME that provides robust and explainable is looking for partners to work on HORIZON-JU-IHI-2024-08-02-two-stage

Arteevo Technologies Ltd. is an SME that provides robust and explainable, use case tailored AI models for disease risk prediction, patient stratification, treatment response prediction,clinical decision making and development of clinical guidelines. Our Deeply Explainable Artificial Neural Network (DxANN) technology makes our predictive models worthy of trust of medical professionals and policymakers. We are highly experienced in HORIZON 2020 and HORIZON EUROPE digital health projects.

Novel Endpoints for Osteoarthritis (OA) by applying Big Data Analytics

TOPIC ID: HORIZON-JU-IHI-2024-08-02-two-stage

Type of grant: Call for proposals

Topic description

ExpectedOutcome:

The action under this topic must contribute to all the outcomes listed below, by integrating existing data sets (clinical registries, prospective observational trials and real-world evidence data, for example from medical claims and biobanks as well as genotypic and epigenetic information), and data collections from historical and ongoing clinical trials (provided by industry partners).

• Algorithms and models, including Artificial Intelligence (AI)-based models, that are adaptable to differences in data availability have been developed and validated in different datasets to allow for the identification of osteoarthritis (OA) patient subpopulations (phenotypes/endotypes) that will benefit from specific, targeted treatment approaches. The identification of subpopulations will be based on:

1. the patient-specific burden of osteoarthritis with focus on underlying drivers (e.g. metabolic disease) and multi-morbidity/holistic patient profiles;
2. the evaluation of underlying pathways driving local vs. centralised pain in joint disease and the correlation of symptoms to joint tissue pathology;
3. the identification of key risk factors for pain in joint disease that can be linked to structural disease progression providing insights into the symptom–structure discordance in OA;
4. the detection of joint areas at risk of progression and quantification of structural progression to a more advanced stage;
5. the measures from existing innovative tools such as functional assessments with mobility and activity assessing devices (including algorithms) to reflect independence, gait measures, and assessments of muscular strength and function, as well as balance and coordination to subtly measure functional changes;
6. evaluating the differences and commonalities of osteoarthritis (OA) and inflammation-driven joint diseases such as psoriatic arthritis (PsA), rheumatoid arthritis (RA), erosive hand osteoarthritis (eHOA).

• A validation strategy is provided for a selected set of novel endpoints to measure and predict OA disease progression that enables planning of regulatory implementation pathways. This validation strategy supports innovative outcome-based and patient-centred development approaches for medicines and other therapeutic options to be discussed by regulatory authorities, health technology assessment (HTA) bodies, healthcare providers, patients, scientists and industry, shaping new approaches to the development of efficient treatments in OA and respective regulatory frameworks;
• A decision tool is developed – based on the predictive models – that supports shared decision-making for patients, their caregivers and healthcare providers according to the predicted disease progression, the most likely associated OA disease drivers and the current disease burden;
• A robust, trustworthy, and interpretable AI framework is established, that enables the development of guidelines or determines any boundaries for predictive modelling at various stages of value generation e.g. biological discovery, patient subgrouping, and clinical trials enrichment. Measures to mitigate the risk of bias and discrimination are implemented including, but not limited, to:

1. careful consideration of data sets to ensure diversity and inclusion (or account for the lack thereof);
2. the running of bias-unaware AI models and provision of fairness metrics;
3. applying AI models within frameworks mitigating bias and promoting fairness during the pre-processing, in-processing and post-processing phases.

• Data platform(s) are designed and implemented to allow a workable and efficient collaboration across the participating organisations in their respective geographies, respecting each data contributor’s access, privacy and consent approaches, which can be facilitated by federated data sharing. This outcome may serve as a blueprint for other data collaborations under the umbrella of the EU’s newly implemented AI act and data policies.

It is expected that certain existing assets like clinical data, algorithms, and data storage infrastructure will be used as background in this action. Therefore, beneficiaries intending to participate in this data-driven action need to be comfortable with the principle that ownership of specific deliverables / project results which would be considered direct improvements to a beneficiary’s background asset, will need to be transferred back to the beneficiary who contributed the background asset to the project. Provision for, and conditions relating to such transfers should be specified in the project’s consortium agreement.

Please, visit the following page to get more information regarding this call: https://www.developmentaid.org/grants/view/1324766/novel-endpoints-for-osteoarthritis-oa-by-applying-big-data-analytics?useNavigation=true